title: Hybrid Modified K-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems.
abstract: Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modified K-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modified K-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces the IDS processing time by up to 70%, while improving the detection accuracy.
authors.affiliation: Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), School of Computer Science, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bandar Baru Bangi, Malaysia ; Al-Furat Al-Awsat Technical University, Iraq.
authors.forename: Wathiq
authors.initials: W
authors.lastname: Laftah Al-Yaseen
authors.affiliation: Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), School of Computer Science, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bandar Baru Bangi, Malaysia.
authors.forename: Zulaiha
authors.initials: Z
authors.lastname: Ali Othman
authors.affiliation: Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), School of Computer Science, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bandar Baru Bangi, Malaysia.
authors.forename: Mohd Zakree
authors.initials: MZ
authors.lastname: Ahmad Nazri