Combinational Feature Selection Approach for Network Intrusion Detection System

In the era of digital world, the computer networks are receiving multidimensional advancements. Due to these advancements more and more services are available for malicious exploitation. New vulnerabilities are found from common programs and even on vulnerability in a single computer might compromise the network of an entire company. There are two parallel ways to address this threat. The first way is to ensure that a computer doesn't have any known security vulnerabilities, before allowing it to the network it has access rights. The other way, is to use an Intrusion Detection System. IDSs concentrate on detecting malicious network traffic, such as packets that would exploit known security vulnerability. Generally the intrusions are detected by analyzing 41 attributes from the intrusion detection dataset. In this work we tried to reduce the number of attributes by using various ranking based feature selection techniques and evaluation has been done using ten classification algorithms that I have evaluated most important. So that the intrusions can be detected accurately in short period of time. Then the combinations of the six reduced feature sets have been made using Boolean AND operator. Then their performance has been analyzed using 10 classification algorithms. Finally the top ten combinations of feature selection have been evaluated among 1585 unique combinations. Combination of Symmetric and Gain Ratio while considering top 15 attributes has highest performance.