A Technical Review on Intrusion Detection System with Various Machine Learning Algorithms in Data Mining
With the progression in information & communication technology (ICT), it has become a vital element of human’s life. But this technology has brought a lot of threats in cyber world. These threats increase the chances of network vulnerabilities to attack the system in the network. To evade these attacks there are distinct ways in which one is Intrusion-Detection System (IDS). IDSs are software or hardware products that automate this monitoring and analysis process. ID is a method of monitoring the un-authorized or un-wanted actions occurring in a computer system which violate n/w security practices. It is an essential technique in the network security domain which tries to identify whether the security of computer system has been compromised or not. IDSs are categorized as n/w, host, & application based systems depending on their mode of deployment & data used for examination. In IDS, there are various methods used in data mining and existing technique is not strong enough to detect the attack proficiently.
Keywords: Data mining, intrusion detection system, sbids, abids, random forest, machine learning approach
Cite this Article
Arvind Kumar, Rahul Gupta. A Technical Review on Intrusion Detection System with Various Machine Learning Algorithms in Data Mining. Journal of Multimedia Technology & Recent Advancements. 2018; 5(1): 1–8p.
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