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+Supervised Feature Selection Techniques in
+Network Intrusion Detection: a Critical Review
+M. Di Mauroa,<, G. Galatrob, G. Fortinocand A. Liottad
+aDepartment of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084, Fisciano, Italy
+bAmazon AWS, Belgard Retail Park, Tallaght, Dublin, Ireland
+cDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, Italy
+dFaculty of Computer Science, Free University of Bozen-Bolzano, Italy
+ARTICLE INFO
+Keywords :
+Feature Selection
+Machine Learning
+Network Intrusion Detection
+Network PerformanceABSTRACT
+Machine Learning (ML) techniques are becoming an invaluable support for network intrusion de-
+tection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML al-
+gorithms are exploited to classify/recognize data traffic on the basis of statistical features such as
+inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast
+diversity and number of features that typically characterize data traffic is a hard problem. This re-
+sults in the following issues: i)the presence of so many features leads to lengthy training processes
+(particularly when features are highly correlated), while prediction accuracy does not proportionally
+improve; ii)some of the features may introduce bias during the classification process, particularly
+those that have scarce relation with the data traffic to be classified. To this end, by reducing the fea-
+ture space and retaining only the most significant features, Feature Selection (FS) becomes a crucial
+pre-processing step in network management and, specifically, for the purposes of network intrusion
+detection. In this review paper, we complement other surveys in multiple ways: i)evaluating more
+recentdatasets(updatedw.r.t. obsoleteKDD 99)bymeansofadesigned-from-scratchPython-based
+procedure; ii)providingasynopsisofmostcreditedFSapproachesinthefieldofintrusiondetection,
+includingMulti-ObjectiveEvolutionarytechniques; iii)assessingvariousexperimentalanalysessuch
+as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines
+to network/security managers who are considering the incorporation of ML concepts into network
+intrusion detection, where trade-offs between performance and resource consumption are crucial.
+1. Introduction
+With the rapid growth of digital technology and com-
+munications, we are overwhelmed by network data traffic,
+whicharediverseformediatype(e.g. video,voice,text,sen-
+sory,etc.),andoriginatefrom(andaretransportedthrough)
+abroadrangeofsources(e.g. mobilenetworks,cloudinfras-
+tructures,InternetofThings,etc.). Consequently,wehandle
+high-dimensionality data, calling for increasingly more so-
+phisticated classification methods [1, 2].
+Typically,werefertohighdimensionalitywhenwedeal
+with data whereby a large number of features may be ex-
+tracted, to the point that the features may even exceed the
+numberofobservations. Thisleadstomajorissues,particu-
+larly the massive increase in training times.
+To this end, Feature Selection (FS) is a promising re-
+searchdirection,lookingatwaystoreducethefeaturespace
+in order to pinpoint only the most significant features. As
+a fundamental pre-processing step in machine learning, FS
+is gaining prominence in network management and, specif-
+ically, for the purposes of network intrusion detection and
+network traffic classification problems [3, 4, 5, 6].
+Moregenerally,FSfindsanevenmuchbroaderapplica-
+bilityinfieldasdiverseasbioinformatics[7,8,9,10],image
+recognition/retrieval [11, 12, 13, 14, 15, 16, 17], fault diag-
+<Corresponding author
+mdimauro@unisa.it (M. Di Mauro); galatrogiovanni@gmail.com (G.
+Galatro); g.fortino@unical.it (G. Fortino); antonio.liotta@unibz.it (A.
+Liotta)nosis[18,19],textmining[20,21,22]and,interestingly, in
+network traffic analysis/classification, whose pertinent bib-
+liography will be covered more closely in the following.
+Machinelearningenginescanbeeasilyembeddedinnet-
+work intrusion detection systems (IDS), which represent an
+essential part of network infrastructures to guarantee secu-
+rity [23, 24, 25] and availability [26, 27, 28, 29, 30, 31, 32,
+33, 34]. Specifically, modern NIDS can be equipped with
+softwareprobesinchargeofanalyzingnetworktrafficonthe
+basis of some characterizing features such as: distribution
+of inter-arrival times, distribution of packet sizes, presence
+of specific TCP/IP flags, percentage of forward/backward
+flows. Suchstatisticalinformationisinstrumentaltoreveal-
+inganomaloustraffic,whichisoftenbehindDistributedDe-
+nial of Service (DDoS) attacks [35, 36], covert Voice-over-
+IPsessions[37],threatdiffusion[38]andPeer-2-Peertraffic
+[39, 40, 41]. In many cases, these flows would pass unob-
+servedthroughotherconventionalsignature-basedanalyses.
+In principle, a larger number of features would allow to
+perform more granular analyses; yet, two main drawbacks
+emerge. First, a proliferation of correlated features leads to
+levels of redundancy that are useless, while resulting in in-
+creasingly longer training times. Also, not all features are
+valuable in characterizing traffic, incurring bias during the
+classification step.
+Ifweconsider,asanexample,somenovelprobessuchas
+ISCXFlowMeter[42],thesecanactuallygeneratemorethan
+80features to characterize data traffic, which makes it ex-
+Di Mauro et al.: Preprint submitted to Elsevier Page 1 of 19arXiv:2104.04958v1  [cs.CR]  11 Apr 2021Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+tremelydifficultforthenetworkanalysttodealwiththisvast
+amount of information. This is where an FS pre-processing
+step becomes invaluable.
+Across the scientific literature, many works are devoted
+to surveying machine learning algorithms with applications
+tointernettrafficclassification. Yet,fewattemptshavebeen
+made in the field of FS techniques, where two main short-
+comings emerge: i)many works analyze and compare FS
+algorithms applied to non-specific datasets [43, 44, 45]; ii)
+areeitherbasedonoutdateddatasets,orlacktheexperimen-
+taldimension. Overall,notmuchnovelalgorithmshavebeen
+compared(refertodetailsprovidedinTable 1,discussedfur-
+ther ahead). Aimed at filling these gaps, our paper provides
+the main following contributions:
+•We perform an experimental analysis of more re-
+centdatasets(classicliteraturefocusesonthe 20-year
+old KDD 99dataset) by means of our designed-from-
+scratchPythonbasedroutine. Thisallowsusto i)per-
+form cleaning, re-balancing, and data mixing opera-
+tions,and ii)automaticallyinteractwithspecificML-
+based engines.
+•We present a variety of experimental results (in-
+cluding: feature correlation, time complexity, perfor-
+mance) aimed at critically comparing selected fam-
+ilies of FS algorithms, ranging from classic ones
+(rank search, linear forwarding selection) to modern
+bio-inspired algorithms (genetic search, ant colonies,
+multi-objectiveevolutionary),thusgoingwellbeyond
+a conventional literature survey.
+Our experimental-based, comparative assessment finds
+fruitfulapplicationsinthefieldofnetwork/securitymanage-
+ment, where machine learning techniques are proved to of-
+feraprecioussupporttointrusiondetectiontasks,andwhere
+criticaltrade-offsbetweenperformanceclassificationandre-
+source consumption arise.
+Thepaperisorganizedasfollows: Section 2offersagen-
+eralperspectiveonFSmethods,inlinewithotheraccredited
+taxonomies. In Section 3we analyze how diverse FS tech-
+niqueshavebeenappliedintheliterature,oftenbasedonthe
+oldKDD 99dataset. Section 4proposesan excursusofpop-
+ular FS algorithms (classic and modern) along with some
+necessary technical details. In Section 5we analyze the ex-
+ploitednoveldatasetsbygroupingthemostrelevantfeatures
+in families. In Section 6we perform an experimental-based
+comparative analysis of prominent FS algorithms, provid-
+ing performance, feature correlation, and time-complexity
+figures. Finally, Section 7draws conclusions and provides
+future-direction indications.
+2. Overview of Feature Selection
+Feature Selection refers to that set of techniques and
+strategies that allow to optimize the feature space, namely,
+ann-dimensionalspacewhereeachsampleisrepresentedas
+a point. When dealing with large feature spaces, the anal-
+ysis of data, which starts from their representative features,can be tremendously time and resource consuming. Hence,
+the need to devise suitable FS strategies aimed at eliminat-
+ingi/irrelevant features, namely, those features that are not
+actually needed to build an optimal feature subset; and ii/
+redundant features, namely, those features that strongly de-
+pend on other features [46].
+Feature selection can be considered as a special case of
+feature extraction methods [47]. The latter refer to a set of
+techniques (e.g. PCA, Single Value Decomposition, Linear
+Discriminant Analysis and others) useful to transform the
+original feature space in a new one aimed at alleviating the
+effectsofthenotorious curseofdimensionality problem[48,
+49]. Itisimportanttonotethatfeatureextractioncomeswith
+thecriticalriskofobtainingatransformedfeaturespacethat
+couldloseitsoriginalphysicalmeaning,whereasclassicFS
+aims at preserving it [50, 51].
+A more formal definition of the FS problem follows.
+Given a feature set X= ^xi:i= 1;§;N`, find a subset
+SK= ^xi1;xi2;§;xiK`withK < N, where an objective
+function Y() is optimized, namely:
+^xi1;xi2;§;xiK` = arg max
+K;ik[Y^xi:i= 1;§;N`]:(1)
+Unlessotherwisestated,theproblemistoselecttheoptimal
+subsetoffeatures(accordingtoaspecificcriterion)fromthe
+initialset,wheretwostepsaretypicallyperformed: thefirst
+oneinvolvesasearchstrategytopinpointcandidatesubsets;
+thesecondoneinvolvesanobjectivefunctiontoevaluatethe
+selected candidate subsets. The latter can be split in two
+types [52, 53, 54, 55]: i/filters, referring to objective func-
+tions that rely on properties of the data by evaluating the
+information content (e.g. correlation measures, inter-class
+distance);ii/wrappers, referring to objective functions that
+exploittrainingmodelsbystartingfromasubsetoffeatures
+and,then,addingorremovingfeaturesbasedontheprevious
+model.
+A commonly accepted taxonomy of FS methods is pre-
+sented in [56], where three classic approaches have been
+identifiedas supervised ,unsupervised ,andsemi-supervised .
+According to the supervised approach, labeled data are ex-
+ploitedtosingleoutafeaturesubset,consideringspecificcri-
+teriaformeasuringthefeaturesimportance. Conversely,un-
+supervisedtechniquesseektounveiltheintrinsicdatastruc-
+turetoselectthemostsignificantfeatures,withoutassuming
+anya prioriknowledge [57].
+Finally, the semi-supervised approach is based on a
+mixedstrategy,strivingtoenrichanunlabeledsetwithsome
+labeled data, so as to improve the FS phase. Both the un-
+supervised and the semi-supervised approaches exhibit the
+drawback of neglecting potential correlations among fea-
+tures, resulting in the analysis of sub-optimal sets. This
+may prove critical when dealing with traffic analysis, where
+we need to take into account statistical-based features (e.g.
+inter-arrivaltimesvariance,averagepacketlength,etc.) and
+deterministic ones (e.g. IP addresses, port numbers, etc.).
+Let us consider, for example, a particular kind of traffic di-
+rected towards a fixed destination port for a certain period
+Di Mauro et al.: Preprint submitted to Elsevier Page 2 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+Table 1
+Prominent related work surveying FS techniques applied to Network Intrusion Detection.
+Authors Experiments Single/Multi Class Description
+Wang et al. [58] Performance analysis Single Class Feature Selection performed on the KDD99
+dataset, by applying C4.5 and Bayesian Net-
+work algorithms
+Janarthanan et al.
+[59]Performance analysis Single/Multi Class Empirical selection of features by performing
+tests on KDD99 and UNSW-NB15 datasets
+by means of Random Forest algorithm
+El-Khatib [60] Performance analysis Single/Multi Class Feature selection combining Filter/Wrapper
+methods for Wireless IDS. Tests have been
+performed on a WLAN environment
+Chen et al. [61] Performance analysis, Time
+analysisSingle Class Correlation-based Feature selection using the
+KDD99 dataset
+Nisioti et al. [62] N/A N/A Classic survey on ML-based techniques (in-
+cludingFS)withpointerstodetailedsources,
+but with no experiments
+Iglesias et al. [63] Performance analysis Single/Multi Class Comparison among 4FS algorithms on the
+NSL-KDD dataset
+Singh et al. [64] Performance analysis, Time
+analysisSingle Class Comparison among various FS algorithms on
+the KDD99 dataset
+Bahrololum et al.
+[65]Performance analysis Single/Multi Class Comparison among PSO, Decision Tree,
+FlexibleneuraltreealgorithmsontheKDD99
+dataset
+Dhote et al. [66] N/A N/A Limited survey of FS techniques applied to
+network traffic, with pointers to detailed
+sources, but with no experiments
+This work Performanceanalysis, Feature
+Correlation analysis, Time
+analysisSingle/Multi Class Feature Selection performed on the CIC-IDS-
+2017/2018dataset, byconsidering 9FSalgo-
+rithms from classic (Rank, Scatter) to mod-
+ern (Genetic, Multi-Objective Evolutionary)
+oftime. Anunsupervisedapproachcouldleadtoasubsetof
+features which does not include the destination port. Thus,
+acrucial(aswellasdeterministic)pieceofinformationgets
+lost.
+Ontheotherhand,asupervisedapproachcanofferopti-
+malresults,providedthatthedataarecorrectlylabeled. This
+case typically occurs in a controlled network environment,
+where, with the help of network analyzers, it is possible to
+automatically label the type of passing data traffic.
+Because of the great potential of supervised methods,
+andthankstotheavailabilityofsuitablelabeleddatasets,we
+have decided to focus our experimental-based comparative
+evaluation on supervised FS methods.
+3. Related Work on Feature Selection applied
+to ML-based Intrusion Detection
+Most scientific literature involving ML approaches is
+typically focused on the proposal of algorithms or tech-
+niquesforclassification/detectionofspecificnetworktraffic
+([67,68,69,70,71,72,73,74]). Unfortunately,thestraight
+applicationofmachinelearningtonetworktrafficanalysisis
+not always feasible, due to the broad variety of traffic types,
+which leads to unmanageable feature spaces. Intuitively, in
+fact, a highly diversified traffic (multimedia, asynchronous,bursty, etc.) requires a large set of features able to capture
+the variegated “nature" of the different flows. That is why
+appropriatepre-processingsteps(i.e. FS)playacrucialrole,
+thus a significant portion of ML-based literature has shown
+interest in FS techniques.
+Forinstance,toimprovetheperformanceofIDSframe-
+works, the authors of [75] propose a mixed strategy in-
+volvingPrincipalComponentAnalysisandfuzzyclustering
+withKNN-basedFStechniques. Acorrelation-basedFSap-
+proachcoupledwithaSupportVectorMachine(SVM)clas-
+sifierisproposedin[76]tobuildacloud-basedIDS.AnIDS
+based on deep learning methods, along with a filter-based
+FS algorithm, is introduced in [77]. Similarly, a Convolu-
+tional Neural Network (CNN) approach is exploited in [78]
+to select traffic features from raw data sets, improving the
+accuracy of an intrusion detector. Yu and Liu propose a
+mutualinformation-basedalgorithmthatcananalyticallyse-
+lect optimal features for classification, by handling linearly
+andnon-linearlydependentdata[79]. Again,FSisexploited
+jointlywithArtificialNeuralNetworks[80]andDeepNeural
+Networks [81], respectively, to improve IDS performance.
+Furthermore,authors in[82]embedin anIDStwoFSalgo-
+rithms that are compared against mutual information-based
+methods.
+A major shortcoming of these works is that methods
+Di Mauro et al.: Preprint submitted to Elsevier Page 3 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+are validated and compared through the outdated KDD 99
+dataset. This contains information about older network at-
+tacks (that have been mitigated by now) or, in some cases,
+adopts an updated version of KDD 99, namely NSL-KDD
+[83]. Yet, although NSL-KDD adds some improvements
+onto KDD 99(e.g. no redundant records, better balancing
+between training and test set, etc.), it does not take into ac-
+count features that characterize novel cyber attacks. Other
+works in the field of machine learning applied to intrusion
+detectionrelyonmorerecentdatasetssuchasUNSW-NB15
+[84, 85, 86, 87]. Although quite recent, such a dataset has
+two limitations: first, the traffic has been collected in a re-
+duced testbed; and secondly, the number of features is lim-
+ited to 49, which is too small to appreciate the effectiveness
+of feature selection techniques. In contrast, in our work we
+relyonanup-to-datedatasetfromtheCanadianInstitutefor
+Cybersecurity [88] which has the following benefits: i/it
+containsdatatrafficgatheredoveravastnetworkarea;and ii/
+itaccountsforabout 80features,allowingtoextensivelytest
+the feature selection algorithms. Additional details about
+this dataset are provided in Section 5.
+Going more specifically into the set of works that share
+withthispapertheaimtocompareorsurveyFSmethodsfor
+intrusiondetection,wehavecollectedthesignificantpapers
+in Table 1. In it, we have identified the material covered in
+the literature, which helps appreciating the contributions of
+our paper. The first column of Table 1points to the source;
+thesecondcolumnhighlightsthetypeofexperimentalanal-
+ysis (if any); the third column pinpoints the type of datasets
+utilized (single or multi-class); the last column provides a
+concise description of the surveyed material.
+4. Review of Feature Selection Algorithms
+under Scrutiny
+In this section, we briefly describe the algorithms under
+scrutiny,whichbelongtodifferentfamiliesofFStechniques,
+rangingfromclassicrank-guided(Rank,LinearForwardSe-
+lection) and meta-heuristic (Tabu, Scatter, Particle Swarm)
+ones, to nature-inspired algorithms (Ant, Cuckoo), and up
+to modern techniques (Genetic, Multi-Objective Evolution-
+ary).
+For each algorithm, we provide a brief recap along with
+itspertinentapplicationinnetworktrafficanalysisandsecu-
+rity in literature.
+4.1. Rank-based Feature Selection
+Algorithms belonging to this family follow an approach
+based on two macro-steps: in the first one, the features are
+rankedaccordingtoacertainstatisticalmeasure,whereasin
+thesecondstepthealgorithmchoosesthetoprankedfeatures
+(eventually partitioned in clusters). We investigate and put
+to test two representative algorithms of this family: Rank
+Search and Linear Forward Selection.
+4.1.1. Rank Search
+TheRankSearchtechniquerefersnotonlytoasingleal-
+gorithm,but,toanumbrellaofmethodsabletoproducealistof attributes ranked with some criteria. One of the most ap-
+plied rank-based techniques in FS relies on the Information
+Gain (IG) concept [89]. Given an attribute Aand a class C,
+theentropyof the class without and with prior observation
+of the attribute are, respectively:
+H.C/ = *É
+cËCp.c/log2p.c/; (2)
+and
+H.CðA/ = *É
+aËAp.a/É
+cËCp.cða/log2p.cða/:(3)
+TheIGisgivenby H.C/ *H.CðAi/,andrepresentsthe
+amount of the class entropy decreasing due to the a priori
+knowledge introduced by i-th attribute.
+Over network traffic analysis literature, the rank search
+methodhasbeenwidelyexploitedinconjunctionwithstan-
+dard machine learning algorithms typically used during the
+classificationstep. In[90],havingtheNSL-KDDdatasetas
+input, the proposed algorithm i/evaluates the information
+gain value,ii/applies fuzzy rules to remove unwanted fea-
+tures,iii/calculatesthemeanvalueofIGacrossthenewsub-
+setoffeatures, iv/refinesthefeaturesubsetsbyapplyingan
+algorithm based on conditional probability evaluation. Au-
+thors in [91] propose a detection model able to cope with
+network attacks based on the feature IG ranking; once ob-
+tained a satisfying feature set, a triangle area based KNN,
+combiningbothSVMandgreedytechniques,isexploitedto
+single out even more discriminative and useful features. A
+combination between an IG-based feature selection method
+andaC4.5-basedclassifierisadvancedin[92],wherethere-
+sultingalgorithmhasbeenoptimized(intermsofpowercon-
+sumption) to reveal DoS attacks in ad hoc networks. Simi-
+larensemblehasbeenexploitedin[93],whereauthorscon-
+siderabroadersetofclassifiers(C4.5,RandomForest,Naive
+Bayes, etc.) and where the analysis focuses on generic mal-
+ware detection. Again, a mixed Genetic/KNN approach for
+intrusion detection presented in [94] benefits from a feature
+reductionprocedureobtainedapplyingID3algorithmtofind
+higher IG.
+4.1.2. Linear Forward Selection
+Suchatechniquetoreducethefeaturespacedimension-
+ality has been presented in [95]. Linear Forward Selection
+(LFS) can be considered as an improvement of Sequential
+ForwardSelection(SFS)methodwhichstartswithanempty
+set of features and sequentially adds one feature at a time.
+In order to face the O.N2/complexity of SFS, in LFS the
+number of considered features at each step does not exceed
+a certain user-specified constant, thus, the resulting perfor-
+mance is impressively ameliorated. Two methods for limit-
+ing the number of features have been implemented in LFS
+algorithm:i/FixedSet,whereascoreobtainedbymeansof
+a wrapper evaluator is used to choose the top- kranked at-
+tributes, thus, the maximum number of evaluations reduces
+tok_2.k+ 1/;ii/Fixed Width , where, at each forward
+Di Mauro et al.: Preprint submitted to Elsevier Page 4 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+selection step, the number of features is increased by one,
+suchthatthesetofcandidateexpansionsconsistsofthebest
+kfeatures not been selected so far during the search. In
+such case, the maximum number of evaluations amounts to
+Nk*k_2.k+1/. ForwardSelection-basedtechniqueshave
+beenexploitedacrossthetrafficclassificationdomainforde-
+tection of malicious application on Android [96], detection
+ofzero dayattacks [97], designing novel anomaly detection
+systems [98].
+4.2. Meta-heuristic Feature Selection
+Techniquesbelongingtothisfamilyrelyontheprinciple
+that it is possible to select heuristics , namely, approximate
+algorithms searching for a sufficiently good solution to an
+optimization problem, useful when some constraints arise
+(e.g. limited computational resources, incomplete informa-
+tion). Weselectthreerepresentativealgorithmsofthisfam-
+ily: Tabu Search, Scatter Search, and Particle Swarm Opti-
+mization.
+4.2.1. Tabu Search
+Tabu Search (TS) algorithm has been originally pro-
+posed in [99], and, then, it has been extended and exploited
+to solve practical optimization problems [100, 101, 102].
+TS is based on a metaheuristic method able to pilot a lo-
+cal heuristic search in exploring the space of solutions be-
+yond the local optimality. Two main features characterize
+TS:adaptive memory andresponsive exploration . The for-
+mer allows to perform local choices guided by information
+collected during the search, whereas, the latter allows to
+make strategic choices. In other words, TS exploits a local
+search procedure combined with memory-based strategies,
+thus,theissueofgettingtrappedinlocaloptimalsolutionsis
+avoided. To implement the memory-based mechanism, TS
+builds a map of recently visited solutions called Tabu List
+(TL). A simplified TS algorithm is illustrated below:
+Algorithm 1: Tabu Search
+1. Given a function f.x/to be optimized over a set
+X, start from initial solution x0ËX, initialize
+Tabu List (TL), and initialize a counter i= 0.
+2. GivenN.xi/ÏXtheneighborhood ofxi,N.xi/
+can be reached by xiby means of a move
+operation. Thus, generate a neighborhood move
+listM.Xi).
+3. Givenxi+1the best solution in M.Xi), update
+TL.
+4. In case stopping conditions are met, terminate.
+Otherwise, repeat Step 2.
+It is interesting to notice that, the memory structures char-
+acterizing TS method operate according four dimensions
+[100]:
+•Recency: concerns the ability of keeping track of so-
+lutions attributes that have changed across the recent
+past.•Frequency : involves the mechanism exploited to
+broad the foundation for selecting preferred moves.
+•Quality: pertainstothecapacityofdiscriminatingthe
+meritofsolutionsvisitedduringthesearchoperation.
+•Influence: takes into account the impact of choices
+performed during the search.
+Inthefieldofnetworktrafficclassification,TShasbeen
+exploitedin[103]jointlywithfuzzytechniquesaimedatop-
+timizing the exploration of feature search space in intrusion
+detection problems. A combination of TS technique and
+KNNispresentedin[104],whereKNNisinitiallyexploited
+to generate a subset of non-redundant features, whereas, TS
+isusedtorefinetheobtainedsubset. Again,authorsin[105]
+propose a wrapperFS algorithm, dubbed GATS-C4.5, that
+embedsanhybridGeneticandTabu-basedmethodasfeature
+selection strategy and a supervised ML algorithm (C4.5) as
+the evaluation function. Similar combinations between TS
+andGenetictechniqueshavebeenusedin[106]wheresome
+tests (vs pure Genetic algorithms) across DARPA dataset
+have been carried out, and in [107] where SVM has been
+adopted as a classification criterion.
+4.2.2. Scatter Search
+Scatter Search is a metaheuristic algorithm involving
+memory-based mechanisms similar to those exploited in
+Tabu Search [108]. The main strategy relies on an iterative
+process that organizes high-quality optimal solutions into
+subsets, and where five “methods" emerge: i/Diversifica-
+tion,tocreateasetofdifferenttrialsolutionsbyexploitinga
+seed solution; ii/Improvement , to convert a trial solution in
+one or more improved solutions; iii/Reference Set Update ,
+to create and keep update a reference set of best solutions;
+iv/SubsetGeneration ,tomanipulatethereferencesetaimed
+at deriving a subset of solutions; v/Solution Combination ,
+to linearly re-combine the solutions on the basis of subsets
+obtained with the previous methods.
+As an effective FS procedure, Scatter Search has been
+exploitedtogetherwithNLPsolversforglobaloptimization
+[109],withroughsetsfortheimplementationofcreditscor-
+ing mechanisms [110], or in a parallelized fashion to im-
+prove the feature subset selection problem [111]. Again,
+ScatterSearchhasbeenprofitablyexploitedinissuesinvolv-
+ingcreditcardsfrauddetection[112],andsoftwaresecurity
+characterization [113].
+4.2.3. PSO Search
+The Particle Swarm Optimization (PSO) algorithm has
+beenoriginallydiscoveredin[114],throughsimulationscar-
+riedoutacrossasimplifiedsocialmodelaimedatreproduc-
+ing the behavior of birds flocking.
+Then,thepopulationofagentsbecamemoresimilartoa
+swarm than flock, and single individuals were named parti-
+clesthat represent the candidate solutions. In a mathemat-
+ical form, given AÏRnthe search space, and f:A, , ™
+Y Ó R, the swarm is defined by a set S= ^x1;x2;§;xN`
+ofNparticles, where xi= .xi1;xi2;§;xin/TËAwith
+Di Mauro et al.: Preprint submitted to Elsevier Page 5 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+i= 1;2;§;N. It is assumed that particles are able to it-
+eratively move within search space Aby means of a ve-
+locity parameter defined as vi= .vi1;vi2;§;vin/Twith
+i= 1;2;§;N. In PSO method, it is also defined a mem-
+ory set P= ^p1;p2;§;pN`containing the best positions
+pi= .pi1;pi2;§;pin/T(withi= 1;2;§;N) visited by
+each particle. Given tthe time counter, the current position
+and velocity for particle iare, respectively, xi.t/andvi.t/,
+whereaspi.t/ = arg mintfi.t/. Accordingly, PSO is defined
+by the following equations:
+vij.t+ 1/ =vij.t/ +
1r1[pij.t/ *xij.t/](4)
++
2r2[g.t/ *xij.t/];
+xij.t+ 1/ =xij.t/ +vij.t+ 1/; (5)
+where:r1andr2denote random variables uniformly dis-
+tributed in [0,1], 
1is thecognitive parameter which affects
+thestepsizethattheparticletakestowardsitsbestcandidate
+solutionpij.t/, and
2is thesocialparameter which affects
+thestepsizethattheparticletakestowardstheswarm’sbest
+solutiong.t/. At each iteration, best positions pi.t+ 1/are
+updated as well, namely
+pi.t+1/ =h
+n
+l
+njxi.t+ 1/iff.xi.t+ 1//ff.pi.t//;
+pi.t/otherwise.(6)
+InthefieldofFSappliedtonetworktrafficanalysis,PSO
+hasbeenprofitablyexploitedjointlywithclassificationtech-
+niques. It is the case of [115, 116], where a particle swarm
+selectionmethodhasbeenexploitedwithSVM-basedclassi-
+fiers. Again,ahybridFSmodelbasedonPSOandRandom
+Foresthasbeenexploitedin[117],whereindependentmea-
+suresandalearningalgorithmareexploitedtoevaluatefea-
+ture subsets. Interesting is also an evolution of classic PSO
+advanced in [118], and dubbed Accelerated PSO, amenable
+to deal with FS on Big Data streams.
+4.3. Nature-inspired Feature Selection
+Although relying on meta-heuristic concepts, this fam-
+ily of algorithms takes inspiration from the nature ecosys-
+tem, where many animal species exhibit impressive behav-
+iorsaimedatoptimizingtheirlifecycle. WeassessAntOp-
+timization and Cuckoo search as representative algorithms
+of such family.
+4.3.1. Ant Search
+This technique, originally proposed in [119], is inspired
+byantscoloniesbehavior,wheretheoptimizationproblemis
+solvedbya“colony"ofcooperatingagents. Analysescarried
+out by ethologists showed that, following pheromone trails,
+eachantisabletofollowaprecedingantwhichreleasessuch
+substance, thus, a whole ant colony is able to self-organize
+itself. The emerging collective behavior relies on a positive
+feedback loop: the probability which an ant chooses a cer-
+tainpathincreaseswiththenumberofantsthatchoosingthesamepath,sincethetrailiscontinuouslyreinforcedwithnew
+pheromone. The Ant algorithm can be profitably exploited
+in feature selection problems by evaluating the probability
+pk
+ijthat thek-th “ant" could arrive to feature jby starting
+from feature i, namely,
+pk
+ij=h
+n
+l
+nj[ij][ij]
+³
+kËUk[ik][ik]ifjËUk;
+0otherwise,(7)
+where,Ukisthesetoffeasibleattributes(notvisitedyet), ij
+istheamountofpheromoneacrossthe ijpath,ijrepresents
+theheuristicinformationfortheselectedattribute j,and
+areparametersinchargeofcontrollingpheromonetrialsand
+heuristic information, respectively.
+As regards the FS problem in network traffic analysis,
+Ant-based methods have been used in: [120] where a SVM
+classifier is adopted on KDD99 dataset, after a feature re-
+duction obtained through ACO (Ant Colony Optimization)
+technique; in [121] where an ACO-based feature selection
+method allows to deal with big streamed data; [122] where
+animprovedACO-basedalgorithm(namedFACO)hasbeen
+designed and tested across the classic KDD99 dataset.
+4.3.2. Cuckoo Search
+This technique is inspired to the brood parasitism strat-
+egycharacterizingsomecuckoosspecies. Inparticular,such
+specieslaytheireggsinthenestsofotherbirds(hosts). Since
+hostbirdscanengageaconflictastheyrecognizealieneggs,
+particularcuckoospecieshaveevolvedinsuchawaythatfe-
+malesareableinmimickingcolourandsizeofeggsofsome
+host species, thus the hosts are cheated, and the probabil-
+ity of cuckoos reproductivity grows. Considering an egg in
+a nest as a solution, three idealized rules emerge in Cuckoo
+Searchprocedure[123]: i/eachcuckoolaysoneeggattime,
+in a randomly chosen nest; ii/the bests nests (having high-
+quality eggs) are candidate to carry over next generations;
+iii/thenumberofnestsisfixedand,asahostbirddiscovers
+alien(cuckoo)eggswithaprobability pd,itgetsridofit. The
+aimofthealgorithmistoexploitnew(andeventuallybetter)
+solutions in place of not-so-good solutions in the nest. New
+solutions xt+1
+ifor cuckooiare obtained through the follow-
+ing expression which represents the stochastic equation for
+random walk, namely,
+xt+1
+i=xt
+i+äL./; (8)
+where, > 0is a scale factor, the product ärefers to en-
+trywise multiplications, whereas Lis the Lévy distribution
+with ( 1<f3).
+Cuckoo search method has been exploited in network
+trafficanalysispairedwithvarioustechniquesandtechnolo-
+gies. In [124], authors propose an algorithm that uses PCA
+and Cuckoo Search to reduce the feature space and to op-
+timize the clustering center selection. A Cuckoo-based FS
+algorithm is proposed in [125] to preprocess network data
+Di Mauro et al.: Preprint submitted to Elsevier Page 6 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+aimedatimprovingtheIDSdetectionaccuracyinclouden-
+vironments. A Cuckoo search strategy has been also used
+in [126] to optimize Artificial Neural Networks when deal-
+ing with traffic anomaly detection issues. Again, coupled
+with SVM, Cuckoo search has been adopted in FS to deal
+with problem of phishing mail detection [127]. Recently,
+extended versions of Cuckoo Search algorithm have been
+advanced to cope with classification of tweetsin sentiment
+analysis[128],ortodefeatattacksinSoftwareDefinedNet-
+work infrastructures [129].
+4.4. Evolutionary Feature Selection
+Suchfamilyofalgorithmsisinspiredbynaturalselection
+theory, claiming that living organisms survived across mil-
+lions of years thanks to an adaptation process. In a similar
+way,thisaptitudecanbetranslatedinsearchforoptimalso-
+lutions to a problem. Two exemplary tested algorithms are:
+Genetic search and Multi Objective Evolutionary search.
+4.4.1. Genetic Search
+Genetic Algorithms (GAs) have been designed around
+themid-1950s,whenbiologistsstartedtoperformcomputer-
+based simulations aimed at analyzing more in deep the
+evolution of genetic processes [130]. Then, GAs have
+been extended to face problems ranging from neural net-
+works weight estimation [131] to inequalities-based prob-
+lems[132]. Apioneeringworkinthisfieldhasbeencarried
+outbyHolland[133,134],and,today,manyvariantsofGAs
+exist [135] and are applied in economy, computer science,
+sociology.
+The basic skeleton of a GA includes three operators
+[136]:Reproduction, Crossover andMutation.
+Reproduction refers to a process in charge of evaluating
+theabilityofanindividualtobeselected(amongothers)for
+reproduction, on the basis of a fitnessscore.
+Crossover concerns the capability of a genetic operator
+in recombining information to create new offspring. Typi-
+cally, offspring is generated by exchanging genes of parents
+until acrossover point is reached.
+Mutation pertains to the probability that some offspring
+genes could be modified or altered.
+Genetic-based feature selection in network traffic analy-
+sishasbeenusedinconjunctionwithmanyML-basedmeth-
+ods. Authors in [137] exploit a GA-based FS approach to
+optimize network traffic data before applying an artificial
+neuralnetworktoperformattacksdetectionacrosscloudin-
+frastructures. A combination of a genetic FS method and a
+supervised classifier based on J48 algorithm is proposed in
+[138]. More frequent across the scientific literature is the
+couplingbetweengeneticFSandSVMclassifiersappliedto
+networktrafficclassificationproblems(see[139,140,141]).
+When dealing with FS problems, GAs allow to explore
+the solution space by selecting the most promising regions,
+thus,avoidingacostlyexhaustivesearch. Inourdomain,the
+initial population is represented by the whole feature space
+and the fitness function relies on the correlation among fea-tures and expressed by means of a meritindicator defined
+further ahead in eq. (12).
+Once entered the cycle represented in Fig. 1, the algo-
+rithm calculates the fitness of each candidate solution per
+iteration, selects individuals to reproduce, and generates a
+newpopulationbytakingintoaccountcrossover(featurere-
+combination with a certain probability), and mutation (one
+featurecanbeturnedintoanotherfeaturewithacertainprob-
+ability).
+!"#$#%&'()*&%$#(" 
+!"#"$%&"'%#'(#(&(%)
+*+*,)%&(+#+-'(#.(/(.,%)0 +,%&*%$#(" 
+1/%),%&"&2"'-(&#"00'
+-,#3&(+#4+567'-,#3&78 -#$".//01%&*./ 
+900(:#&2"'%**$+*$(%&"'
+-(&#"00'/%),"0 
+2.&.3$#(" 
+;(#:)"'+,&'&2"'(#.(/(.,%)0 
+-+$'$"*$+.,3&(+# 4.)5(6*3$#(" 
+!"#"$%&"'#"<'(#.(/(.,%)0 
+43$+00+/"$='>,&%&(+#8'
+Figure 1: Genetic Algorithms life cycle.
+4.4.2. Multi-Objective Evolutionary Search
+The family of solutions concerning a multiobjective op-
+timization problem (MO) includes all the elements of the
+search space whose objective vectors cannot be simultane-
+ously improved (Pareto optimality concept) [142]. The set
+of such objective vectors is said non-dominated.
+Moreformally,aMOproblemcanbeformulatedasfol-
+lows: given a vector of nobjective functions fof a vector
+variable xin a domain Ddefined as
+f.x/ = .f1.x/;f2.x/;§;fn.x/; (9)
+a decision vector xhËDis Pareto-optimal iffthere is no
+xkËDsuch that:
+h
+n
+n
+l
+n
+njÅiË ^1;§;n`;kifhi
+á
+ÇiË ^1;§;n` :ki<hi:(10)
+On the other hand, Evolutionary Algortihms (EAs)
+can be profitably exploited in MO-based problems since
+many “individuals" can search in parallel for multiple so-
+lutions, with the possibility of taking advantage of similari-
+tiesamongsolutionsbelongingtothesamefamily. Possible
+implementationsofMO-EAtechniquesareENORA(Evolu-
+tionary NOn-dominated Radial slots based Algorithm), and
+NSGA (Non-dominated Sorted Genetic Algorithm) com-
+pared in [143].
+Applied to the FS problem, the purpose of a multi-
+objective search algorithm is to discover a subset of fea-
+tures (a family of solutions) being a good approximation of
+the Pareto front. The MO-EA approach has been exploited
+Di Mauro et al.: Preprint submitted to Elsevier Page 7 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+in network traffic classification jointly with ensemble ML-
+based methods [144], where some objectives such as max-
+imizing true positive rate, maximizing classification accu-
+racy,minimizingfeaturenumber,andminimizingfalsepos-
+itiverate,aresatisfiedsimultaneouslyandwithnoconflicts.
+An NSGA-based approach to ameliorate the performance
+classificationofIDSplatformshasbeenadoptedin[145]and
+in[146]. Again,aMO-EAtechniquejointlywithfuzzyclas-
+sifiers for coping with the traffic classification problem has
+been introduced in [147].
+5. The considered Datasets
+One of the great issues when dealing with supervised
+FSapproachesintrafficanalysis,isfindingtrainingsetsthat
+are both recent and labeled. As remarked in Sect. 3, many
+works rely on the obsolete KDD 99dataset [148]. Created
+about 20years ago, KDD 99has been broadly employed to
+validate machine learning algorithms, particularly to differ-
+entiatemaliciousdatatrafficfrombenignone. However,the
+KDD 99dataset does no longer reflect the characteristics of
+moderndatatraffic,whichhassensiblychangedacrosstime.
+Thisisverymuchthecaseofmultimediatraffic(e.g. voice,
+video), being the principle revenue-making stream for ser-
+vice providers but also the vehicle of covert malicious data.
+Percontra ,inthisworkweconsidermorerecentdatasetsob-
+tainedbymeansofCICFlowMeter[42,149],anopen-source
+enginethatcanbothgatherandlabelnetworktrafficinacon-
+trolled environment. Each dataset contains records labeled
+eitherasBenignorMalicious(malicioustrafficisoftensplit
+insub-labeledtrafficrepresentingdifferentkindsofattacks).
+Specifically, we consider the following datasets:
+•DDoSwhichcontainstrafficrelatingtodistributedde-
+nialofserviceattacks,aimedatsaturatingthenetwork
+resources of specific targets;
+•Portscan which contains traffic relating to Portscan
+attacks, aimed at discovering open, network device
+ports;
+•Webattack , including malicious traffic which imple-
+mentsvariousweb-basedattackssuchasBruteForce,
+Cross-Site Scripting, and Sql Injection;
+•TORwhichincludestrafficpassingovertheTORnet-
+work,ananonymousandprivatedatacircuitoftenex-
+ploited to carry dangerous information or malicious
+encrypted traffic;
+•Androidwhich embeds various families of Android-
+based threats (adwares, ransomwares, etc.).
+We want to notice that the first four datasets are exploited
+into the single class analysis, whereas the Androiddataset
+is exploited in the forthcoming multi-class analysis. In this
+latter analysis, we build a new dataset called MultiAndroid
+(see Sect. 6:2), obtained by selecting the most relevant mo-
+bile threats from the Androiddataset mixed with some be-
+nign traffic. In order to avoid the issue of bias that typi-
+cally arises during classification in imbalanced datasets, wehave first re-arranged the datasets. We have achieved well-
+balanced benign/malicious features, spanning across about
+50kinstances for each dataset. Each one contains up to 78
+features, except for the TOR dataset including 30 features.
+Forthesakeofconvenience,wefounditusefultogroupthe
+featuresin 5macro-classes(thecompletelistoffeaturescan
+be found in [150]):
+•Time-based features: Forward/Backward inter-
+arrivaltimes(IAT)betweentwoflows,durationofac-
+tive flow (min, max, mean, std), duration of idle flow
+(min, max, mean, std), etc.;
+•Byte-based features: Forward/Backward number of
+bytes in a flow, Forward/Backward number of bytes
+used for headers, etc.;
+•Packet-based features: Forward/Backward number
+of packets in a flow, Forward/Backward length of
+packets in a flow (min, max, mean, std), etc.;
+•Flow-based features: Length of a flow (mean, max,
+etc.);
+•Flag-based features: Number of packets with active
+TCP/IP flags (FIN, SYN, RST, PUSH, URG, etc.).
+6. Experimental Results
+Herein we describe our analytical study, which required
+the development of a dedicated Python routine, to normal-
+ize/balance the datasets and to automate the comparison
+among FS algorithms. We have validated the scrutinized
+search algorithms using the Correlation-based Feature Se-
+lector (CFS) as objective function [151, 152].
+The main idea behind CFS is that a good feature sub-
+setincludesthosefeaturesthatarehighlycorrelatedwiththe
+class,whilebeingstronglyuncorrelatedamongthem. Afor-
+mal definition is offered in [153]: a feature Xiis relevant iff
+there is some xiandyfor whichp.Xi=xi/>0such that
+p.Y=yðXi=xi/‘p.Y=y/: (11)
+Namely,Xiis relevant if Yis conditionally dependent on
+Xi. Thus, CFS is a filter algorithm that can rank feature
+subsets according to a correlation-based heuristic function.
+Precisely,givenasubset Sincludingkfeatures,theheuristic
+meritMS;kis defined as:
+MS;k=krfct
+k+k.k* 1/rff; (12)
+whererfcis the average value of feature/class correlations,
+andrffis the average value of feature/feature correlations.
+The numerator of (12) may be seen as an indicator of how
+far a set of features is predictive of a class; whereas, the
+denominator contains information about how much redun-
+dancy there is among features.
+Di Mauro et al.: Preprint submitted to Elsevier Page 8 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+(a) Ant (21 fts)
+ (b) Scatter (4 fts)
+ (c) MO-EA (5 fts)
+(d) Ranking (10 fts)
+ (e) Cuckoo (7 fts)
+ (f) Tabu/LFS (6 fts)
+(g) Genetic (27 fts)
+ (h) PSO (18 fts)
+Figure 2: Correlation maps for different algorithms - DDoS dataset. In parenthesis is
+reported the number of features surviving after the FS process.
+Our assessment is split into two parts: the first one con-
+cernsasingleclass analysis,whereweevaluatedatasetsex-
+hibitingdichotomous information (malign/benign); the sec-
+ond one is focused on multi class problems, where we eval-
+uate the effectiveness of FS in the presence of multiple
+classes.
+6.1. Single Class Analysis
+Let us consider the Distributed Denial of Service
+(DDoS) attack which, recently, is also affecting modern
+SDN-basednetworks[154,155]. DDoSattacksaredesignedto overwhelm the target network resources by means of a
+botnet, namely, a network composed of a large number of
+malicious nodes sending tiny packets towards the target, ul-
+timately coordinated by a botmaster .
+Let us now analyze the results obtained by pre-
+processing the DDoS dataset through the set of FS algo-
+rithms introduced above. In Fig. 2 we report, for each al-
+gorithm, the correlation map corresponding to a graphical
+representation of covariance matrices. This representation
+embedsthreeimportantpiecesofinformation: i)thenumber
+offeaturessurvivingaftertheFSprocessingstep; ii)thetype
+Di Mauro et al.: Preprint submitted to Elsevier Page 9 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+0 1 2 3 4 5
+Training Size ×104100101102Feat. Sel. Time (sec)MO-EA
+Rank
+Ant
+Tabu
+Genetic
+Particle Swarm
+Cuckoo
+Lin.Fwd.Sel.
+Scatter
+0 1 2 3 4 5
+Training Size ×104100101102Training Time (sec)NO Feat. Sel.
+MO-EA
+Rank
+Ant
+Tabu
+Genetic
+Particle Swarm
+Cuckoo
+Lin.Fwd.Sel.
+Scatter
+Figure 3: FS times - DDoS dataset (a); Training times - DDoS dataset (b).
+offeatures;and iii)therelationshipexistingamongsurviving
+features. Thelatteristakenintoaccountbymeansofagray
+scale, in which darker shades indicate higher levels of cor-
+relation. Thus, each .i;j/“pixel" gives the correlation level
+between feature iand featurej. Accordingly, the pixels on
+the main diagonal are always black (maximum correlation,
+corr= 1), due to the self-correlation. As was to be expected,
+highercorrelationarefoundamongthosefeaturesbelonging
+to the same family (Time-based, Flow-based, etc.).
+Some interesting considerations about the various cor-
+relation maps arise. First, the number of features retained
+by different algorithms may significantly diverge, which is
+due to the specific approaches adopted by each algorithm.
+TheGeneticalgorithmistheoneretainingthemostfeatures.
+This is to be ascribed to the particular strategy of this al-
+gorithm, which strives to escape local optima by applying
+themutationoperator,thusallowingtoconsidermorepaths,
+namely, more features. Second, some common features re-
+tained by all the algorithms can be recognized. For in-
+stance,thedestinationportfeatureisalwayspresentsince,in
+aDDoSattack, atargetvictimis typicallyreachedonapar-
+ticular exposed TCP/UDP port. Moreover, since DDoS at-
+tacksarecharacterizedbyalargeamountofsmall-sizepack-
+ets, features embodying information about packet lengths
+are retained. The difference is that, some algorithms (e.g.
+Scatter, MO-EA, Cuckoo, Tabu, LFS) just keep the essen-
+tialfeaturesrelatedtopacketlength(e.g. totalpacketlength,
+total number of bytes sent in initial window); whereas,
+other algorithms (e.g. Ranking, Genetic, PSO, Ant) pre-
+fer to retain more features belonging to the same family.
+DDoSisalsocharacterizedbysomekindofsynchronization
+amongthebots,whicharecoordinatedtolaunchanalmost-
+simultaneous attack. This means that time-related features
+willoftenprovideusefulinformationtodetectDDoS.Inter-
+estingly, the Genetic algorithm retains 5features relating to
+the inter-arrival flow times, resulting in a dark gray cluster
+at the center of the correlation map (Fig. 2(g)).ItisalsopossibleforDDoSattackstobeevenmoreeffec-
+tivethroughthemodificationoftheIPflags(e.g. SYN/RST
+flooding). Accordingly, features embodying information
+aboutIPflags(e.g. RST-SYN-URGflagcount)areretained
+by algorithms such as Ant (Fig. 2(a)), MO-EA (Fig. 2(c)),
+Cuckoo (Fig. 2(e)), Genetic (Fig. 2(g)), and PSO (Fig.
+2(h)). Let us note that many algorithms opt for selecting
+featuresthatareuncorrelatedamongthem(fewdarkgrayor
+blackclustersarepresent)sincetheyconveymorevariegated
+information.
+Let us now analyze some findings obtained from the
+time-complexity evaluation. To this aim, we use a PC
+equipped with Intel CoreTMi5-7200U CPU@ 2.50GHz
+CPUand16GBofRAM.InFig. 3(a),weshowhowtheFS
+timevarieswithtrainingsize,fortheDDoSdataset. Nodra-
+maticdifferencesareobservedacrossthevariousalgorithms,
+even more significantly as the training size grows. Consid-
+ering a relatively large training size (with 5104training
+instances), FS times range from about 10seconds (Scatter
+algorithm) to almost 26seconds (MO-EA algorithm). Sur-
+prisingly, the FS times are rather uniform, in spite of the
+broadvariationinnumberofretainedfeatures(byeachofthe
+algorithms). For instance, remaining in the case of 5104
+training instances, Scatter retains the minimum number of
+features (4), while Genetic retains the maximum number of
+features(27);yetFStimesarecomparable( 16:19and10:18
+seconds, respectively). Although it is legitimate to expect
+that higher FS time could be justified to produce a more re-
+ducedfeaturespace,thescarcecorrelationbetweensuchob-
+servables is due to the particular logic implemented in each
+FS algorithm.
+On the other hand, Fig. 3(b) provides the training times
+obtained by applying the J48 benchmark algorithm, down-
+stream of the FS processing step. Here, the black line (with
+emptycircles)givesthetrainingtimesobtainedwhennoFS
+processing is employed. We can observe how FS leads to
+significantimprovements,intermsofbothtimesandtrends.
+Di Mauro et al.: Preprint submitted to Elsevier Page 10 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.0051.01DDoS Dataset
+Accuracy (DDoS)
+F-Measure (DDoS)
+Accuracy (Benign)
+F-Measure (Benign)
+(a)
+  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.0051.01Portscan Dataset
+Accuracy (Portscan)
+F-Measure (Portscan)
+Accuracy (Benign)
+F-Measure (Benign) (b)
+  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.005WebAttack Dataset
+Accuracy (WebAttack)
+F-Measure (WebAttack)
+Accuracy (Benign)
+F-Measure (Benign)
+(c)
+  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.005TOR Dataset
+Accuracy (TOR)
+F-Measure (TOR)
+Accuracy (Non TOR)
+F-Measure (Non TOR) (d)
+Figure 4: Performance in terms of Accuracy/F-Measures for different single class datasets:
+DDoS (a), Portscan (b), WebAttack (c), TOR (d).
+The black (benchmark) line grows rapidly to almost 80 sec-
+onds,whilemostalgorithmspeaktoalmost5seconds,with
+theexceptionoftheGeneticalgorithm(yellowline)andthe
+Particle Swarm algorithm (light blue line) that take over 10
+seconds to complete. This indicates that the FS process, on
+the whole, brings gains in the range of about one order of
+magnitude,whichmaybecomeevenmoresignificantasthe
+dataset grows.
+Let us now analyze the performance of the proposed FS
+algorithmsintermsofAccuracyandF-Measure. Thesetwo
+metrics,widelyusedinthefieldoftrafficclassification[156,
+157], are defined as follows:
+•Accuracy : the ratio of the correctly predicted obser-
+vations to the total observations. This is the most in-
+tuitive indicator.
+•F-Measure : the weighted average of precision (ratio
+ofcorrectlyclassifiedflowsoverallpredictedflowsina class) and recall (ratio of correctly classified flows
+overallgroundtruthflowsinaclass). Thisisanindi-
+cator of a per-class performance.
+To verify that the effectiveness of the FS algorithms is
+notlinkedtospecificdatasets,wehaveconsideredthe 4dif-
+ferent datasets introduced in Sect. 5(DDoS, Portscan, We-
+bAttack, and TOR), reporting our findings in Fig. 4. Just
+like for the previous experiments, we have used the tree-
+based J 48algorithm as a benchmark. We have adopted a
+10-foldcross-validationwhichistypicalinappliedML,and
+offersagoodtrade-offbetweentrainingtimeandrobustness.
+Noticeably,allFSalgorithmsperformsatisfactorily(bothin
+accuracy and F-measure) in comparison to the benchmark
+(firstbarsinallthehistograms,labeledas“NOF.S.”)forthe
+four datasets.
+InsomeinstancestheFSalgorithmsperformedevenbet-
+ter than the benchmark (e.g., Rank and Genetic algorithms
+in the WebAttack dataset). This can be explained by a phe-
+Di Mauro et al.: Preprint submitted to Elsevier Page 11 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+(a) Ant (22 fts)
+ (b) Scatter/Tabu (9 fts)
+Tot Lenof BwdPktsFwdPktLenStdBwdPktLenMeanInit_Win_bytes_FwdInit_Win_bytes_BwdFlow Pkt/sTot Lenof BwdPktsFwdPktLenStdBwdPktLenMeanInit_Win_bytes_FwdInit_Win_bytes_BwdFlow Pkt/s (c) MO-EA (6 fts)
+(d) Ranking (28 fts)
+ (e) Cuckoo (17 fts)
+Tot Lenof BwdPktsFwdPktLenStdAvgBwdSegmentSizeInit_Win_bytes_FwdInit_Win_bytes_BwdFlow IAT MaxFwdPktLenMaxBwdPktLenStdFlow IAT MinTot Lenof BwdPktsFwdPktLenStd
+AvgBwdSegmentSizeInit_Win_bytes_FwdInit_Win_bytes_BwdFlow IAT MaxFwdPktLenMaxBwdPktLenStdFlow IAT Min (f) LFS (9 fts)
+(g) Genetic (31 fts)
+ (h) PSO (23 fts)
+Figure 5: Correlation maps - MultiAndroid dataset. In parenthesis is reported the number
+of features surviving after the FS process.
+nomenon that is well-known in ML, whereby models based
+on too many features may lead to biased classification. On
+theotherhand,whenFSmanagestoretainasufficientlyhigh
+number of meaningful features, there is a positive effect on
+accuracy. This is the case of the Genetic algorithm applied
+to the TOR dataset (Fig. 4(d)) that performs better than the
+other methods.6.2. Multi Class Analysis
+Another fruitful analysis is aimed at evaluating FS al-
+gorithms when multi-instance datasets are considered. This
+turns out to be particularly useful when it is not possible to
+discerndifferenttypesofdatatrafficviasomepre-processing
+filter (e.g. IP/Port-based filtering). To assess this case, we
+consider two datasets: the MultiAndroid dataset, containing
+benigntrafficmixedupwithfivedifferenttypesofAndroid-
+based threats; and the DDoS/Portscan dataset, including a
+Di Mauro et al.: Preprint submitted to Elsevier Page 12 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+0 1 2 3 4 5
+Training Size ×104100101102Feat. Sel. Time (sec)MO-EA
+Rank
+Ant
+Tabu
+Genetic
+Particle Swarm
+Cuckoo
+Lin.Fwd.Sel.
+Scatter
+(a)
+0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
+Training Size ×104100101102103Training Time (sec)NO Feat. Sel.
+MO-EA
+Rank
+Ant
+Tabu
+Genetic
+Particle Swarm
+Cuckoo
+Lin.Fwd.Sel.
+Scatter (b)
+Figure 6: FS times - MultiAndroid dataset (a); Training times - MultiAndroid dataset (b).
+mix of DDoS, Portscan, and benign traffic. The MultiAn-
+droiddataset,includesthefollowingtypesofmaligntraffic:
+•FakeApp.AL : a scareware hidden inside a fake
+Minecraft application, one of the most popular game
+applications;
+•Android Defender : a malware which, once acci-
+dentally downloaded and installed, raises some fake
+alerts;
+•Gooligam : an insidious malware that has already in-
+fected more than 1million Android-based devices,
+aimed at stealing Google accounts for Drive, Docs,
+Gmail, etc.;
+•Feiwo: belonging to the adware family, it acts by
+showingadvertisementsinthesystemnotificationbar,
+and by sending device GPS coordinates to a remote
+server;
+•Charger: a ransomware hidden in some Google Play
+applications, which gains root privileges and steals
+contacts before asking for a ransom.
+Let us analyze how FS algorithms impact on the Mul-
+tiAndroid dataset in terms of feature correlation referring
+first to the panels of Fig. 5. Comparing these results with
+the ones of Fig. 2, an interesting difference emerges: all FS
+algorithms retain more features w.r.t. the single-class case.
+Thisbehavioriscoherentwiththefactthat,todealwithdif-
+ferent types of threats (ransomware, adware, malware) we
+need more features, to be able to capture this higher vari-
+ability. This effect is even more evident in time-based fea-
+tures (mainly inter-arrival times) and in size-based features
+(mainly packet lengths).
+Looking at DDoS, we observe a difference between
+single- and multi-class analysis. In the latter, the destina-tion port is not retained as a crucial feature. This is possi-
+blybecausemalwaresexploitdifferentmechanismstocreate
+damage: rather than directly overwhelming a particular tar-
+getport,theyfirstactinthebackground(e.g. bystealingpri-
+vacy data) and then produce malicious traffic in egress. On
+the other hand, DDoS attacks generate ingress traffic from
+the infected device.
+It is worth noticing that, when applied to multi-class
+problems,allalgorithmshavepreservedtheiroriginallogic.
+For instance, with 31 surviving features, the Genetic algo-
+rithm is still the algorithm that saves more features, thanks
+totheroleplayedbythemutationoperator. Anotherexample
+istheMO-EAalgorithmthat,justlikeinthesingle-classex-
+periment,retainsthesmallestnumberoffeatures( 6). Thisis
+mainly due to the diversity-preservation mechanism, which
+forces the selection of a representative subset of the whole
+Pareto front. It optimizes conflicting objective functions,
+thus few solutions survive.
+The time-complexity evaluation is reported in Fig. 6,
+which evaluates the usual FS algorithms onto the MultiAn-
+droid dataset. FS times exhibit the same order of magni-
+tude as in single-class analysis (Figs.3(a)). For a training
+size amounting to 5104instances, the fastest algorithm is
+Scatter (FS time amounting to 9:541seconds); whereas the
+slowest one is MO-EA (FS time amounting to 24:827sec-
+onds).
+The situation changes dramatically when we consider
+training times for the J 48benchmark algorithm (Fig. 6(b)).
+Notably, multi-class algorithms are roughly one order of
+magnitudeslowerthantheirsingle-classcounterpart. Forin-
+stance,letusconsidertheGeneticalgorithm(yellowcurve).
+Fora 103trainingsize,GeneticFSreducesthetrainingtime
+to1:861seconds, growing to the following (X;Y) points:
+(104;10:731); (2 < 104;56:748); (3 < 104;133:346);
+(5 < 104;301:997). Thelongertrainingtimesarisefromthe
+process of training multiple classes. Nevertheless, signifi-
+Di Mauro et al.: Preprint submitted to Elsevier Page 13 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+   NO F.S.       MO-EA        Rank         Ant          Tabu        Genetic       PSO         Cuckoo      LFS/Scat   0.20.30.40.50.60.70.80.9Multi-Class Dataset (Android threats) - Accuracy
+FakeAppal
+Andr.Defender
+Gooligan
+Feiwo
+Charger
+Benign
+(a)
+   NO F.S.       MO-EA        Rank         Ant          Tabu        Genetic       PSO         Cuckoo      LFS/Scat   0.20.250.30.350.40.450.50.550.6Multi-Class Dataset (Android threats) - F-Measure
+FakeAppal
+Andr.Defender
+Gooligan
+Feiwo
+Charger
+Benign (b)
+  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.980.9850.990.99511.005Multi-Class Dataset (DDoS/Portscan) - Accuracy
+DDoS
+Portscan
+Benign
+(c)
+  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.980.9850.990.99511.005Multi-Class Dataset (DDoS/Portscan) - F-Measure
+DDoS
+Portscan
+Benign (d)
+Figure 7: MultiAndroid dataset: Accuracy (a), F-Measure (b); DDoS/Portscan dataset:
+Accuracy (c), F-Measure (d);
+cant gains are still obtained by all FS algorithms compared
+to the “NO F.S.” benchmark, which peaks at 446:329secs.
+Turning now to the performance analysis, in Fig. 7
+wecomparethetwomulti-classdatasets,MultiAndroidand
+DDoS/Portscan,drawingsomeinterestingconsiderations. It
+is comparably more difficult to detect Android threats than
+DDoS/Portscan attacks - MultiAndroid accuracy is below
+0:7and F-Measure is below 0:5. However, this issue is
+not generated by the FS processes, since the “NO F.S.” per-
+formance is poor too, particularly with the “Benign” class.
+This issue arises from two facts. First, mobile network at-
+tacks are often accompanied by activities that do not di-
+rectly/immediately generate network anomalies. Examples
+are ransomware and malware, whereby the anomalies arise
+after the user has downloaded the malicious application.
+There is typically a lag between infection and anomalies,
+as the malicious program initially establishes a secret/silent
+communication with a remote server, and then graduallysteals/sends private user data. Another example is adware,
+wherethoseannoyingbannersactuallyincurverylittledata,
+thusmakingithardtodetectfromtheregulartraffic. Asec-
+ond reason for the poor MultiAndroid performance is the
+strongsimilarityamongdifferentmalignclasses(e.g.,scare-
+ware, adware, ransomware). Similar considerations hold
+trueinthecaseinwhichweconsideradatasetincludingWe-
+battackandTORtraffic(notreportedforspaceconstraints),
+whereby the high similarity between the two classes re-
+sulted in poor classification performance. We should how-
+ever stress that FS algorithms are still very beneficial, since
+thetime-complexitybenefitsidentifiedareachievedwithno
+dramatic loss in accuracy.
+By contrast, the DDoS/Portscan multi-class case
+achieves outstanding performance (Figs.7(c) and (d)). This
+is because these types of attacks are radically distinct in
+the way they exploit network vulnerabilities: DDoS falls
+under the umbrella of volumetric attacks; whereas Portscan
+Di Mauro et al.: Preprint submitted to Elsevier Page 14 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
+attacks employ monitoring strategies to unveil possible
+open ports. In other words, a peculiar symptom of a DDoS
+attack is the presence of an exceptionally large number
+of connections coming from different nodes and heading
+towards one network target’s port. Conversely, a symptom
+of Portscan attacks is the presence of just a single node (or
+a few nodes in case of simultaneous Portscans) opening a
+considerably large number of connections towards multiple
+ports of a certain network target. Thus it is relatively easier
+to differentiate between these two attacks.
+6.3. General Remarks
+Overall, we can observe that FS algorithms do lead to
+an effective reduction in feature space, ranging from 65~
+(Single Class, Genetic) to 95~(Single Class, Scatter) and
+from 60~(Multi Class, Genetic) to 92~(Multi Class, MO-
+EA). Such feature-space reduction translates into signifi-
+cantcomputational-timeimprovements,whichbecomeeven
+more remarked as the training size grows. For instance,
+with a training set of 50ksamples (single-class DDoS) the
+MO-EA algorithm takes 24:8secs to perform FS, while the
+trainingtimecomparedtothebenchmarkdropsfrom 72:2to
+5:13secs. Atthesametime,performanceisnotsignificantly
+degraded by the feature reduction process - accuracy drops
+from 0:9993to0:9971. Similar considerations hold for all
+other algorithms.
+The performed assessment provides invaluable guide-
+linesfornetwork/securitymanagementpractitionersdealing
+with traffic classification problems. Our evaluation frame-
+work aims at weighing the practical benefits of the vari-
+ous FS techniques in terms of time-complexity reduction
+and performance guarantees. For instance, if we aimed at
+minimizing the overall processing time (i.e., FS plus train-
+ing times), the Scatter algorithm would be the best choice.
+Thisincursatotalprocessingtimeamountingto 14:338sec-
+onds for the single-class case (FS= 10:178secs plus train-
+ing= 4:16secs),andto 219:963secondsformulti-class(FS=
+9:541secsplustraining= 210:422secs). Conversely,theGe-
+neticmethodwouldbepreferabletomaximizeperformance.
+7. Conclusion and Future Direction
+Aprominentresearchdirectionfornetworkintrusionde-
+tectionistheadoptionofmachinelearningmethods,partic-
+ularly for the detection of anomalous (and often malicious)
+network-traffic flows. Looking at the literature, we find am-
+ple examples of network classification problems. Yet, little
+attention has been turned towards feature selection, which
+is an essential classification pre-processing step. We argue
+that the main reason for this overlook is that most studies
+have been based on the obsolete KDD 99dataset, which in-
+cludesfewfeatures,thusmakingFSirrelevant. Ontheother
+hand, we consider that modern network engines generate
+much richer features (in fact, hundreds of features), which
+allowmorefineandgranularnetworktrafficanalyses. How-
+ever, this extra capability results into impractical ML train-
+ingtimes,makingitnecessarytounderstandhowFSmaybe
+realized effectively.To this end, herein we have carried out an experimental
+comparativeevaluationofprominentmethods,withtheview
+to provide insights as to how the different FS algorithms
+perform in the peculiar context of network-traffic classifica-
+tion. Our assessment shows how few, relevant features are
+retained, but also that the FS reduction process is virtually
+lossless, with a significant acceleration of the overall train-
+ing process.
+To sum up, the novelties of our work are:
+i)we carry out an experimental-based review, consider-
+ingrecentdatasets(includingDDoS,Portscan,WebAttacks,
+and Android threats), as opposed to the obsolete KDD 99
+dataset adopted in most literature;
+ii)we compare and contrast a representative number
+of alternative FS algorithm types, including classic rank-
+guided methods (LFS, Ranking), meta-heuristic methods
+(Particle Swarm, Tabu, Scatter), nature-inspired methods
+(Ant, Cuckoo), and evolutionary methods (Genetic, MO-
+EA);
+iii)we provide actual experimental results, unveiling
+trade-offs between performance (Accuracy/F-Measure) and
+computational time, at different scales (training set size).
+Ultimately,ouranalysisshowsthebenefitslinkedtoem-
+bedding the FS process into network analysis, providing a
+valuable tool for identifying the most useful features out of
+hundreds of possibilities. This will prove invaluable to the
+fieldsofnetworkmanagement,securitymanagement,intru-
+sion detection and incident response. We should note that,
+the purpose of our comparative evaluation was not to claim
+the predominance of some FS algorithms over others but,
+rather, to suggest a methodical framework to work with FS.
+As a byproduct of our investigation, some interesting
+open research directions emerge: i)extending the present
+analysisto unsupervised FStechniques,whichwouldbeuse-
+ful to deal with datasets lacking class labels, or with new
+types of (unknown) malicious traffic - this is the case of so
+called zero-day attacks that have no prior information; ii)
+consideringthecaseofstreameddataanalysis,whichisnec-
+essary when dealing with extremely time-variant streams,
+wherebytheFSprocessshouldberepeatedacrosstime(e.g.
+byusingamobiletimewindow),soastoperiodicallyupdate
+theresultingdatasetwiththefreshestfeatures; iii)designing
+routines to automatically manage the best FS strategies to
+be applied in accordance to specific criteria (e.g. accuracy
+target, latency needs, etc.). Our investigation goes into the
+direction of the 6G paradigm that, according to most net-
+workscientists,willbecharacterizedbyintelligentresource
+management,smartadjustments,andautomaticservicepro-
+visioning.
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