Fusion of Feature Selection and Random Forest for an Anomaly-Based Intrusion Detection System

Author:

Khonde Shraddha1,Ulagamuthalvi V.2

Affiliation:

1. Research Scholar, Sathyabama Institute of Science and Technology, Chennai 600119, India

2. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India

Abstract

Considering current network scenario hackers and intruders has become a big threat today. As new technologies are emerging fast, extensive use of these technologies and computers, what plays an important role is security. Most of the computers in network can be easily compromised with attacks. Big issue of concern is increase in new type of attack these days. Security to the sensitive data is very big threat to deal with, it need to consider as high priority issue which should be addressed immediately. Highly efficient Intrusion Detection Systems (IDS) are available now a days which detects various types of attacks on network. But we require the IDS which is intelligent enough to detect and analyze all type of new threats on the network. Maximum accuracy is expected by any of this intelligent intrusion detection system. An Intrusion Detection System can be hardware or software that analyze and monitors all activities of network to detect malicious activities happened inside the network. It also informs and helps administrator to deal with malicious packets, which if enters in network can harm more number of computers connected together. In our work we have implemented an intellectual IDS which helps administrator to analyze real time network traffic. IDS does it by classifying packets entering into the system as normal or malicious. This paper mainly focus on techniques used for feature selection to reduce number of features from KDD-99 dataset. This paper also explains algorithm used for classification i.e., Random Forest which works with forest of trees to classify real time packet as normal or malicious. Random forest makes use of ensembling techniques to give final output which is derived by combining output from number of trees used to create forest. Dataset which is used while performing experiments is KDD-99. This dataset is used to train all trees to get more accuracy with help of random forest. From results achieved we can observe that random forest algorithm gives more accuracy in distributed network with reduced false alarm rate.

Publisher

American Scientific Publishers

Subject

Electrical and Electronic Engineering,Computational Mathematics,Condensed Matter Physics,General Materials Science,General Chemistry

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning based Robust Techniques to Detect DDoS Attacks in WSN;2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET);2022-09-22

2. Recommendations for DDOS Attack-Based Intrusion Detection System Through Data Analysis;Proceedings of Second Doctoral Symposium on Computational Intelligence;2021-09-20

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