Indian Journal of Science and Technology

Abstract

Objectives: The rapid growth of new vulnerabilities causes the network by Denial of Service attack (DoS). The DoS attack causes traffic flow in network. Therefore it increases the difficulties to detect the DoS attack in traffic by means of misuse detection. The behavior patterns are analyzed in anomaly detection to identify the attack. Methods: In detection of unknown worms anomaly detection is more comfortable than misuse detection. In this paper, hybrid optimization and extreme machine learning classifier is proposed for anomaly detection. This approach detects the DoS attack by analyzing the profiles of traffic patterns. Findings: Kernel Principal Component Analysis (KPCA) is adopted in this approach to extract the feature from the dataset. A short time window is utilized to gather all features from packet headers. Extreme learning machine based HGAPSO is used to classify the unknown attack. Improvement: Thus the proposed system is implemented as real-time. Performance evaluation shows that this approach provides 1.016s time consumption and 95 % accuracy than existing approach during detection of DoS in network traffic.

Keywords: Dos, ELM, MLBG, Optimization