The multi-demeanor fusion based robust intrusion detection system for anomaly and misuse detection in computer networks

Abstract

Combating the cyber threats, particularly attack detection, is a challenging area of the intrusion detection system (IDS). Exceptional development and internet usage raise concerns about how digital data can be securely communicated and protected. This work has proposed a multi demeanor fusion-based intrusion detection system where stream data mining based on ST-SR (stochastic relaxation). Thus it includes uncertain c capitals clustering with multi data fusion is incorporated to classify the fused network traffic information effectively. Subsequently, classified data would be sent to the web usage mining based on a stochastic Latent Semantic and synthetic Analyzer which analyzes the traffic information. Even though being classified and analyzed the network traffic information itself can’t get connected to the secured network due to its dynamic nature so to handle this situation, this work has incorporated IDS model (intrusion detection model based on parallel ensemble using bagging) which predicts the quality of service of each network during network traffic and enables the user to get connected with a secured network which holds high packet delivery ratio, less packet loss, and high throughput.