Paper Info
Title
An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms
Abstract
AbstractThis 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.
Year
DOI
Venue
2018
10.4018/IJCAC.2018040105
Periodicals
Keywords
Field
DocType
Cloud Computing, Neucube Algorithms, Online Intrusion Detection, Zero-Day Attack
Computer science,Real-time computing,Intrusion detection system,Cloud computing
Journal
Volume
Issue
ISSN
8
2
2156-1834
Citations 
PageRank 
References 
4
0.38
21
Authors
5
Name
Order
Citations
PageRank
Ammar Almomani11168.68
Mohammad Alauthman2183.39
Firas Albalas340.38
Osama Dorgham4325.29
Atef A. Obeidat542.07