scholarly journals High Throughput Signature Based Platform for Network Intrusion Detection

2013 ◽  
pp. 544-551
Author(s):  
José Manuel Bande Serrano ◽  
José Hernández Palancar ◽  
René Cumplido
Keyword(s):  
Intrusion Detection ◽  
High Throughput ◽  
Network Intrusion Detection ◽  
Network Intrusion

High throughput and low area cost FPGA‐based signature match circuit for network intrusion detection

2009 ◽  
Vol 32 (3) ◽  
pp. 397-405 ◽  
Author(s):  
Wen‐Jyi Hwang ◽  
Chien‐Min Ou ◽  
Ying‐Nan Shih ◽  
Chia‐Tien Dan Lo
Keyword(s):  
Intrusion Detection ◽  
High Throughput ◽  
Network Intrusion Detection ◽  
Low Area ◽  
Network Intrusion

A Studying on Implementation of NIDS Pattern Matching Based on FPGA

2011 ◽  
Vol 403-408 ◽  
pp. 1985-1988
Author(s):  
Jing Jiao Li ◽  
Ho Cholman ◽  
Yong Chen ◽  
Song Ho Pak
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
High Throughput ◽  
High Performance ◽  
Low Cost ◽  
Network Intrusion Detection ◽  
Application Area ◽  
Network Intrusion ◽  
Rule Set ◽  
Matching Performance

Intrusion detection for network security is an application area demanding high throughput. The pattern matching in intrusion detection requires extremely high performance to process string matching. Most of pattern matching using software has many time complexities and cannot reach the requirements of high throughput. The pattern matching using hardware considerably improves the speed of matching and has several other advantages. This paper describes a FPGA-based pattern matching architecture, using hashing method called XOR Hashing. The proposed method updates new patterns without reconfiguration and processes the collision and has high matching performance. The proposed system implements the pattern matching by using Snort rule-set, an open source Network Intrusion Detection and has simulation processing on PC. Compared with existing hardware method, the results explained that our method has relatively high performance for the pattern matching and can else process the pattern matching with high performance on low–cost FPGA device.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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