An RSSI-based Sybil attack detection system with continuous authentication using a novel lightweight multimodal biometrics

Abstract

Purpose

Any node in a mobile ad hoc network (MANET) can act as a host or router at any time and so, the nodes in the MANET are vulnerable to many types of attacks. Sybil attack is one of the harmful attacks in the MANET, which produces fake identities similar to legitimate nodes in the network. It is a serious threat to the MANET when a malicious node uses the fake identities to enter the network illegally.

Design/methodology/approach

A MANET is an independent collection of mobile nodes that form a temporary or arbitrary network without any fixed infrastructure. The nodes in the MANET lack centralized administration to manage the network and change their links to other devices frequently.

Findings

So for securing a MANET, an approach based on biometric authentication can be used. The multimodal biometric technology has been providing some more potential solutions for the user to be able to devise an authentication in MANETs of high security.

Research limitations/implications

The Sybil detection approach, which is based on the received signal strength indicator (RSSI) variations, permits the node to be able to verify the authenticity of communicating nodes in accordance with their localizations.

Practical implications

As the MANET node suffers from a low level of memory and power of computation, there is a novel technique of feature extraction that is proposed for the multimodal biometrics that makes use of palm prints that are based on a charge-coupled device and fingerprints, along with the features that are fused.

Social implications

This paper proposes an RSSI-based multimodal biometric solution to detect Sybil attack in MANETs.

Originality/value

The results of the experiment have indicated that this method has achieved a performance which is better compared to that of the other methods.

Citation

Brindha, N.V. and Meenakshi, V.S. (2022), "An RSSI-based Sybil attack detection system with continuous authentication using a novel lightweight multimodal biometrics", International Journal of Intelligent Unmanned Systems, Vol. 10 No. 1, pp. 3-21. https://doi.org/10.1108/IJIUS-08-2020-0034