scholarly journals Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 56046-56058 ◽  
Author(s):  
Fadi Salo ◽  
Mohammadnoor Injadat ◽  
Ali Bou Nassif ◽  
Abdallah Shami ◽  
Aleksander Essex
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Intrusion Detection ◽  
Systematic Literature Review ◽  
Intrusion Detection Systems ◽  
Data Mining Techniques ◽  
Detection Systems

scholarly journals Data Mining Techniques for Providing Network Security through Intrusion Detection Systems: a Survey

2018 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Prabhu Kavin B ◽  
Ganapathy S
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Data Mining Algorithms ◽  
Soft Computing Techniques

Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.

Synthesis of Supervised Approaches for Intrusion Detection Systems

2014 ◽  
pp. 44-57
Author(s):  
Ahmed Chaouki Lokbani ◽  
Ahmed Lehireche ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Bayesian Networks ◽  
Decision Trees ◽  
Association Rules ◽  
Intrusion Detection Systems ◽  
Data Mining Techniques ◽  
Evaluation Measures ◽  
Detection Systems

Given the increasing number of users of computer systems and networks, it is difficult to know the profile of the latter, and therefore, intrusion has become a highly prized area of network security. In this chapter, to address the issues mentioned above, the authors use data mining techniques, namely association rules, decision trees, and Bayesian networks. The results obtained on the KDD'99 benchmark have been validated by several evaluation measures and are promising and provide access to other techniques and hybridization to improve the security and confidentiality in the field.

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