Abstract: The tremendous growth of the usage of computers over the network and development in applications running on various platforms captures the attention toward network security. This paradigm exploits security vulnerabilities on all computer systems that are technically difficult and expensive to solve. Hence intrusion is used as a key to compromise the integrity, availability, and confidentiality of a computer resource. The Intrusion Detection System (IDS) plays a vital role in detecting anomalies and attacks in the network. In this work, the data mining concept is integrated with IDS to identify the relevant, hidden data of interest for the user effectively and with less execution time. Four issues such as Classification of Data, High Level of Human Interaction, Lack of Labeled Data, and Effectiveness of Distributed Denial of Service Attack are being solved using the proposed algorithms like the EDADT algorithm, Hybrid IDS model, Semi-Supervised Approach, and Varying HOPERAA Algorithm respectively. Our proposed algorithm has been tested using the KDD Cup dataset. The entire proposed algorithm shows better accuracy and reduced false alarm rate when compared with existing algorithms. Threat intelligence integration is a critical component of modern cyber security strategies, helping organizations stay one step ahead of cyber threats and minimize security risks. It empowers security teams to make data-driven decisions and respond effectively to evolving threats in today's complex threat landscape.
Keywords: Data Mining, Intrusion Detection, Network Intrusion, EDADT, HIDS, Hoperaa, Predictive Data Mining, Network Data Systems, Denial of Service (DNS), Distributed Data Mining, Host-based Intrusion Detection Systems, Threat Intelligence Integration Systems (TIIS).
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DOI:
10.17148/IJARCCE.2024.13926
