scholarly journals A Multi-Fusion Pattern Matching Algorithm for Signature-Based Network Intrusion Detection System

2016 ◽  
Author(s):  
Manohar Naik S ◽  
Geethanjali N
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Matching Algorithm ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Pattern Matching Algorithm

Security has become a critical issue in today’s highly distributed and networked systems. Network intrusion detection systems (NIDSs), especially signature-based NIDSs, are being widely deployed in a distributed network environment with the purpose of defending against a variety of network attacks. Most of the commercially available NIDSs are software based and rely on pattern matching to extract the threat from network traffic. The increase in network speed and traffic may make existing algorithms to become a performance bottleneck. Therefore it is very necessary to develop faster and more efficient pattern matching algorithm in order to overcome the troubles on performance of NIDSs. Therefore, we propose a multi fusion pattern matching algorithm for Network Intrusion Detection Systems. The results obtained in percentages from the proposed fusion algorithm given better values in terms processing time in milliseconds than the existing algorithms when data English text are applied to evaluate the fusion performances.

scholarly journals Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

2021 ◽  
Vol 15 (16) ◽  
pp. 158
Author(s):  
Atheer R. Muhsen ◽  
Ghazwh G. Jumaa ◽  
Nadia F. AL Bakri ◽  
Ahmed T. Sadiq
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Selection Strategy ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Experimental Findings

<p>The task of network security is to keep services available at all times by dealing with hacker attacks. One of the mechanisms obtainable is the Intrusion Detection System (IDS) which is used to sense and classify any abnormal actions. Therefore, the IDS system should always be up-to-date with the latest hacker attack signatures to keep services confidential, safe, and available. IDS speed is a very important issue in addition to learning new attacks. A modified selection strategy based on features was proposed in this paper one of the important swarm intelligent algorithms is the Meerkat Clan Algorithm (MCA). Meerkat Clan Algorithm has good diversity solutions through its neighboring generation conduct and it was used to solve several problems. The proposed strategy benefitted from mutual information to increase the performance and decrease the consumed time. Two datasets (NSL-KDD &amp; UNSW-NB15) for Network Intrusion Detection Systems (NIDS) have been used to verify the performance of the proposed algorithm. The experimental findings indicate that, compared to other approaches, the proposed algorithm produces good results in a minimum of time.</p><p><strong> </strong></p>

scholarly journals Parallel Scalable Approximate Matching Algorithm for Network Intrusion Detection Systems

2020 ◽  
Vol 18 (1) ◽  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Matching Algorithm ◽  
Approximate Matching ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Computer Processor ◽  
Sequential Matching

Matching algorithms are working to find the exact or the approximate matching between text “T” and pattern “P”, due to the development of a computer processor, which currently contains a set of multi-cores, multitasks can be performed simultaneously. This technology makes these algorithms work in parallel to improve their speed matching performance. Several exact string matching and approximate matching algorithms have been developed to work in parallel to find the correspondence between text “T” and pattern “P”. This paper proposed two models: First, parallelized the Direct Matching Algorithm (PDMA) in multi-cores architecture using OpenMP technology. Second, the PDMA implemented in Network Intrusion Detection Systems (NIDS) to enhance the speed of the NIDS detection engine. The PDMA can be achieved more than 19.7% in parallel processing time compared with sequential matching processing. In addition, the performance of the NIDS detection engine improved for more than 8% compared to the current SNORT-NIDS detection engine

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