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All Journal Bulletin of Electrical Engineering and Informatics
Feresa Mohd Foozy, Cik
Universiti Tun Hussein Onn
Electrical & Electronics Engineering

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MULTISCHEME FEEDFORWARD ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR DDOS ATTACK DETECTION Muhammad, Arif Wirawan; Feresa Mohd Foozy, Cik; Malik, Kamaruddin
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2671

Abstract

Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, Scaled-Conjugate and Resilient-Propagation training functions. Based on the studies conducted, it concluded that ANN Architecture sufficiently affected the Scaled-Conjugate and Resilient-Propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the Quasi-Newton training function with 6-(12)-2 neural network architecture
Co-Authors Arif Wirawan Muhammad Malik, Kamaruddin