Intelligent Attack Detection and Network Intrusion based on Cyber Attack Invasion in Data Security: Review and Open Issues from Machine Learning Perspective

Sunday Ochigbo, Souley Boukery, Sani Abba

Abstract


Network security is closely related to computers, networks, programs, various data, and so forth, where the purpose of defense is to prevent unauthorized access and modification. However, the growing number of internet-connected systems in finance, E-commerce, and military makes them become targets of network attacks, resulting in large quantity of risk and damage. Essentially, it is necessary to provide effective strategies to detect and defend attacks and maintain network security. Furthermore, different kinds of attacks are usually required to be processed in different ways. How to identify different kinds of network attacks thus becomes the main challenge in domain of network security to be solved, especially those attacks never seen before. To overcome this problem, a lot of efforts have been devoted to modeling the attack or anomaly by using machine learning techniques. Machine learning is successfully used in many areas of computer science such as image processing and intrusion detection. Hence, this research survey intelligent attack detection and network intrusion based on multi-channel invasion in data security. The research surveys the existing literature covering their contributions and limitations respectively. Base on the review, we identified the research opportunities that can be utilized by researches to enhance security in information with high integrity.


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