INTRUSION DETECTION BASED ON DEEP LEARNING TECHNIQUES IN COMPUTER NETWORKS
Main Article Content
Abstract
Security Breaches in computer networks have increased a lot in the last decade due to the profitable underground cybercrime economy. Many researches have been working on finding efficient techniques for detecting intrusions. Many surveys were present on different Machine Learning and Deep Learning Techniques in the last decade. Solutions proposed for dealing with network intrusions can be broadly classified as signature based and anomaly based. In this paper, a critical survey of Machine Learning (ML) and Deep learning (DL) techniques presented in the literature in the last ten years is presented. This survey would serve as a supplement to other general surveys on intrusion detection as well as a reference to recent work done in the area for researches working in ML and DL based intrusion detection systems. Some open issues are also discussed that are needed to be addressed.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.