Authors: H. Gohargazi; S. Jalili; M. Rahmanimanesh
Addresses: Faculty of Electrical and Computer Engineering, Tarbiat Modares University (T.M.U.), Tehran 14115-111, Iran ' Faculty of Electrical and Computer Engineering, Tarbiat Modares University (T.M.U.), Tehran 14115-111, Iran ' Faculty of Electrical and Computer Engineering, Semnan University, Semnan 35131-19111, Iran
Abstract: Optimised link state routing (OLSR) protocol as one of the four standard routing protocols provided for mobile ad hoc networks (MANETs) is vulnerable to attacks launched by authorised nodes. An anomaly detection system (ADS) that uses a small set of features, is unable to detect different types of attacks. In this paper, we define a set of features for OLSR behaviour to learn all behavioural aspects of this protocol. Furthermore, we propose a Conceptual Data Collection based ADS (CDC-ADS) in which ensemble methods are used to enhance the accuracy of anomaly detection. Data are collected based on four aspects of OLSR behaviour, then an expert model is learned for each aspect. A selection-based aggregation mechanism is used to conclude from votes of the learned models. The experiments show that creating the experts and combining their predictions increase the accuracy of detecting attacks. Also, testing CDC-ADS with various time slot lengths and network speeds shows its robustness.
Keywords: MANETs; mobile ad hoc networks; OLSR; optimised link state routing; ensemble methods; anomaly detection; routing attacks; aggregation; routing anomalies; mobile networks; time slot lengths; network speeds; network security.
International Journal of Mobile Communications, 2015 Vol.13 No.3, pp.276 - 298
Received: 12 Apr 2013
Accepted: 24 Mar 2014
Published online: 01 Apr 2015 *