A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
Keywords:
Intrusion Detection System, ML classifiers, Different IDS datasets, Evaluation matrix with accuracy, Detected assaultsAbstract
IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and automated processes, ensuring secured systems, applications, and networks has become one of the most significant problems of this era. The global web and digital technology have significantly accelerated the evolution of the modern world, necessitating the use of telecommunications and data transfer platforms. Researchers are enhancing the effectiveness of IDS by incorporating popular datasets into machine learning algorithms. IDS, equipped with machine learning classifiers, enhances security attack detection accuracy by identifying normal or abnormal network traffic. This paper explores the methods of capturing and reviewing intrusion detection systems (IDS) and evaluates the challenges existing datasets face. A deluge of research on machine learning (ML) and deep learning (DL) architecture-based intrusion detection techniques have been conducted in the past ten years on a variety of cyber security-based datasets, including KDDCUP'99, NSL-KDD, UNSW-NB15, CICIDS-2017, and CSE-CIC-IDS2018. We conducted a literature review and presented an in-depth analysis of various intrusion detection methods that use SVM, KNN, DT, LR, NB, RF, XGBOOST, Adaboost, and ANN. We have given an overview of each technique, explaining the function of the classifier mentioned above and all other algorithms used in the research. Additionally, a comprehensive analysis of each method has been provided in tabular form, emphasizing the dataset utilized, classifiers employed, assaults detected, an accurate evaluation matrix, and conclusions drawn from every technique investigated. This article provides a comprehensive overview of recent research on developing a reliable IDS using five distinct datasets for future research. This investigation was carefully analyzed and contrasted with the findings from numerous investigations.
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