Simple Search:

Features selection for ids in encrypted traffic using genetic algorithm


Barati, Mehdi and Abdullah, Azizol and Mahmod, Ramlan and Mustapha, Norwati and Udzir, Nur Izura (2013) Features selection for ids in encrypted traffic using genetic algorithm. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28-30 Aug. 2013, Sarawak, Malaysia. (pp. 279-285).

Abstract / Synopsis

Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic due to inability to analyze packet content. An encrypted malicious traffic is able to evade the detection by IDS. Feature selection for IDS is a fundamental step in detection procedure and aims to eliminate some irrelevant and unneeded features from the dataset. This paper presents a hybrid feature selection using Genetic Algorithm and Bayesian Network to improve Brute Force attack detection in Secure Shell (SSH) traffic. Brute Force attack traffic collected in a client-server model is implemented in proposed method. Our results prove that the most efficient features were selected by proposed method.

Download File

[img] PDF
Restricted to Repository staff only

Download (713kB)

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
Publisher: UUM College of Arts and Sciences, Universiti Utara Malaysia
Keywords: Feature selection; Genetic algorithm; IDS; Encrypted traffic
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 03 Nov 2015 11:26
Last Modified: 03 Nov 2015 11:26
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item