<prompt> Theha algorithm ho bona li-URL tse mpe ho latela sehlopha sa data sa li-URL.Sehlopha sa data sa 200 URLs  <response>: Instruction in English: Create an algorithm to detect malicious URLs given a data set of URLs. . Response in English: Step 1: Compile a list of known suspiciousmalicious URLs or domains for reference, this information can be retrieved using an API provided by organizations such as VirusTotal or PhishTank. Step 2: Preprocess the URLs in the dataset, Some of the important features that needs to be extracted from the given URLs include:- URL length, longer URLs than average length are more likely to be phishing.- Number of dots, since dots are used to delineate different levels of a URL, many dots are more typical of a phishing URL.- Presence of Suspicious Characters e.g , - , the presence of some characters has been found to be more frequent in phishing URLs.Step 3: Train a machine learning model using a training dataset, consisting of both legitimate and malicious URLs with similar features extracted from the data set on hand. Machine learning models that have shown to perform well in URL classification include Naive Bayes, Decision Tree, Random Forest and SVM.Step 4: Predict the labels for the 200 URLs in the dataset using the trained model. The model should be able to predict if the URL is legitimate or not based on the extracted features.Step 5: Compare the predicted labels with the known suspiciousmalicious URLs list to verify the accuracy of the model.Step 6: Flag any URL that is predicted as malicious by the machine learning model or is found on the known suspiciousmalicious URLs list to be investigated further.Note: For the best result, machine learning model should be periodically retrained using updated datasets. . Response in Sesotho: Mohato oa 1: Etsa lethathamo la li-URL tse tsebahalang tse belaetsang tse lonya kapa libaka bakeng sa litšupiso, tlhahisoleseling ena e ka khutlisoa ho sebelisoa API e fanoeng ke mekhatlo e kang VirusTotal kapa PhishTank. Mohato oa 2: Etsa esale pele li-URL ho dataset, Tse ling tsa likarolo tsa bohlokoa tse hlokang ho ntšoa ho li-URL tse fanoeng li kenyelletsa: - Bolelele ba URL, li-URL tse telele ho feta bolelele bo tloaelehileng ho ka etsahala hore e be phishing. - Palo ea matheba, kaha matheba a sebelisoa ho hlalosa maemo a fapaneng a URL, matheba a mangata a tloaelehile haholoanyane ho phishing URL. - Ho ba teng ha Litlhaku tse belaetsang mohlala, , - , boteng ba batho bao ho buuoang ka bona bo fumaneha hangata ho li-URL tsa phishing. Mohato oa 3: Koetlisa mokhoa oa ho ithuta ka mochini o sebelisa pokello ea lintlha tsa koetliso, e nang le li-URL tse molaong le tse mpe tse nang le likarolo tse tsoanang tse nkiloeng ho data e behiloeng letsohong. Mefuta ea ho ithuta ka mochini e bontsitseng ho sebetsa hantle ho sehlopha sa URL e kenyelletsa Naive Bayes, Decision Tree, Random Forest le SVM. Mohato oa 4: Etsa esale pele lileibole bakeng sa li-URL tse 200 ho dataset u sebelisa mohlala o koetlisitsoeng. Mohlala o lokela ho tseba ho bolela esale pele hore na URL e nepahetse kapa che ho latela likarolo tse nkiloeng. Mohato oa 5: Bapisa lileibole tse boletsoeng esale pele le lethathamo le tsebahalang la li-URL tse belaetsangbokhopo ho netefatsa bonnete ba mofuta ona. Mohato oa 6: Tlaleha URL efe kapa efe e boletsoeng esale pele e le kotsi ke mofuta oa ho ithuta oa mochini kapa e fumanoa lethathamong le tsebahalang la li-URL tse belaetsangbokhopo hore li hlahlojoe ho ea pele. Tlhokomeliso: Bakeng sa sephetho se setle, mofuta oa ho ithuta oa mochini o lokela ho koetlisoa nako le nako ho sebelisoa li-database tse ntlafalitsoeng. 