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IDS for Improving DDoS attack recognition based on attack profiles and network traffic features

Sallam, Amer A. and Kabir, M. Nomani and Alginahi, Yasser M. and Jamal, Ahmed and Esmeel, Thamer Khalil (2020) IDS for Improving DDoS attack recognition based on attack profiles and network traffic features. In: 16th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2020, 28-29 February 2020 , Langkawi, Malaysia. pp. 255-260.. ISBN 978-172815310-0

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Abstract

Intrusion detection system (IDS) is one of the important parts in security domains of the present time. Distributed Denial of Service (DDoS) detection involves complex process which reduces the overall performance of the system, and consequently, it may incur inefficiency or failure to the network. In this paper, the attacks database is split into a set of groups by classifying the attack types in terms of the most dominant features that define the profile of each attack along with the sensitive network traffic features. Decision Tree, AdaBoost, Random Forest, K-Nearest Neighbors and Naive Bayes are then used to classify each attack according to their profile features. DDoS attack was considered for all chosen classifiers. It is found that the average classification accuracy for the above-mentioned algorithms is 95.31% , 95.68%, 95.69%, 92.61% and 83.11%, respectively, providing plausible results when comparing to other existing models.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: AdaBoost; Anomaly-based intrusion detection; Decision tree; K-nearest neighbors; Naive bayes; Random forest; Signature-based intrusion detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computing
Depositing User: Dr. Muhammad Nomani Kabir
Date Deposited: 24 Sep 2020 06:29
Last Modified: 24 Sep 2020 06:29
URI: http://umpir.ump.edu.my/id/eprint/29302
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