DRI Table Based Traffic-Behaviour Analysis Approach for Detection of Blackhole Attack

Abstract

Background: The blackhole infection can affect the collaborative communication in mobile networks. It is man-in-middle attack that seizes and deflects the route and avoids packet-forwarding in the network. The occurrence of collaborative-blackhole reduces the trust and trustworthiness over the network. Objective: A probabilistic and weighted analysis based protocol is proposed in this research for detection of cooperative blackhole nodes and generating the preventing route over the network. The aim of the work is to improve the communication reliability. Methods: In this paper, the communication behaviour is analyzed under associated and probabilistic measures using Data Routing Information (DRI) table to discover the blackhole attack. It applies a dual check based on participation and communication constraints to estimate the node criticality. The evaluation is performed by neighbours and neighbour-on-neighbour nodes with weights and threshold specific decisions. These measures are evaluated through composite and integrated measures and presented as decision metrics. The parametric and probabilistic checks are conducted as a comprehensive evaluation within the proposed PSAODV (Probabilistic Secure Adhoc On Demand Distance Vector) protocol. Results: The simulation of PSAODV protocol is conducted in NS2 environment on various scenarios with mobility, density and traffic type variations. The scenarios are defined with a higher density of blackhole nodes within the network. The adaptive weights are identified by simulating the network with different weight combinations. These weights are employed within the PSAODV protocol to configure it with the maximum benefits. The analytical evaluations are taken against AODV and SAODV protocols and identified the performance enhancement in terms of Packet Delivery Ratio (PDR) Ratio, delay, attack detection ratio parameters. Conclusion: A significant improvement in attack detection is achieved by this proposed PSAODV protocol. The proposed protocol improved the reliability and effectiveness of mobile network.

Design of a novel micro‐Zone adaptive Energy‐Trust evaluation Active On‐Demand Vector protocol to optimize communication in challenging and attacked mobile network

“…The communication is performed in extreme conditions including congestion density 0 to 10%, and varied attack types (blackhole, grayhole, and DDOS). After setting up the scenario, the communication is performed using AODV, SAODV, PSAODV, 39 PDS‐AODV, 40 NCV‐AODV, 17 iNCV‐AODV, 17 DFMCRP, 4 FBSSR, 21 Tripathy et al, 6 MBDP‐AODV, AGHA, Singh et al, and mZ‐ETAODV protocols. Probabilistic‐secure AODV (PSAODV) 39 used Data Routing Information (DRI) table‐based probabilistic evaluation on various node‐level features for locating the attackers.…”

“…After setting up the scenario, the communication is performed using AODV, SAODV, PSAODV, 39 PDS‐AODV, 40 NCV‐AODV, 17 iNCV‐AODV, 17 DFMCRP, 4 FBSSR, 21 Tripathy et al, 6 MBDP‐AODV, AGHA, Singh et al, and mZ‐ETAODV protocols. Probabilistic‐secure AODV (PSAODV) 39 used Data Routing Information (DRI) table‐based probabilistic evaluation on various node‐level features for locating the attackers. The author used the participation and communication‐based features to estimate the reliability of nodes.…”

“…In this subsection, the experiment is conducted on the network described in Section 4.1. The comparative results are taken against AODV, SAODV, PSAODV, 39 PDS‐AODV, 40 NCV‐AODV, 17 iNCV‐AODV, 17 DFMCRP, 4 FBSSR, 21 Tripathy et al, 6 MBDP‐AODV, AGHA, and Singh et al protocols. The simulation results are collected against energy and communication measures for varied attack density.…”

“…The communication is performed in extreme conditions including congestion density 0 to 10%, and varied attack types (blackhole, grayhole, and DDOS). After setting up the scenario, the communication is performed using AODV, SAODV, PSAODV, 39 PDS-AODV, 40 NCV-AODV, 17 iNCV-AODV, 17 DFMCRP, 4 FBSSR, 21 17 included the situation that the new nodes are malicious at an early stage, but later they can behave as malicious. Distributed fuzzy multicast routing protocol (DFMCRP) 4 used a fuzzy reference engine to handle network uncertainty, unstable links, and restricted resource problem.…”