Machine Learning based Intrusion Detection System for Minority Attacks Classification

Abhay Pratap Singh, Sanjeev Kumar, Amit Kumar, Mohd Usama

20222022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)30 citationsDOI

Abstract

With the fast usage of internet services, the Internet of Things (IoT) and edge machines are also connecting to the World Wide Web (WWW). The frequent utilization of these devices poses a significant amount of challenge to network security researchers to deal with advanced or sophisticated cyber threats. Traditional based antiviruses and Intrusion detection system (IDS) mechanisms failed to detect advanced cyber threats like minority and majority attacks. Therefore, an efficient mechanism is required to counter this type of cyber-attacks. This paper introduces an efficient IDS mechanism using several supervised machine learning algorithms. The machine learning classifiers are performed on the CIC-IDS dataset 2017 to analyze and inspect the performance of the presented model. The presented method displayed the average accuracy (99%), recall (100%) for four classifiers, namely, Random Forest (RF), Decision Tree (DT), Extra Tree (ET), and K-Nearest Neighbor (K-NN).

Topics & Concepts

Computer scienceIntrusion detection systemDecision treeMachine learningArtificial intelligenceRandom forestThe InternetEnhanced Data Rates for GSM EvolutionTree (set theory)Precision and recallStatistical classificationComputer securityData miningWorld Wide WebMathematicsMathematical analysisNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesInternet Traffic Analysis and Secure E-voting