Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization
Journal Title: EMITTER International Journal of Engineering Technology - Year 2016, Vol 4, Issue 2
Abstract
This paper describes the advantages of using Evolutionary Algorithms (EA) for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS) are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA) and Particle Swarm Optimizations (PSO) as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN) as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets). However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.
Authors and Affiliations
Iwan Syarif
Keywords
Related Articles
- EP ID EP269507
- DOI 10.24003/emitter.v4i2.149
- Views 131
- Downloads 0
Analysis of Channel Coding Performance in OFDM Technique for Underwater Acoustic Communication System
One way to increase the performance of Orthogonal Frequency Division Multiplexing System (OFDM) system is by adding a channel coding (error correction code) in order to detect and correct errors that occur when sending d...
Ambient Noise Characterization of Shallow Water Environment
Understanding of environmental characteristic is a key factor in design of communication system. This paper describes an ambient noise characterization of measurement in the shallow water ofSurabaya bay. The result show...
Optimizing Fuzzy Rule Base for Illumination Compensation in Face Recognition using Genetic Algorithms
Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Int...
An Implementation of Error Minimization Position Estimate in Wireless Inertial Measurement Unit using Modification ZUPT
Position estimation using WIMU (Wireless Inertial Measurement Unit) is one of emerging technology in the field of indoor positioning systems. WIMU can detect movement and does not depend on GPS signals. The position is t...
Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network
Wireless Sensor Network (WSN) can be applied for Air Pollution Level Monitoring System that have been determined by the Environmental Impact Management Agency which is and CO. In WSN, node system is constrained to a...