Mitigating On-Off Attacks in the Internet of Things Using a Distributed Trust Management Scheme

Abstract

In the Internet of Things (IoT), physical objects are able to provide or require determined services. The purpose of this work is to identify malicious behavior of nodes and prevent possible On-Off attacks to a multiservice IoT. The proposed trust management model uses direct information generated from direct communication with the nodes to evaluate trust between nodes. This distributed approach allows nodes to be completely autonomous in making decisions about the behavior of other nodes. We perform network simulations using Contiki-OS to analyze the performance of the proposed trust model. Simulation results show effectiveness against On-Off attacks and also a good performance to recognize malicious nodes in the network.

T‐D2D: A trust model for service offloading in device‐to‐device communication

“…The centralized trust models suffer from their reliance on higher level entity, which may be not available temporarily or permanently everywhere. To overcome this issue, other trust models perform both trust evaluation and provider selection in a distributed manner . In T‐D2D, all functions are executed distributively inside each IoT node, while recommendations are exchanged between different IoT nodes.…”

“…To overcome this issue, other works proposed trust models performing completely in a distributed manner. In this regard, Mendoza et al proposed a trust model only based on direct trust for a multiservice IoT environment . Each node gains its neighborhood via announcement packets received from neighbor nodes and rates its interactions with each of them based on a fixed reward or punishment value.…”

“…In this regard, Mendoza et al proposed a trust model only based on direct trust for a multiservice IoT environment. 12,38 Each node gains its neighborhood via announcement packets received from neighbor nodes and rates its interactions with each of them based on a fixed reward or punishment value. To discourage bad conducts, the punishment for a node when it does not provide a service is twice the reward for properly providing a service.…”

“…Although a number of works has already proposed trust models to face with on‐off or bad‐mouthing attacks, these works are not capable to detect light or dispersed on‐off conducts especially when dishonest recommenders exist in the environment . To be more explicit, most of the existing works with emphasis on on‐off attacks attempt to increase the impact of misbehavior in trust calculation, avoiding an attacker to compensate its misbehavior with normal conducts . However, these works are not capable to detect light attacks with relatively more significant normal conducts than misbehavior.…”

“…6,[9][10][11] To be more explicit, most of the existing works with emphasis on on-off attacks attempt to increase the impact of misbehavior in trust calculation, avoiding an attacker to compensate its misbehavior with normal conducts. 12,13 However, these works are not capable to detect light attacks with relatively more significant normal conducts than misbehavior. With regard to bad-mouthing attacks, some works rely only on recommendations received from recommenders with a high trust level.…”