# Exploiting Inductive Bias in Transformer for Point Cloud Classification and
Segmentation

1st Zihao Li, 2nd Pan Gao  College of Computer Science and Technology
Nanjing University of Aeronautics and Astronautics, Nanjing
<EMAIL_ADDRESS><EMAIL_ADDRESS>3rd Hui Yuan  School of Control Science
and Engineering
Shandong University, Jinan
<EMAIL_ADDRESS>4th Ran Wei∗ *Corresponding author Science and Technology
on Electro-optic Control Laboratory, Luoyang
<EMAIL_ADDRESS>5th Manoranjan Paul  Charles Sturt University, Bathurst
<EMAIL_ADDRESS>
###### Abstract

Discovering inter-point connection for efficient high-dimensional feature
extraction from point coordinate is a key challenge in processing point cloud.
Most existing methods focus on designing efficient local feature extractors
while ignoring global connection, or vice versa. In this paper, we design a
new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point
relations, which considers both local and global attentions. Specifically,
considering local spatial coherence, local feature learning is performed
through Relative Position Encoding and Attentive Feature Pooling. We
incorporate the learned locality into the Transformer module. The local
feature affects value component in Transformer to modulate the relationship
between channels of each point, which can enhance self-attention mechanism
with locality based channel interaction. We demonstrate its superiority
experimentally on classification and segmentation tasks. The code is available
at: https://github.com/jiamang/IBT

###### Index Terms:

Point cloud, Inductive Bias-aided Transformer, classification, segmentation

##  I Introduction

Point cloud is a digitalized data of the surface on a real three-dimensional
object, containing geometry coordinates, and sometimes, also having attribute
information. The original point cloud data can be obtained by equipment such
as lidar or depth camera. Because of its irregularity and disorder, new
challenges have been brought to point cloud processing.

Thanks to Pointnet [1] for opening a precedent for directly absorbing raw
point cloud data [2, 3, 4], and also thanks to many public point cloud
datasets that are available for training and evaluation, methods based on deep
learning continuously emerge. Some approaches [1, 5, 6, 7, 8] based on MLP or
convolution operations can directly operate on unstructured raw points for
end-to-end training. However, these methods lack the modeling of the
interconnection between points. Considering the inherent topology of
unstructured data, many studies [9, 10, 11] have applied graph convolutional
neural networks to point cloud learning and achieved good results.The graph
structure can establish correlation between points and extract local
information between nodes. But how different neighbor points contribute to the
center point is not investigated. In recent years, inspired by the success of
the attention mechanism in the NLP field, many works [12, 13, 14, 15, 16, 17]
have also applied it to point cloud to further improve network performance. It
mainly calculates and obtains local or global attention coefficients, and then
linearly combines them with the corresponding feature vectors to obtain output
features, which can distinguish the different levels of contributions of each
point. But those attentions are paid to improving the ability of local feature
extractors while ignoring global attention and the interaction between local
and global attentions.

To address these issues, we propose an efficient Inductive Bias-aided
Transformer (IBT). Inductive Bias is indeed like the local consistency of CNN
filters, which is mentioned in many papers [18, 19, 20]. Broadly speaking,
inductive bias encourages learning algorithms to prioritize solutions with
certain properties. IBT mainly consists of three crucial modules.

Figure 1: The proposed Inductive Bias-aided Transformer. The top-left shows
the relative position encoding module for extracting position information, and
the top-right illustrates the procedure of fusion of two types of feature
pooling. The bottom panel shows how the improved transformer utilizes local
features to enhance self-attention mechanism.

The Relative Position Encoding module is mainly to explicitly encode the
spatial layout of 3D points, which plays a crucial role in shape analysis.
Existing methods [5, 21, 9] usually concatenate location information and
features into features for learning, but are not ideal in capturing meaningful
ensemble patches. Therefore, the advantage of our design module is to first
explicitly encode the point position, and then combine the output with the
feature of the point to further enhance it, so that each point can observe its
local geometry, thus finally enriching the entire network. This lays a good
foundation for subsequent deep feature learning. Attentive Feature Pooling in
the follow-up design is to update the features of the center point, focusing
on deep relational learning. we designe two branches to fully integrate deep-
level features, including maximum pooling to obtain the most prominent
features of neighboring points and attention pooling to automatically select
the most important features. In order to establish the connection between
local and global features, we pass the learned local features through the
sigmoid function to obtain the weight coefficient of each channel for each
point, and integrate them into the Locality Aware Transformer for global
feature learning. We stack three layers of IBT to obtain semantic information
from coarse-to-fine for better classification and segmentation. We have done
extensive experiments on the ModelNet40 [2], ScanObjectNN [3] and ShapeNetPart
[4] datasets to confirm its effectiveness. Our main contributions are
summarized as follows:

  * •

We design an effective Inductive Bias-aided Transformer layer to deeply
integrate local attention with global attention. The inductive bias refers
specifically to the locality used to model local visual structure.

  * •

We design Relative Position Encoding and Attentive Feature Pooling to obtain
the most critical and representative local features, and improve the
traditional transformer with locality-based channel interaction.

  * •

By incorporating the above modules into the design of the network
architecture, our method surpasses previous best performing work in point
cloud classification, and yields the best IoU performance for most of object
categories, demonstrating very competitive segmentation performance.

##  II Related work

Direct processing on raw points will not damage original geometry information.
PointNet [1] was the first of its kind to advance this type of research and
achieved good results. It mainly uses MLP for each point independently and
aggregates the global features with a symmetric function. Subsequent
development work can be roughly divided into three categories: MLP or
convolution based, graph convolution-based and Transformer-based methods.

_MLP or Convolution based Methods._ It extracts features through MLP or
convolution. Since PointNet [1] ignores the interconnection between points,
the subsequently proposed PointNet++ [5] uses a multi-scale structure to
effectively capture local features. In addition, there are some studies on how
to perform convolution on points. For example, PointConv [6] introduces point
density into the convolution kernel. This algorithm also proposes a strategy
for effectively calculating weight functions to increase network depth and
performance.

_Graph-based Methods._ It updates features by constructing local or global
graphs. Dynamic graph-based DGCNN [9] is good at capturing geometric
structural features from constructed local graphs. In addition, DeepGCN [11]
connects all points as a global graph structure, introduces residual
connection and convolution into graph convolutional network (GCN), which
finally builds a GCN as deep as CNN.

_Transformer-based Methods._ It pays more attention to important parts by
computing different coefficients between points. For example, PCT [12] obtains
deeper semantic information by stacking several global offset attention
layers. PT [13] uses attention to obtain semantic information of different
resolutions via the process of progressive downsampling. In addition,
PointANSL [14] employs the attention mechanism to handle noisy point cloud
data via adaptive sampling.

Figure 2: Network architecture for point cloud classification and segmentation
tasks.

##  III Method

We define the original input point cloud as
$X=\left\\{x_{i}|i=1,2,...,N\right\\}\in\mathbb{R}^{N\times 3}$ . Here $x_{i}$
represents the three-dimensional coordinates $(x,y,z)$ of the _i_ -th point.
Similarly, we define the feature of each point as
$F=\left\\{f_{i}|i=1,2,...N\right\\}\in\mathbb{R}^{N\times D}$ . We use KNN to
find k-nearest neighbors for each center point to construct a local graph
structure, including self-loops, defined as
$\mathcal{G}=(\mathcal{V},\mathcal{E})$, where
$\mathcal{V}=\left\\{1,2,...K\right\\}$ and
$\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}$ are the vertices and edges,
respectively. Suppose that $x_{i}$ is the center point of the graph structure,
and then $N(i)=\left\\{j:(i,j)\in\mathcal{E}\right\\}$ is the neighboring
point set. Next, we will introduce our Inductive Bias-aided Transformer and
the network architecture for point cloud classification and segmentation.

###  III-A Local Feature Extraction

We construct a local graph by finding k-nearest neighbors for each center
point in space, and then update features by interacting information with
neighboring points. We define the feature of the edge as
$e_{ij}=h_{\Theta}(f_{i},f_{i}^{j})$ , where
$h_{\Theta}:\mathbb{R}^{D}\times\mathbb{R}^{D}\rightarrow\mathbb{R}^{M}$ is a
nonlinear function with a set of learnable parameters $\Theta$. Finally, the
features of all edges are aggregated to obtain local features. As shown in the
upper part of Figure 1, it is mainly implemented by Relative Position Encoding
and Attentive Feature Pooling.

Relative Position Encoding. For each centroid point, we encode its relative
spatial position with k-nearest neighbors and their Euclidean distance to
better capture local geometric patterns. Additionally, we retrieve and encode
feature differences between pairs of points in the local graph. Finally, they
are concatenated and subjected to MLP operation to obtain rich relative
position information. The process is written as follows:

$p_{xi}=MLP((x_{i}-x_{i}^{j})\oplus\parallel
x_{i}-x_{i}^{j}\parallel\oplus(f_{i}-f_{i}^{j}))$ (1)

where $j\in N(i)$. $x_{i}$ and $x_{i}^{j}$ represent the original three-
dimensional coordinates, $f_{i}$ and $f_{i}^{j}$ represent the corresponding
features, $\oplus$ is the concatenate operation, and $\parallel\cdot\parallel$
calculates the Euclidean distance between the neighbours and center point.
Next, we concatenate the location information and point features into Feature
Pooling module after changing the dimension through MLP. The expression is as
follows:

$h_{xi}=MLP(p_{xi}\oplus f_{i}^{j}),~{}j\in N(i)$ (2)

Figure 3: Visual comparison with other methods for part segmentation.

Attentive Feature Pooling. This module is used to aggregate features of
neighboring points. We use a combination of max pooling and attention
mechanism to integrate update features. Max pooling can preserve the most
salient features of adjacent points, while the attention mechanism can
automatically learn the importance of local features.

Given a set of features of local graph points
$F_{i}=\left\\{f_{i}^{1},f_{i}^{2},...,f_{i}^{j}\right\\}$, we design a shared
function $g()$ which consists of a shared MLP and a softmax to learn an unique
attention score for each feature. It is defined as follows:

$s_{i}^{j}=g(f_{i}^{j},W_{s})$ (3)

where $W_{s}$ is the learnable weight coefficient.

The learned attention score can automatically allocate the importances of
features for weighted summation. On the other hand, we also use the maximum
pooling method to aggregate the features of neighbor points. For each center
point, our Relative Position Encoding and Attentive Feature Pooling module
learn to aggregate the geometric patterns and features of its K nearest
points, and finally generates an informative feature vector $\hat{f}_{i}$
denoted as follows:

$\hat{f}_{i}=MLP(\sum_{j=1}(f_{i}^{j}\cdot s_{i}^{j})\oplus
Max(f_{i}^{j})),~{}j\in N(i)$ (4)

###  III-B Locality Aware Transformer

Locality Aware Transformer integrates location embedding and local feature
attention to improve traditional point cloud transformers. Positional
embeddings allow the operator to adapt to local structures in the data, so
each point is transformed to the same dimensionality as the features using a
shared MLP.

$\delta=MLP(x_{i})$ (5)

Then it is added with the features of the corresponding point as the starting
feature $F_{in}$ as follows:

$F_{in}=f_{i}+\delta$ (6)

In addition, we send the local features obtained in the previous section to a
Sigmoid function for normalization to obtain a weight coefficient $W$ between
0 and 1 for each channel of each point as follows:

$W=Sigmoid(\hat{F})$ (7)

Next, we use the attention mechanism to compute the semantic similarity
between different items in a data sequence. Let $Q$, $K$, and $V$ be the
query, keyword, and value matrices generated by linear transformation on the
input feature $F_{in}=\mathbb{R}^{N\times D}$ respectively, as follows:

$Q,K=F_{in}\cdot(W_{q},W_{k})$ (8) $V=(F_{in}\cdot W_{v}+\delta)\cdot W$ (9)

where $W_{q}$, $W_{k}$, $W_{v}$ are shared learnable transformation
parameters. We use the original self-attention mechanism to calculate the
weight coefficients and multiply it with $V$. The process is expressed as
follows:

$F_{sa}=SoftMax(\frac{Q\times K^{T}}{\sqrt{d}})\times V$ (10)

where $d$ is the dimension of query and key vector. In the experiment, it is
set to D/4 to reduce the amount of calculation. Further, we employ the offset
attention mechanism according to PCT [12], which has strong theoretical basis
of Laplacian operator. It can further improve network performance by computing
the offset between self-attention features and input features with element-
wise subtraction. The complete offset attention is expressed as follows:

$F_{out}=MBR(F_{in}-F_{sa})+F_{in}$ (11)

where MBR represents a combination of MLP, Batch Normalization and ReLU
activation function.

TABLE I: Part segmentation on ShapeNetPart dataset. Metric is mIoU(%), and NUM
is the number of shapes in the category.

Methods | mIou | air. | bag | cap | car | cha. | ear. | gui. | kni. | lam. | lap. | mot. | mug | pis. | roc. | ska. | tab.
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
NUM |  | 2690 | 76 | 55 | 898 | 3758 | 69 | 787 | 392 | 1547 | 451 | 202 | 184 | 283 | 66 | 152 | 5271
Pointnet[1] | 83.7 | 83.4 | 78.7 | 82.5 | 74.9 | 89.6 | 73.0 | 91.5 | 85.9 | 80.8 | 95.3 | 65.2 | 93.0 | 81.2 | 57.9 | 72.8 | 80.6
Pointnet++[5] | 85.1 | 82.4 | 79.0 | 87.7 | 77.3 | 90.8 | 71.8 | 91.0 | 85.9 | 83.7 | 95.3 | 71.6 | 94.1 | 81.3 | 58.7 | 76.4 | 82.6
RGCNN[10] | 84.3 | 80.2 | 82.8 | 92.6 | 75.3 | 89.2 | 73.7 | 91.3 | 88.4 | 83.3 | 96.0 | 63.9 | 95.7 | 60.9 | 44.6 | 72.9 | 80.4
DGCNN[9] | 85.2 | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6
PCNN[22] | 85.1 | 82.4 | 80.1 | 85.5 | 79.5 | 90.8 | 73.2 | 91.3 | 86.0 | 85.0 | 96.7 | 73.2 | 94.8 | 83.3 | 51.0 | 75.0 | 81.8
3D-GCN[23] | 85.1 | 83.1 | 84.0 | 86.6 | 77.5 | 90.3 | 74.1 | 90.9 | 86.4 | 83.8 | 95.3 | 65.2 | 93.0 | 81.2 | 59.6 | 75.7 | 82.8
MLMST[17] | 86.0 | 83.6 | 84.7 | 86.3 | 79.8 | 91.1 | 71.2 | 90.2 | 88.6 | 84.9 | 95.9 | 72.8 | 94.8 | 83.4 | 56.2 | 76.7 | 82.6
PointASNL[14] | 86.1 | 84.1 | 84.7 | 87.9 | 79.7 | 92.2 | 73.7 | 91.0 | 87.2 | 84.2 | 95.8 | 74.4 | 95.2 | 81.0 | 63.0 | 76.3 | 83.2
PCT[12] | 86.4 | 85.0 | 82.4 | 89.0 | 81.2 | 91.9 | 71.5 | 91.3 | 88.1 | 86.3 | 95.8 | 64.6 | 95.8 | 83.6 | 62.2 | 77.6 | 83.7
Ours | 86.2 | 85.2 | 81.4 | 86.1 | 80.1 | 91.5 | 76.6 | 91.9 | 87.6 | 84.6 | 97.1 | 72.9 | 95.4 | 84.3 | 63.7 | 76.5 | 83.9

###  III-C Network Architecture

We design two network architectures for point cloud classification and
segmentation tasks using the proposed Inductive Bias-aided Transformer (IBT)
as shown in the figure 2. First, we send the point cloud coordinates to the
input embedding layer using the shared MLP to obtain a higher-dimensional
feature $F$, while saving the original coordinates. Then the coordinate $X$
and the feature $F$ of the corresponding point are sent to the three IBT
layers to obtain detailed low-dimensional geometric features of different
semantic levels. On the other hand, we max-pool the features obtained by the
embedding layer to obtain a low-dimensional global feature vector, which can
provide a coarse feature. In the experiments, the input embedding layer and
the output features of the IBT layer are both 128-dimensional.

Classification network. As shown in Figure 2, we concatenate three low-
dimensional geometric features and global feature vectors, and upscale them to
1024 dimension through shared MLP. Finally, the scores of different prediction
categories are output via the maximum pooling and linear layers.

Segmentation network. Segmentation requires more detailed features, so on the
basis of the 1024-dimensional global features obtained in the classification
task, we also introduce the feature vector of the category and concatenate
them with the detailed geometric features again, which are then gone through
MLP, Batch Normalization, ReLU activation function and Drop layer (MBRD) to
predict the category of each point.

##  IV Experiments

We evaluate the effectiveness of the proposed IBT using the ModelNet40 [2] and
ScanObjectNN[3] datasets for the point cloud classification and the
ShapeNetPart [4] dataset for the part segmentation. In addition, we also
compare the performance with other deep learning and transformer-based methods
to demonstrate its superiority.

Implementation details. We implement IBT and point cloud learning tasks using
Pytorch on two RTX 2080Ti GPUs. For classification and segmentation, we
randomly sample the original point cloud into 1024 points and send them to the
network for training and testing. Both use SGD optimizers with momentum and
learning rate of 0.9 and 0.1, respectively. For the classification task, the
k-nearest neighbor value is set to 40, and for the segmentation task, it is
set to 80 in order to obtain more detailed features.

###  IV-A Classification

The ModelNet40 [2] dataset contains 12311 CAD models in 40 categories. They
are split into 9843 models for training and 2468 models for testing. For
evaluation metrics, we use the average accuracy per class (mAcc) and the
overall accuracy (OA) across all classes. It can be seen from the comparison
in table II with other mainstream methods that the method we proposed can
obtain the highest average category and overall accuracy under the premise of
only inputting 3D coordinates, which demonstrates its excellent performance.

TABLE II: Classification results on ModelNet40.

Methods | Input | point | mAcc | OA
---|---|---|---|---
Pointnet++[5] | xyz,normal | 5k | - | 91.9
PointCNN[8] | xyz | 1k | 88.1 | 92.2
DGCNN[9] | xyz | 1k | 90.2 | 92.2
FPConv[24] | xyz,normal | 1k | - | 92.5
PointConv[6] | xyz,normal | 1k | - | 92.5
MLMST[17] | xyz | 1k | - | 92.9
3DCTN[25] | xyz,normal | 1k | 91.2 | 93.3
PointASNL[14] | xyz,normal | 1k | - | 93.2
CloudTransformers[16] | xyz | 1k | 90.8 | 93.1
PointStack[26] | xyz | 1k | 89.6 | 93.3
PCT[12] | xyz | 1k | - | 93.2
Ours | xyz | 1k | 91.0 | 93.6

ScanObjectNN [3] is a realistic 3D point cloud classification dataset that is
extremely challenging due to characteristics such as backgrounds, missing
parts, and deformations. It consists of occluded objects extracted from real-
world indoor scans and contains 2,902 3D objects from 15 categories. Some
method results are taken from the official website test of the dataset. In
addition, the number of input points and feature dimensions of the comparative
methods are unknown, so they will not be listed here. The input of our
experiment on this data set is only 3D coordinate of 1024 points. We also
compare the mean class and overall accuracy as shown in Table III. Due to the
challenges of this dataset, the comparison with other methods highlights the
efficiency and robustness of our method.

TABLE III: Classification results on ScanObjectNN.

Methods | mAcc | OA
---|---|---
Pointnet++[5] | 75.4 | 77.9
DGCNN[9] | 73.6 | 78.1
PointCNN[8] | 75.1 | 78.5
BGA-DGCNN[3] | 75.7 | 79.7
BGA-PN++[3] | 77.5 | 80.2
DRNet[27] | 78.0 | 80.3
GBNet[28] | 77.8 | 80.5
SimpleView[29] | - | 80.5
PRANet[30] | 79.1 | 82.1
Ours | 80.0 | 82.8

###  IV-B Segmentation

The ShapeNetPart [4] dataset consists of 16,880 models across 16 shape
categories annotated for 3D object part segmentation. Among them, 14006 3D
models are used for training and 2874 are used for testing. The number of
parts in each category ranges from 2 to 6, for a total of 50 different parts.
For each object, we convert the correct information of which category it
originally belongs to into one-hot encoding, and after MLP, it is a
64-dimensional feature vector shown in Figure 2. For evaluation metrics, we
report mIoU per class and mIoU across all shapes in all categories. The
comparison with other mainstream methods is shown in Table I, and some
visualization results are shown in Figure 3. Our method works better on more
categories, and it can be seen from the visual comparison that our method is
closer to the ground truth.

###  IV-C Ablation experiment

In this subsection, we further explore each module of the proposed method and
ablate different choices within the module. All ablation experiments are based
on classification experiments with 1024 points on the ModelNet40 [2] dataset.
First, we ablate the main modules including Relative Position Encoding,
Attentive Feature Pooling and Locality Aware Transformer modules as shown in
the table IV, where ✓means to keep this module, blank means to remove this
module, and replace it with MLP if necessary. It can be seen that the feature
extraction only through the local feature extractor or only the global
transformer is not as good as the proposed local and global interaction
method. Forming weight coefficients by local features to enhance the global
Transformer with channel attention can extract geometric detail features more
comprehensively.

TABLE IV: Ablation experiments for different modules.

Model |  Position |  Pooling | Transformer | mAcc | OA
---|---|---|---|---|---
A | ✓ | ✓ |  | 90.2 | 93.4
B |  | ✓ | ✓ | 90.0 | 93.2
C |  |  | ✓ | 90.6 | 93.4
D | ✓ | ✓ | ✓ | 91.0 | 93.6

On the other hand, we explore the effect of removing some operations in each
module and setting different k values as shown in Table V. It can be seen that
if the k value is set too small, the interaction with the surrounding points
is not enough. When the k value is too large, redundant information will be
generated, which is not conducive to subsequent tasks. In addition, in the
feature pooling module, it is proved that it is the most efficient way to
combine the most significant features produced by the maximum pooling and the
weighted features produced by the attention pooling. In the Locality Aware
Transformer module, we also did ablation experiments on the weight coefficient
$W$ used to link the local and global attentions, and whether the inclusion of
location embedding has an impact. It can be seen that the architecture we have
chosen performs best.

TABLE V: Ablation experiments for different options within the module.

Module |  | mAcc | OA
---|---|---|---
Relative Position Encoding | k=10 | 90.0 | 93.2
k=20 | 90.2 | 93.3
k=40 | 91.0 | 93.6
k=60 | 90.6 | 93.0
Feature Pooling | w/o maxpooling | 90.6 | 93.4
w/o attention pooling | 90.6 | 93.3
Locality Aware Transformer | w/o weight $W$ | 90.5 | 93.3
w/o Position Embedding | 90.3 | 93.3

##  V Conclusion

This paper proposes a new transformer for 3D point cloud understanding, i.e.,
Inductive Bias-aided Transformer (IBT), and conducts extensive experiments to
verify its effectiveness. The main contribution of our method is to extract
local features and incorporates them into an improved global Transformer in
the form of channel clues. In this way, we can finally obtain a more detailed
geometric feature represented by the fusion of local and global features. Our
proposed model has a good performance on the public datasets both
quantitatively and qualitatively. In addition, IBT is easy to integrate into
many existing networks in a plug-and-play manner.

## Acknowledgment

This work is supported in part by the Natural Science Foundation of China
under Grant 62272227 and the Aeronautical Science Foundation of China under
Grant 201951052001.

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## Abstract

Darshan Harsora1

1School of Engineering,
University of Guelph, Ontario, Canada
<EMAIL_ADDRESS>Khushalkumar Khoyani2
2School of Engineering,
University of Guelph, Ontario, Canada
<EMAIL_ADDRESS>
It is expected that the creation of next-generation wireless networks would
result in the availability of high-speed and low-latency connectivity for
every part of our life. As a result, it is important that the network is
secure. The network’s security environment has grown more complicated as a
result of the growing number of devices and the diversity of services that 5G
will provide. This is why it is important that the development of effective
security solutions is carried out early. Our findings of this review have
revealed the various directions that will be pursued in the development of
next-generation wireless networks. Some of these include the use of Artificial
Intelligence and Software Defined Mobile Networks. The threat environment for
5G networks, security weaknesses in the new technology paradigms that 5G will
embrace, and provided solutions presented in the key studies in the field of
5G cyber security are all described in this systematic literature review for
prospective researchers. Future research directions to protect wireless
networks beyond 5G are also covered.

## A systematic literature review of cyberwarfare and state-sponsored hacking
teams

Darshan Harsora1

1School of Engineering,
University of Guelph, Ontario, Canada
<EMAIL_ADDRESS>Khushalkumar Khoyani2
2School of Engineering,
University of Guelph, Ontario, Canada
<EMAIL_ADDRESS>
###### Index Terms:

cyberwarfare, cyber forces, hacked, cyber security, Information warfare,
cyberattack.

##  1 Introduction

Cyberspace has grown with the Internet’s quick expansion. The growth of
cyberspace is significantly altering and influencing communication practices.
Cyberspace, after the land, sea, air, and space battlefields, has become more
important in the military industry. Cyberattacks of all kinds are used to
advance and produce cyberwarfare in cyberspace [1].In this type of cyber risk
environment, it’s critical to swiftly recognize and recognize specific attack
information in order to successfully fight against assaults. The current cyber
defense system, however, is concentrated on repair after damage has been done.
The defensive system that is solely concerned with recuperation has a drawback
since it is hard to react quickly in between real operations. The recovery
point defense system’s drawback is that it is hard to react quickly in between
operational phases. A new framework for cyberwarfare operations is required to
support this. Every nation develops its hacking teams in a unique way. After
the army, navy, and air force, there is a distinct cyber force, which is the
fourth force. However, many of them are informal since they take the shape of
task forces made up of different community members who have been assigned
particular duties. This cyber army is made up of a variety of people with
exceptional and distinctive skills who work together to create a resilience
capacity against new cyberattacks [2].

There are some peculiar aspects of cyber warfare. Since the invention of
nuclear weapons and intercontinental missiles, it is perhaps the first
significant new style of warfare. Due to its novelty, there is now a virtual
policy space there are no educated, transparent, political, or public
conversations about what would constitute an ethical and prudent approach to
the employment of such weapons. The ”attribution problem” refers to the
difficulty in identifying the source of cyberattacks. Since many nations can
credibly assert that the cyberattacks may have begun from within their borders
but that their governments did not start them, this fact would provide many
cyberattacks credible immunity from prosecution [3].

###  1.1 Prior research

Regarding the cyberwarfare and hacking subject of our review, we have located
a sizable number of publications, research papers, and periodicals. The
information on this subject is still scant and insufficient. According to a
study on cyberwarfare by Richardus Eko Indrajit, Marsetio, Rudy Gultom, and
Pujo Widodo [4], the cyber army engage in four different types of operations.
Each pertains to attempts to: I defend important national assets; (ii) cope
with incoming assaults; (iii) launch attacks to render the opposing side
helpless; and (iv) wage war on different enemies. Additionally, it is done to
develop the knowledge and abilities required to be able to execute these four
activities.The findings indicate that thirty professions predominate in the
building of the collective cyber army capability.

”An operational website whose distinct and distinctive characteristics are
framed by the use of devices and the emission spectra to create, store,
modify, exchange, and manipulate information through interconnected
digitalization (ICT) based systems and their associated infrastructures,”
according to Dan Kuehl[5]. This fits with how we initially defined the
cyberworld: as any virtual reality confined within a group of devices and
networks. There are several cyberworlds, but the Internet is the one that has
the most bearing on the issue of cyberwarfare right now. Cyberwarfare combines
assault, defense, and special technical activities on computer networks [6].

Government websites in Ukraine were hacked, taken over, and flooded with
threatening comments during January 14–16, 2022. A report detailing the
discovery of ”destructive cyberattacks aimed directly against Ukraine’s
digitalization” was published online by Microsoft’s Threat Information Centre
(MSTIC) on January 15; days earlier, attackers were using a Structured Query
Language altitude weakness to insert ”wiper” ransomware in Ukrainian servers.
As a result, several Ukrainian organizations had their data wiped away on
January 18[7].

###  1.2 Research goals

The goals of this study are the analysis of previous research and its
conclusions, as well as a summary of the efforts of cyberwarfare research. As
stated below in table I, we created three research questions to help us
concentrate the effort.

Table I: Research questions. Research Questions(RQ) |  Discussion
---|---
RQ1: What is cyberwarfare? |  Cyberwarfare includes nation-state activity.or a foreign government or group to engage in harm the information or computers of another country networks, such as by computer viruses or assaults that disrupt service.
RQ2: Is cyberwarfare ethical? |  To assess the ethically appropriate military responses ethical and legal in the face of a cyber-attack Norms focused primarily on what is prohibited Physical confrontations are less obvious than relevant to cyberattacks without taking into account the target has a serious, damaging physical potential irrelevant code
RQ3: What kind of infrastructure is required to cope with cyberwarfare? |  utilities must create effective incident response systems. discuss their network’s best practices and make strategies, and promote openness by alerting authorities to a governmental body. a number of nations have established cyber soldiers or armies whose mission is the protection the nation from disasters brought on by online assaults.

###  1.3 Contribution and Layouts

For anyone wishing to advance their work in cyberwarfare and cybersecurity,
this SLR offers the following contributions:

  * •

We find 61 primary studies on hacking and cyberwarfare.Other academics might
utilize this collection of papers to deepen their understanding of this topic.

  * •

We further choose 15 primary research that satisfy the standards we establish
for quality evaluation. These papers can serve as appropriate comparison
points for examination of related research.

  * •

To communicate the concepts and problems in the disciplines of science of
cyberwarfare, hacking, and cyber security, we undertake an extensive
examination of the data included in the subset of 15 papers.

  * •

We give a meta-analysis of such current situation with relation to the
definition of cyber warfare, its moral underpinnings, and its effects on vital
infrastructure.

  * •

To encourage more research in this field, we establish recommendations and
make representations.

The format of this essay is as follows: The techniques used to choose the
primary studies for analysis in a methodical manner are described in Section
2. The results of all the primary research chosen are presented in Section 3.
The findings in relation to the earlier-presented study topics are discussed
in Section 4. The research is concluded in Section 5, which also makes some
recommendations for more study.

##  2 Research Methodology

We conducted a systematic evaluation of several cyberwarfare research
publications in order to reach the goal of answering the research questions.
To prepare for our systematic literature review, we will evaluate the studies
in-depth.

###  2.1 Selection of primary studies

The search capability of a certain magazine or search engine was emphasized by
entering keywords. The keywords were selected to support the growth of study
results that would help in resolving the research’s problems. On a number of
websites, we searched for research papers and reviews utilizing keywords as
”cyberwarfare,” ”cyberwar,” ”cybersecurity,” ”state-sponsored hacking,” etc.

Passing keywords to a particular publication’s or search engine’s search
function highlighted primary studies. The keywords were chosen to encourage
the development of study findings that would aid in addressing the study’s
issues [8]. We looked for research articles and reviews using keywords like
”cyberwarfare,” ”cyberwar,” ”cybersecurity,” ”state-sponsored hacking,” etc.
on a variety of web sites.

The platforms searched were:
\- IEEE Xplore Digital Library
\- SpringerLink
\- Google Scholar
\- UOG Library
\- ScienceDirect
According to the search platforms, the title, keywords, or abstract were used
in the searches. On October 15, 2022, we did the searches and processed all
research that had submitted up to that point. The inclusion/exclusion
criteria, which will be provided in Section 2.1, were used to filter the
results of these searches.

###  2.2 Inclusion and Exclusion criteria

Studies that are part of this systematic literature review should include
empirical findings and may include case studies, hypothetical analyses, or
studies that address the real consequences of cyberattacks organized by one
country or organization against another. The requirements for admission are
met by papers that discuss problems and difficulties in cyberwarfare. They
must be written in English and subjected to peer review. Google Scholar might
return papers that are of a lesser caliber, thus all results will be examined
for conformity with these standards [9]. This Systematic Literature Review
will only contain the most recent iteration of a study.

###  2.3 Selection results

The initial search results on the chosen platforms turned up a total of 61
studies. 52 were left after duplicate studies were eliminated. There were 20
publications left after the research were examined under the
inclusion/exclusion criteria. The inclusion/exclusion criteria were applied
again after reading all 20 articles, leaving 15 papers.

###  2.4  Quality assessment

In accordance with the guidelines established by Kitchen ham and Charters
[10], a review of the main studies’ quality was conducted. This made it
possible to evaluate the papers’ applicability to the research topics while
taking into account any indications of bias in the study and the reliability
of the experimental results. The evaluation procedure was modeled after the
method employed by Hosseini et al. [11]. Each and every one of the selected
main studies is subjected to the following quality evaluation checklist.

Table II: Inclusion and exclusion for primary studies Criteria for Inclusion |  Criteria for exclusion
---|---
The paper must present empirical data related to cyberwarfare and hacking groups. |  Grey literature such as blogs and government documents
The paper must contain information related to cyberwarfare, cyber security, and hacking |  Non-English papers
The paper published in a conference proceeding or journal |

The stages of finding the excluded studies, as seen in Table LABEL:Mytable3,
are as follows:
Stage 1: Cyberwarfare The study of cyberwarfare, state-sponsored hacking, and
information relating to cyber security must be the main topics of the paper.
Stage 2:Context The aims and conclusions of the research must be adequately
contextualized. This will make it possible to understand the research
correctly.
Stage 3: Executing Cyberattack The study must have sufficient information to
accurately depict how the cyberattack is carried out against a particular
infrastructure, which will help to address research questions RQ1 and RQ3.
Stage 4: Ethical context In an effort to help in answering RQ2, the paper must
explain the morality of cyberwarfare.
Stage 5: Data acquisition To assess accuracy, specifics regarding the data’s
collection, measurement, and reporting must be provided.

###  2.5  Data extraction

Data was then taken from all publications that had survived the quality
evaluation in order to evaluate the completeness of the data and verify the
accuracy of the information included within the articles. Before being applied
to the whole set of research studies that have successfully completed the
quality evaluation step, the data extraction technique was first tested on a
sample of five studies. Each study’s data was taken out, put into categories,
and then entered into a spreadsheet. The data was categorized using the
following categories:
Context data: Information on the study’s objectives.
Qualitative data: Results and recommendations offered by the authors.
Quantitative data: Data obtained from testing and research are then applied to
the study. The attrition rate of papers obtained from the first keyword
searches on each platform through to the final decision of primary studies are
shown in Figure 1 along with the quantity of articles chosen at each stage of
the process.

###  2.6 Data analysis

We gathered the information included in the qualitative and quantitative data
categories in order to achieve the goal of responding to the study questions.
We also performed a meta-analysis on the studies that were exposed to the last
step of data extraction.

####  2.6.1 Publications over time

Despite being ubiquitous, the phrase ”cyberwarfare” was less well-known due to
its limited use.But after 2013, the public may access a sizable number of
publications on cybersecurity and cyberwarfare. This may draw attention to how
novel the concepts of cyberwarfare and cybersecurity are. The number of
initial studies published each year is depicted in Figure 2 as a chart. The
employment of the terms ”cybersecurity” and ”cyberwarfare,” as seen in the
image, is on the rise. In the future, we anticipate seeing a rise in the
number of publications related to cyberwarfare.

Figure 1: Attrition of papers through processing Figure 2: publication over
time

####  2.6.2 Significant keyword counts:

An analysis of keywords across all 42 studies was done in order to identify
the recurring characteristics among the chosen primary research. The frequency
of various key terms in all primary studies is shown in Table III.

Table III: Counts of the keywords in the primary studies. Keywords |  Count
---|---
cyberwarfare |  1029
cyber security |  526
hacking |  120
critical infrastructure |  254
breach |  20
information warfare |  35
cyber conflict |  92
cyberspace |  74
cyberattacks |  141
denial-of-service |  210

##  3 Findings

After carefully reading each main research article, pertinent qualitative and
quantitative information was taken out and presented in Table 3. The main
issue or emphasis of each of the key studies was how cyberwarfare is changing
the globe. In Table 3 below, the emphasis of each study is also listed.The
percentages of the 30 main studies’ various themes that allowed them to pass
the quality evaluation and be incorporated into the data analysis are shown in
Figure 3.

Primary Study |  Key Qualitative & Quantitative Data Reported |  Theme
---|---|---
[12] |  The fast expansion of the Internet has led to an expansion of cyberspace. Cyberwarfare has replaced traditional warfare as a result of the growth of cyberspace. Cyberspace has been recognized as the fifth battleground, after ground, sea, air, and space, particularly in the subject of defense. |  cyberspace
[13] |  When compared to conventional combat, cyberwarfare is far cheaper and is conducted without endangering human life. Attacking a country or any personal data through cyberwarfare is on the rise nowadays. |  cyberwarfare
[14] |  There has been a lot of discussion on how cyberattacks, hacktivists, and underground cybercrime have contributed to the war between Russia and Ukraine. |  cyberattacks
[15] |  Many cyberattacks on critical systems in the past years will never be acknowledged owing to national security considerations. |  critical infrastructure
[16] |  Due to the fast growth and widespread use of computer technology, cyberspace has evolved into an essential component of a government, a society, and people’s daily lives. |  cyberspace
[17] |  Cyberwarfare is the use of viral assaults or denial-of-service (DOS) attacks by any international body to target and make attempts to destroy the systems, infrastructure, or data management of another country. |  denial-of-service
[18] |  Recent cyberattacks have blurred the distinction between real combatants and political hackers. This essay examines the implications of cybermilitias and the potential advantages and disadvantages of cyberconflict. |  hacking
[19] |  The conventional tendency of cyber attacks against physical computer systems is toward controlling and command of the physical infrastructure. |  cyberattack
[20] |  Human skills and capacities, including the capacity to guard, protect, assault, and win battles, must be tailored to the demands of cyberwarfare itself. A cyber unit must do all of these tasks in a variety of ways and in cooperation with critical institutions. These abilities represent the collective competency of a cyber troop. |  cyberwarfare
[21] |  Because cyberwarfare differs from traditional kinetic warfare, it is important to examine fundamental warfare concepts in order to distinguish it from traditional armed conflict. |  cyberwarfare
[22] |  In order to examine the mechanics and ramifications of offensive and defense cyberwarfare operations, Offensive and Protective Software Technologies is designed to bring along technical and non-technical cyberwarfare specialists, academics, and practitioners in relevant domains. |  Cyber security
[23] |  Cybersecurity for vital infrastructure and services throughout the world is changing as a result of the hybrid conflict in Ukraine. For a very long time after the battle is resolved, the events will continue to alter cybersecurity practices all across the world. |  Cyber security
[24] |  Cyberwarfare is the term used to describe politically inspired hacking for destruction and espionage. It is a sort of cyberwarfare that is occasionally contrasted with traditional warfare. |  cyberwarfare
[25] |  Cyberwarfare entails operations that have a military or political purpose. Second, it entails using cyberspace to deliver straight or compounding kinetic impacts that produce outcomes equivalent to those of conventional military forces.Third, it produces outcomes that either lead to a major threat to a country’s security, or that are taken in reaction to such a threat. |  cyberwarfare
[26] |  They can anticipate prolonged periods of low-intensity,global cyberwarfare, a Digital Cold War, while a play balance is pursued, and that cyberwarfare is not subject to control by international agreements. |  cyberwarfare
Table IV: Main findings and themes of the primary studies Figure 3: Chart of
themes of primary studies.

Nearly half 48% of all research are focused on cyberwarfare, according to the
themes found in the primary studies. With a proportion of 24.5% , cyber
security is the second most widely discussed subject. With 11.8% of the total
themes, hacking is the third most prevalent and is largely concerned with how
crucial political or financial information might be stolen by hackers.

The fourth most frequent subject, with a 6.6% share, is
cyberattacks.Cyberspace makes up 3.5% of primary study themes, whereas
Critical Infrastructure makes up 5.6%.

##  4 Discussion

Initial search terms reveal that there are several papers that discuss
cyberwarfare. Most of the chosen main studies are experimental hypotheses or
notions with limited quantitative information and few real-world implications
for challenges of today [27].”Cyberwar” and ”cyberwarfare” are two different
concepts. Cyberwarfare is the term used to describe the tactics, methods, and
procedures that may be deployed in a cyberwar. The term ”war” refers to a
large-scale struggle that frequently lasts a long period and may include
objectives including the use of force or the intention to kill.

Richard A. Clarke, who served as US President George W. Bush’s chief cyber
security advisor, described cyber war as ”activities by a nation state to
infiltrate another nation’s systems and networks for the objective of causing
injury or damage” (Knake and Clarke, 2010)[28].

According to the definition of cyberwar provided by the Oxford English
Dictionary, it is only ”another name for cyberwar.” Cyber war, as described by
the Oxford Dictionary (2013)[29], is ”the use of digital technology to hamper
a state or institution’s functioning, notably the deliberate attack of
communications infrastructure by some other country or organization.” Like the
other definitions under discussion, this one may be viewed as having
issues.Like any activity that has the potential to hurt, cyberwarfare raises
ethical issues. Particularly nations must comprehend if cyberwarfare is
morally permissible and how to conduct it morally [30].All cyberattacks are
currently orchestrated and planned by people. These tasks are projected to be
created and performed in the future by artificial intelligence. Artificial
intelligence (AI) technologies will be quicker than humans in analyzing and
breaching security measures. They will have considerably greater ability to
disrupt systems than they had in the past. One of the best methods to defend
against cyberattacks will be blockchain. Systems can be kept safe, and data
can be shielded from hackers.

Cyberwarfare is the useing of viral attacks or denial-of-service attacks by
any international organization to target and try to destroy the computers,
infrastructure, or data management of another country. Cyberwarfare is the use
of pcs, IoTs, and networking in a conflict area or as a component of covert
warfare. It involves aggressive and sceptical actions in respect to the risk
of cyberattacks, monitoring, and collateral/other harm. Whether or not these
actions fall within the definition of ”war” is up for discussion.
Transnational cyber-security is still a problem for major national and
international entities.Current experts lack the means to deal with the ongoing
change in the cybersecurity field since they lack in-depth knowledge of the
issue. Both the culprit and the victim of these cyberattacks are mostly the
United States. Following several cyberattacks, the nation has not implemented
the required security to ensure against modern-day crime: cyberwarfare[31].

####  4.0.1 RQ1: What is cyberwarfare?

It is crucial to emphasize that the systematic literature evaluation under
consideration will only concentrate on cyberattacks and warfare. In light of
this, it should be mentioned that the researchers discovered a large number of
papers on terrorism and conflict during the attrition process used to choose
the primary studies. But the selection process gave special attention to
research that have cyberwarfare at their core.

The term ”cyber warfare,” which is widely used in the establishment media, has
a wide variety of connotations. Cyberwarfare, according to Alford’s definition
from 2001, is ”any help to improve the overall to compel an adversary to carry
out our national will, performed against the program controlling functions
inside an adversary’s system.” [32]

This description from Alford represents the idea that nations would use
cyberwarfare to further a national objective. However, it may be argued that
modern combat does not necessarily strive to further such a cause. Possibly,
the objective of modern warfare is to spread non-national philosophies and
religious convictions. Therefore, it is imprudent to limit a definition of
cyberwarfare to having that goal as its primary objective.

”Cyber warfare is the science and art of battling without fighting; of
conquering an opponent without sacrificing their blood,” says Jeffrey Carr in
another explanation of the term [33]. Unlike Alford’s (2001), this term does
not try to explain why the opposing sides are fighting. The idea that
cyberwarfare won’t result in casualties, however, has to be called into doubt.
Losses of life might happen from a cyberattack on vital national
infrastructure, including the electrical system.

####  4.0.2 RQ2: Is cyberwarfare ethical?

Like any act that has the potential to do harm, cyberwarfare poses ethical
issues. Nations must precisely comprehend when and how to conduct such combat
in cyberspace in accordance with moral principles. According to Taddeo, the
Just War Theory, a set of precisely outlined criteria that define when a
country is ethically justified in starting a war and how to act decently while
at war, governs conventional battles. Taddeo argues that these ideas are
difficult to apply to cyber warfare and that these difficulties necessitate
further research[34].

Taddeo proposes three criteria that make up a ”Just Cyber War” in an effort to
solve the problem of cyber warfare ethics. These guidelines are connected to
the concept of a ”infosphere.” This is described by Taddeo as ”the setting in
which informational things, both alive and lifeless, digital and analog, are
ethically judged.”

  1. 1.

Only entities that threaten or interfere with the infosphere’s stability
should be targeted by cyberwarfare.

  2. 2.

To protect the health of the infosphere, cyberwar should be fought.

  3. 3.

Fighting a cyberwar shouldn’t be done to protect the infosphere.

Point 1 stands for the idea that using cyberwar is acceptable to get rid of
threats to the atmosphere’s wellbeing. The next two arguments represent the
idea that cyberwar should never be utilized to improve the well-being of the
Infosphere beyond its natural condition, but rather solely to restore it to a
status quo following a detrimental effect. Together, these arguments argue
that cyberwar is morally acceptable so long as it helps to preserve the
infosphere.

####  4.0.3 RQ3: What kind of infrastructure is required to cope with
cyberwarfare?

Attacks by hackers on communication networks, governments, or power grids may
bring entire civilizations to a standstill.As the battle in Ukraine shows,
this makes them an effective military tool.The actions that can be taken to
defend crucial infrastructure against cyberattacks:
Security Information and Event Management (SIEM):safeguards against dangerous
software and keeps track of network activities and access
Firewall:Inbound communication between computer operations and the outside
world is examined, evaluated, and filtered by a digital firewall.
Trust Zones: Your internal network has extra firewalls created to safeguard
sensitive information requiring increased protection.
Data Encryption: Secure connections and data on personal devices are crucial
for Iot network like smart metering and smart meters.
Multi-Factor Authentication:requires staff to access a system or network using
more than just a password

##  5 Conclusion and future study

This essay has offered an overview of current ideas about the difficulties
posed by cyberwarfare. It started off by examining the terms cyberwar and
cyberwarfare as they are now used. The United States should get ready for
three crucial things, according to James Miller, a former assistant secretary
of state of security for policy [35].

  1. 1.

Give strength for nuclear attack systems and for far-off conventional stages
top priority, and invest in it.

  2. 2.

Make a concerted effort to project the nation’s vital infrastructure and
maintain a safety margin so that terrorist organizations are unable to put the
nation at danger through cyberattacks.

  3. 3.

Create a type of script in advance and use it to direct and manage how you
will react to a sizable cyberattack.

The most important finding is that there is no one field that can adequately
address all of the problems brought by cyberwarfare. For instance, attribution
and cyber defense are undoubtedly technological concerns, but effective
resolution of these and other problems need political, legal, and societal
participation. Similar to this, developing a set of rules for cyber warfare
calls for input from the technical and military on what would be possible to
enforce in addition to legal experts.

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