A Proposal of Rule-Based Hybrid Intrusion Detection System Through Analysis of Rule-Based Supervised Classifiers

Intrusion detection systems must detect the vulnerability consistently in a network and also perform efficiently with the huge amount of traffic. Intrusion detection systems must be capable of detecting emerging and proactive threats in the networks. Various classifiers are used to classify the threats as normal or intrusive by supervising the system activity. In this chapter, layered fuzzy rule-based classifier is proposed to detect the various intrusions, and fuzzy entropy-based feature selection is proposed to identify the relevant features. Layered fuzzy rule-based classifier is proposed to improve the performance of the intrusion detection system. KDD dataset contains various attacks; these attacks are grouped into four classes, namely Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Real-time dataset is also considered in this research. Experimental result shows that the proposed method provides good detection rate, minimizes the false positive rate, and less computational time.

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A novel rule-based Intrusion Detection System using data mining

2010 3rd International Conference on Computer Science and Information Technology ◽

10.1109/iccsit.2010.5563714 ◽

2010 ◽

Cited By ~ 14

Author(s):

Lei Li ◽

De-Zhang Yang ◽

Fang-Cheng Shen

Keyword(s):

Data Mining ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Rule Based ◽

Using Data

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Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026818500074 ◽

2018 ◽

Vol 17 (02) ◽

pp. 1850007 ◽

Cited By ~ 2

Author(s):

Syed Ali Raza Shah ◽

Biju Issac ◽

Seibu Mary Jacob

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Fuzzy Logic ◽

Intrusion Detection ◽

Open Source Software ◽

Intrusion Detection System ◽

Detection System ◽

Machine Learning Algorithms ◽

Support Vector ◽

Rule Based

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10[Formula: see text]Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10[Formula: see text]Gbps worked quite well.

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