Survey of data mining techniques for intrusion detection systems

Cahyo, Aditya Nur and Winarko, Edi and Musdholifah, Aina (2020) Survey of data mining techniques for intrusion detection systems. In: 5th International Conference on Informatics and Computing, ICIC 2020.

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Abstract

Nowadays, the number of cyber-attacks is increasing; therefore, it is important for companies or organizations to secure their networks. Intrusion Detection System (IDS) is one of the core components used to secure networks. IDS's role is to detect an attack or attempted attack. The security tools are growing rapidly, along with the increasing number and variety of attacks carried out. The same with the development of IDS, a lot of research has been done to improve the ability of IDS to detect an attack. Many studies have used data mining techniques on IDS. This paper aims to conduct a comprehensive survey on the use of data mining techniques on IDS in the last five years. In this survey, we category the techniques used into several categories, and discuss each category in detail. © 2020 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 3; Conference name: 5th International Conference on Informatics and Computing, ICIC 2020; Conference date: 3 November 2020 through 4 November 2020; Conference code: 166085
Uncontrolled Keywords: ids, data mining, machine learning, anomaly ids,misuse ids
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department
Depositing User: Sri JUNANDI
Date Deposited: 12 Aug 2025 07:01
Last Modified: 12 Aug 2025 07:01
URI: https://ir.lib.ugm.ac.id/id/eprint/16703

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