Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes. Issue 113 (February 2022)

• Record Type: Journal Article
• Authors: Jim, Lincy E.; Islam, Nahina; Gregory, Mark A.
• Is Part Of: Computers & security. Issue 113(2022)
• Publication Date: 2022-02
• Subjects: Artificial immune system -- Dendritic cell -- Human immune system -- Mobile Ad hoc networks -- Routing -- Trust model; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2021.102538
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The dynamic distributed topology of a Mobile Ad Hoc Network (MANET) provides many challenges including decentralized infrastructure wherein each node acts as both the host and router for transiting traffic. MANET nodes provide the transmission capability to receive, transmit and route traffic from a sender node to the destination node. In this paper, we design an artificial immune system-based security method for MANET by simulating the mechanism of the human immune system. This paper presents a new bio inspired algorithm namely Artificial Immune System Based Algorithm (AISBA) to select selfish nodes in MANET, which utilizes principles of Artificial Immune Systems (AIS) that in turn mimics the strategy of the Human Immune System. Unlike Combined Immune Theories Algorithm, AISBA does not utilize a learning stage. In AISBA two different Trust models are developed as well to distinguish between a genuine node as well as a selfish node. An average detection rate of 93.41 % was achieved with this bio inspired approach. The Simulation results demonstrating the superiority of the proposed scheme compared to the well known secured AODV (SAODV) protocol in terms of packet delivery ratio (about 87 % of packet delivery ratio is achieved compared to about 37 % in the case of SAODV) and about 94 % of detection rate for the proposed scheme compared to 86 % for SAODV in the presence of misbehaved nodes. The statistical analysis of the false positive probability is also conducted to assess the efficiency of the obtained results, showing promising results with respect to the effect of weight and trust on threshold.