An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

Ammar Almomani, Mohammad Alauthman, Firas Albalas, O. Dorgham and Atef Obeidat
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Ammar Almomani: IT Department, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan
Mohammad Alauthman: Department of Computer Science, Faculty of information technology, Zarqa University, Zarqa, Jordan
Firas Albalas: Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan
O. Dorgham: Prince Abdullah Ben Ghazi Faculty of Information Technology, Al-Balqa Applied University, Al Salt, Jordan
Atef Obeidat: IT Department, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

International Journal of Cloud Applications and Computing (IJCAC), 2018, vol. 8, issue 2, 96-112

Abstract: This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresses the source of the above issues through detecting an intrusion in cloud computing before it further disrupts normal network operations, because the complexity of malicious attack techniques have evolved from traditional malicious attack technologies (direct malicious attack), which include different malicious attack classes, such as DoS, Probe, R2L, and U2R malicious attacks, especially the zero-day attack in online mode. The proposed online intrusion detection cloud system (OIDCS) adopts the principles of the new spiking neural network architecture called NeuCube algorithm. It is proposed that this system is the first filtering system approach that utilizes the NeuCube algorithm. The OIDCS inherits the hybrid (supervised/unsupervised) learning feature of the NeuCube algorithm and uses this algorithm in an online system with lifelong learning to classify input while learning the system. The system is accurate, especially when working with a zero-day attack, reaching approximately 97% accuracy based on the to-be-remembered (TBR) encoding algorithm.

Date: 2018
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