Similarity-AF-Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics

Fadlil, Abdul and Riadi, Imam and Aji, Sukma (2017) Similarity-AF-Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics. Bulletin of Electrical Engineering and Informatics, 6 (2). ISSN 2302-9285

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Abstract

Distributed Denial of Service (DDoS) is a type of attack using the volume, intensity, and more costs mitigation to increase in this era. Attackers used many zombie computers to exhaust the resources available to a network, application or service so that authorized users cannot gain access or the network service is down, and it is a great loss for Internet users in computer networks affected by DDoS attacks. This research proposed to develop a new approach to detect DDoS attacks based on network traffic activity were statistically analyzed using Gaussian Naive Bayes method. Data will be extracted from training and testing of network traffic in a core router at Master of Information Technology Research Laboratory Ahmad Dahlan University Yogyakarta (MITRLADUY). The new approach in detecting DDoS attacks is expected to be a relation with Intrusion Detection System (IDS) to predict the existence of DDoS attacks based on average and standard deviation of network packets in accordance with the Gaussian method.

Item Type:	Artikel Umum
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Master (Magister) > Master of Technology Informatica (Magister Teknologi Informatika)
Depositing User:	Drs. Abdul Fadlil, M.T., Ph.D.
Date Deposited:	20 Aug 2022 04:55
Last Modified:	20 Aug 2022 04:58
URI:	http://eprints.uad.ac.id/id/eprint/36313

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