Uncertainty-based Data Collection in Mobile Ad-Hoc Networks

Abstract

As an important security measures in Intrusion Detection System (IDS), Data Collection process monitors data in the network and supports network performance evaluation. Therefore, Data Collection plays an essential and a crucial role in distributed IDSs. Accordingly, the key idea of our proposed approach is to implement an effective and an adaptive data collection mechanism that could enhance the efficiency of the detection and identification of malicious nodes. In addition, we propose an efficient in-depth collection and analysis of data in mobile networks for intrusion detection system based on a uniform set of evaluation criteria. We note that the uncertainty present in the data collected represents a major challenge. Our proposed solution enhances intrusion detection efficiency by tacking into consideration incomplete information about occurring malicious nodes. This paper focuses on Denial of Service (DoS) attack scenarios within labeled datasets and specially the detection of the most potential threats that can disrupt the availability of routing services in mobile Ad-Hoc Network.