Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks.

Heba Kurdi,Lina Altoaimy,Alanoud Alhussain

doi:10.3390/s21134484

Abstract

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.

Highlights

The booming number of Internet of Thing (IoT) devices is paving the way for peer-to-peer (P2P) architecture to dominate in IoT platforms so that connectivity and latency issues usually associated with centralized cloud services architecture are avoided [1]
The success rate can be calculated as the total number of good resources received by good peers divided by the total number of resources received by good peers, as in Success rate is presented in Table 1, against the percentage of malicious peers as the number of transactions increases from 500 to 3000
The results suggest a correlation between increasing the percentage of malicious peers and the success rate of good peers when Trutect is utilized

Summary

Introduction

The booming number of IoT devices is paving the way for peer-to-peer (P2P) architecture to dominate in IoT platforms so that connectivity and latency issues usually associated with centralized cloud services architecture are avoided [1]. Trust management systems monitor peers’ conduct and allow them to give feedback on their previous transactions; good and bad peers can be identified This binary classification of peers is the approach followed by most previous studies [12,21,22,23], disregarding the fact that peers vary greatly in their behavior between the two extremes. Existing trust management systems considered neither predicting the specific peer model nor identifying other members within his/her malicious group. To bridge this gap, this paper proposes. Trutect, which is a trust management system that uses the power of neural networks to detect malicious peers and identify their specific model and other group members, if any. A well-controlled evaluation framework to study the performance of a trust management system

Literature Review

System Design

Evaluation Methodology

Success Rate

Running Time

Accuracy in Predicting Malicious Model and Group Members

Conclusions