# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Categories of Attacks  
Due to the particularity of the IoV's environment and the lack of infrastructure, it is vulnerable to a large number of attacks, especially smart malicious attacks, which are often difficult to detect, because such malicious attacks switch back and forth between trusted and untrusted. Hence, we need to design a trust model with better accuracy and use various flexible and effective detection methods. Different trust models can detect different attack types, but the attack types in the system may change at any time, and the fixed trust model may not be detected; details are shown in Table 5. Designing a trust model that can detect any attack type is therefore also a challenge that we need to solve in the future. Trust-based attacks are classified as follows:  
* _Bad-mouthing attack and Good-mouthing attack_ Attackers attempt to send fake trust messages to frame legitimate nodes so that they are not detected. Hence, the purpose of this attack is to undermine the proper trust assessment and make malicious attacks hard to identify. In [41], the authors proposed a trust mechanism based on evidence combination, which can resist bad-mouthing attacks. The precision and recall were still higher than 80% under bad-mouthing attack. Compared with bad-mouthing attacks, good-mouthing attacks send positive recommendations about malicious nodes.
* _Selective Misbehavior Attack_ During a selective misbehavior attack, malicious nodes only provide false messages to some nodes, which is normal for other nodes, which will lead to inconsistent trust among different nodes that is difficult to detect. During a time-dependent attack, the behavior of nodes changes with time and is not fixed. In [41], three types of attacks were detected: bad-mouthing attacks, selective misbehavior attacks, and time-dependent attacks.
* _Time-varying Attack_ During a time-varying attack, the behavior of nodes changes with time and is not fixed. Initially, an attacker would establish itself as a legitimate node for a short period of time, gain the trust of other vehicles, and then launch an attack, sharing malicious messages and ratings with neighboring vehicles. In [47], the authors detected this kind of attack.
* _Zig-Zag Attack (On-and-off Attack)_ During a zig-zag attack, also known as the \"on-and-off\" attack, malicious nodes attack randomly. At first, these nodes are normal, and when they gain enough trust values, they launch malicious attacks, which are difficult to detect. In [52], the authors proposed an effective attack detection model, which mainly detected man-in-the-middle (MITM) attacks and zig-zag attacks as well as a combination of the two attacks.
* _Self-promoting Attack_ Unlike other types of malicious attacks, selfish attacks gain their own benefits from attacks, which indicates that there is little cooperation between vehicles. For example, in [55], the authors proposed an incentive technology to prevent such attacks.
* _Whitewashing Attack/Newcomer Attack_During a whitewashing attack, a node has a bad history before entering the system, but after re-entering the system, the node adopts a new identity in order to gain more trust and erase its dark history. In order to resist such attacks, in [31], new nodes were given a relatively low trust value, and an adaptive attenuation factor was also introduced so that the trust value of newcomers could reach a relatively high value over a long period of time.