Review of AI Techniques in development of Network Intrusion Detection System in SDN Framework

被引:2
|
作者
Dahiya, Seema [1 ]
Siwach, Vikas [1 ]
Sehrawat, Harkesh [1 ]
机构
[1] Maharshi Dayanand Univ, CSE, UIET, Rohtak, Haryana, India
关键词
AI; Deep Learning (DL); Machine Learning (ML); network security; IDS; NIDS; SDN; DEEP LEARNING APPROACH; NEURAL-NETWORK; TRANSMISSION;
D O I
10.1109/ComPE53109.2021.9752430
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Along with the advancement in the network and communication field in recent times, the attackers are also challenging the system in multiple ways. To ensure confidence, integrity, and availability, an intrusion detection system (IDS) is implemented to prevent possible network intrusion by inspecting network traffic and tracing malicious activities. The challenges associated with IDS are varied due to the pace of technology shift, new and different types of attacks need to develop a flexible and adaptive security system to mitigate the challenges. Due to advanced computational machine and CPU throughput, AI-based systems are used in various sectors, which apply machine and deep neural networks. In this paper, the recent paradigm shift of IDS systems to detect and prevent intrusions in public networks in a systematic manner with software defined networks is discussed at length.
引用
收藏
页码:168 / +
页数:7
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