scholarly journals Design of Intrusion Detection System using Fuzzy Class-Association Rule Mining based on Genetic Algorithm

2012 ◽  
Vol 53 (14) ◽  
pp. 20-27 ◽  
Author(s):  
Swati Dhopte ◽  
N. Z. Tarapore
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Association Rule Mining ◽  
Detection System ◽  
Rule Mining ◽  
Class Association Rule

Association Rule-Mining-Based Intrusion Detection System With Entropy-Based Feature Selection

2020 ◽  
pp. 1-24
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Association Rule Mining ◽  
Detection System ◽  
False Positive Rate ◽  
Rule Mining ◽  
Detection Rates ◽  
Mining Algorithm ◽  
Positive Rate

Intrusion detection system (IDSs) are important to industries and organizations to solve the problems of networks, and various classifiers are used to classify the activity as malicious or normal. Today, the security has become a decisive part of any industrial and organizational information system. This chapter demonstrates an association rule-mining algorithm for detecting various network intrusions. The KDD dataset is used for experimentation. There are three input features classified as basic features, content features, and traffic features. There are several attacks are present in the dataset which are classified into Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The proposed method gives significant improvement in the detection rates compared with other methods. Association rule mining algorithm is proposed to evaluate the KDD dataset and dynamic data to improve the efficiency, reduce the false positive rate (FPR) and provides less time for processing.

Association Rule-Mining-Based Intrusion Detection System With Entropy-Based Feature Selection

2021 ◽  
pp. 183-206
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Association Rule Mining ◽  
Detection System ◽  
False Positive Rate ◽  
Rule Mining ◽  
Detection Rates ◽  
Mining Algorithm ◽  
Positive Rate

Intrusion detection system (IDSs) are important to industries and organizations to solve the problems of networks, and various classifiers are used to classify the activity as malicious or normal. Today, the security has become a decisive part of any industrial and organizational information system. This chapter demonstrates an association rule-mining algorithm for detecting various network intrusions. The KDD dataset is used for experimentation. There are three input features classified as basic features, content features, and traffic features. There are several attacks are present in the dataset which are classified into Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The proposed method gives significant improvement in the detection rates compared with other methods. Association rule mining algorithm is proposed to evaluate the KDD dataset and dynamic data to improve the efficiency, reduce the false positive rate (FPR) and provides less time for processing.

scholarly journals Association Rule Mining for Intrusion Detection System: A Survey

2019 ◽  
Vol 8 (1) ◽  
pp. 20-24
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Detection System ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Detection Systems ◽  
Large Sets

Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.

Sign in / Sign up

Export Citation Format

Share Document