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.

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.

scholarly journals Intrusion Detection System using Genetic Algorithm and Data Mining: An Overview

2012 ◽  
pp. 118-122
Author(s):  
V.P. Kshirsagar ◽  
Sonali M. Tidke ◽  
S.S. Vishnu
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Maintenance Cost ◽  
Detection Systems ◽  
Detection Technology

Network security is of primary concerned now days for large organizations. Various types of Intrusion Detection Systems (IDS) are available in the market like Host based, Network based or Hybrid depending upon the detection technology used by them. Modern IDS have complex requirements. With data integrity, confidentiality and availability, they must be reliable, easy to manage and with low maintenance cost. Various modifications are being applied to IDS regularly to detect new attacks and handle them. In this paper, we are focusing on genetic algorithm (GA) and data mining based Intrusion Detection System.

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 MULTI-GIGABIT PATTERN FOR DATA IN NETWORK SECURITY

2013 ◽  
pp. 249-256
Author(s):  
Praveen Kumar . Ch ◽  
Prof.P.Vijai Bhaskar ◽  
Ravi. Ch ◽  
B.Rambhupal Reddy
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Pattern Matching ◽  
Intrusion Detection Systems ◽  
Search Pattern ◽  
Detection Systems ◽  
Network Intrusion ◽  
Matching Techniques ◽  
Detection Search ◽  
Perfect Hashing

In the current scenario network security is emerging the world. Matching large sets of patterns against an incoming stream of data is a fundamental task in several fields such as network security or computational biology. High-speed network intrusion detection systems (IDS) rely on efficient pattern matching techniques to analyze the packet payload and make decisions on the significance of the packet body. However, matching the streaming payload bytes against thousands of patterns at multi-gigabit rates is computationally intensive. Various techniques have been proposed in past but the performance of the system is reducing because of multi-gigabit rates.Pattern matching is a significant issue in intrusion detection systems, but by no means the only one. Handling multi-content rules, reordering, and reassembling incoming packets are also significant for system performance. We present two pattern matching techniques to compare incoming packets against intrusion detection search patterns. The first approach, decoded partial CAM (DpCAM), pre-decodes incoming characters, aligns the decoded data, and performs logical AND on them to produce the match signal for each pattern. The second approach, perfect hashing memory (PHmem), uses perfect hashing to determine a unique memory location that contains the search pattern and a comparison between incoming data and memory output to determine the match. The suggested methods have implemented in vhdl coding and we use Xilinx for synthesis.

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