[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119833-en":3,"doc-seo-119833-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119833,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Performance Evaluation of Machine Learning Algorithms for Intrusion Detection System","Escalating cyber hazards and hijacking of digital networks demand effective defenses against malicious activity. Intrusion Detection Systems (IDS) are central for monitoring and recognizing attacks, and Machine Learning (ML) classifiers are increasingly used to distinguish normal from anomalous network traffic. This work analyzes IDS performance using ML methods on the KDD CUP-’99 dataset, comparing multiple classifiers with metrics such as accuracy, precision, recall, and F1 to assess detection quality.","PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS FOR INTRUSION DETECTION SYSTEM  \nSudhanshu Sekhar Tripathy 1 and Bichitrananda Behera 2  \n1, 2 Department of Computer Science and Engineering, C. V. Raman Global University Bhubaneswar, Odisha.  \n[Email:](Email:1 tripathysudhanshu6@gmail.com)[1](Email:1 tripathysudhanshu6@gmail.com)[ tripathysudhanshu6@gmail.com](Email:1 tripathysudhanshu6@gmail.com), [2](2 bbehera19@gmail.com)[ bbehera19@gmail.com](2 bbehera19@gmail.com)  \nAbstract  \nThe escalation of hazards to safety and hijacking of digital networks are among the strongest perilous difficulties that must be addressed in the present day. Numerous safety procedures were set up to track and recognize any illicit activity on the network's infrastructure. IDS are the best way to resist and recognize intrusions on internet connections and digital technologies. To classify network traffic as normal or anomalous, Machine Learning (ML) classifiers are increasingly utilized. An IDS with machine learning increases the accuracy with which security attacks are detected. This paper focuses on intrusion detection systems (IDSs) analysis using ML techniques. IDSs utilizing ML techniques are efficient and precise at identifying network assaults. In data with large dimensional spaces, however, the efficacy of these systems degrades. Correspondingly, the case is essential to execute a feasible feature removal technique capable of getting rid of characteristics that have little effect on the classification process. In this paper, we analyze the KDD CUP-'99' intrusion detection dataset used for training and validating ML models. Then, we implement ML classifiers such as “Logistic Regression, Decision Tree, KNearest Neighbour, Naïve Bayes, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, XG-Boost Classifier, AdaBoost, Random Forest, SVM, Rocchio classifier, Ridge, Passive-Aggressive classifier, ANN besides Perceptron (PPN), the optimal classifiers are determined by comparing the results of Stochastic Gradient Descent and backpropagation neural networks for IDS”, Conventional categorization indicators, such as \"accuracy, precision, recall, and the f1-measure\", have been used to evaluate the performance of the ML classification algorithms.  \nKeywords: ML classifiers, Intrusion detection system (IDS), False alarm rate, KDD CUP-99 dataset  \n1. INTRODUCTION  \nIn computer networks, the number of fraudulent operations, intrusions, and attacks has increased dramatically in recent years. Owing to innovations in technology progress, more than 90 percent of practical-circumstances activities are currently accessible in cyberspace. Several procedures involving financial services, buying something, examinations via the Internet, online sales, and exchange of information are thoroughly employed. With the dynamic development in the number of Internet-accessible services, Internet information security must be regularly maintained and adequate protection against cyberattacks is required. Use of traditional technology, such as a firewall, to repel attacks. Consequently, an IDS is typically set up to enhance the network security of businesses and other organizations [1] . A firewall is a passive system of manual protection, whereas an IDS system is an active system of automated protection.  \nAn IDS (Intrusion Detection System), is the technique for detecting and tracking intrusive activities and reporting on any security breaches in an IT infrastructure or a network, along with analyzing evidence of potential events, such as unpredictability or imminent threats of breaching computer network security designs, acceptable implemented policies, or obsolete safety provisions. The two primary types of intrusion detection systems IDS are based on both signatures and abnormalities. The first method utilizes a repository that contains known malicious activity signatures along with generating an alert if communication over the network fits a specific signature, while the","cbCaih4CT3HTpLwa","https://ap.wps.com/l/cbCaih4CT3HTpLwa","pdf",656853,1,20,"English","en",105,"# Introduction\n## Intrusion detection and cyber security challenges\n## Types of IDS: signature, anomaly, and hybrid\n## False positives and evaluation importance\n# Methodology and Dataset\n## KDD CUP-’99 dataset for training and validation\n# ML Models and Experiments\n## Classifiers compared\n## Performance metrics\n# Results and Comparison\n## Stochastic Gradient Descent vs backpropagation neural networks","[{\"question\":\"Why are IDS and machine learning used for intrusion detection?\",\"answer\":\"IDS monitor and report intrusive activities, while ML classifiers help classify network traffic as normal or anomalous, improving attack detection accuracy.\"},{\"question\":\"What dataset is used to train and validate the ML models?\",\"answer\":\"The study uses the KDD CUP-’99 intrusion detection dataset for training and validation.\"},{\"question\":\"Which performance metrics are used to evaluate the ML classification algorithms?\",\"answer\":\"The paper evaluates algorithms using accuracy, precision, recall, and the F1-measure (f1-score).\"}]","Performance Evaluation of Machine Learning Algorithms for Intrusion Detection System | 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are IDS and machine learning used for intrusion detection?","Question",{"text":75,"@type":76},"IDS monitor and report intrusive activities, while ML classifiers help classify network traffic as normal or anomalous, improving attack detection accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used to train and validate the ML models?",{"text":80,"@type":76},"The study uses the KDD CUP-’99 intrusion detection dataset for training and validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which performance metrics are used to evaluate the ML classification algorithms?",{"text":84,"@type":76},"The paper evaluates algorithms using accuracy, precision, recall, and the F1-measure 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