The Hidden Threat: Analyzing Trust Manipulation Attacks in Trust Management Systems for Vehicular Networks

When it comes to securing networks, Trust Management Systems (TMS) are still an important and significant solution in providing a secure network by identifying and mitigating malicious behaviors. However, these systems remain vulnerable to sophisticated or smarter Trust Manipulation Attacks (TMA), where there are adversaries that exploit trust evaluation mechanisms to evade detection. One of the most security-critical network environments are vehicular networks, including Vehicular Ad Hoc Networks (VANETs) and the Internet of Vehicles (IoV), where trust is very major. Without proper security, these networks become vulnerable to malicious attacks that can disrupt traffic coordination, spread false information, and ultimately compromise road safety. In this paper, we analyze two primary strategies used in TMA: Behavioral Switching and Sybil-Based Trust Inflation. In the first one, a malicious node alternates its behavior between a legitimate and a malicious one, make sure that its trust level always stays above the detection threshold. In the second, attackers will create multiple and a significant number of fake identities to help them collectively share trust scores and falsely establish credibility. By looking closely at these attack methods, we show how they affect systems that rely on trust to stay secure, and how they can trick common detection approaches. We also explain what these attacks mean for the stability and safety of the whole network. This work shows why it's important to build better trust systems that can spot and respond to these changing threats.