A Comprehensive Overview of IDPS and Using The TII-SSRC‎‎-2023 Dataset for Implementing Machine Learning Techniques ‎for Enhancing Network Security - Chandrasekharan , Ashwathi; Kizhakkethottam , Dr. Jubilant J (International Journal of Basic and Applied Sciences, 2025)
The growing number of connected devices and individuals, coupled with the rise in AI-driven attacks, has made robust network security ‎essential. The dynamic nature of networks, the increasing variety of attack types, and the exponential growth of Artificial Intelligence in ‎attacks have rendered traditional Intrusion Detection techniques obsolete. Therefore, it is necessary to create an effective Intrusion ‎Detection and Prevention System (IDPS). Only a warning or alert message is sent by the Intrusion Detection System (IDS); it does not ‎stop an intrusion from occurring within the network. Therefore, countermeasures can prevent, detect, and limit such attacks that could have a ‎significant impact on the services that these networks offer. This paper begins with an explanation of the distinctions between IDS and ‎IDPS.‎ Additionally, it describes several intrusion detection mechanisms and their corresponding tools, such as SNORT, OSSEC-HIDS, and ‎KISMET, continuing with the different IDPS types and how they operate. Next, for intrusion detection methods in large-scale networks, ‎we delve deeper into some machine learning-based approaches in this research, including SVM, Decision Trees, MLP, XGBoost, and ‎others. The TII-SSRC-23 dataset was utilized for evaluation, and the outcomes were compared to well-known machine learning-based ‎methods for network intrusion detection. Additionally, this paper provides a detailed explanation of various recent cyberattacks documented ‎in the TII-SSRC-23 dataset, including DDoS assaults, Brute-Force attacks, Mirai attacks, and information gathering‎.