Building an Intrusion Detection System on Ecommerce Data using Regression Analysis


  • Praveen Kumar Shukla Department of Computer Science & Engineering, FET, Rama University, Kanpur 209217, INDIA
  • C. S. Raghuvanshi Department of Computer Science & Engineering, FET, Rama University, Kanpur 209217, INDIA
  • Hari Om Sharan Department of Computer Science & Engineering, FET, Rama University, Kanpur 209217, INDIA


Intrusion Detection System, Machine Learning, LR, K Nearest Neighbour, SVM, Network Security


A system for anomaly-based intrusion detection learns to identify acceptable network behaviour in order to detect intrusion. When anomalous network behaviour is observed outside of its training sets, it then issues a warning. Administrators utilize the Network Intrusion Detection and Prevention System to identify network security vulnerabilities in their organizations by detecting and blocking a number of well-known network attacks. It is more crucial than ever to identify network anomalies and cyberattacks since they aid in the creation of an efficient intrusion detection system, which is necessary for contemporary security. The Canadian Institute of Cyber Security published a new data set called CICIDS2019 network data set, which fixed the NSL-KDD issue. The research's Network Intrusion Detection dataset can be downloaded for free from Kaggle. The dataset is standardised after being pre-processed to eliminate cells with null values. Based on the networking facts, a variety of computational techniques have been used to determine whether or not an intrusion has occurred, including classic ML and ensemble learning models. Classic machine learning methods like AdaBoost, Naive Bayes, K Nearest Neighbour, Support Vector Machine, and Logistic Regression are employed in this work. The Ad, K Nearest Neighbour, Naive Bayes, Support Vector Machine, and Logistic Regression models are all developed into the proposed model. According to the accuracy, precision, recall, and f-measure experimental findings from the NSL-KDD dataset used in this work, the proposed system outperforms the existing methods.


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How to Cite

Kumar Shukla, P. ., Raghuvanshi, C. S. ., & Sharan, H. O. . (2023). Building an Intrusion Detection System on Ecommerce Data using Regression Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 731–743. Retrieved from



Research Article