# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 2 Background  
### Insider Packet Drop Attacks and Countermeasures  
Inside attackers can launch various types of attacks actively (such as modification, packet drop, or misrouting) or passively (such as eavesdropping). Among these, insider packet drop attacks are the most challenging to defend because they cannot be prevented by authentication and authorization [1; 3], and hence they can potentially cause significant degradation to the network performance. Below are three representative types of insider packet drop attacks [9; 14; 17]:1. Blackhole attack: This attacker will drop all the received packets. It causes the most serious damage to the network among all types of packet drop attacks. However, the monitoring neighbors can easily capture this attacker as it consistently drops all packets.
2. On-off attack: This attacker will drop all received packets when it is in attacking mode and forward all the received packets when attack is off. It repeats this drop-forward pattern periodically. This attacker can be suspicious to its monitoring neighbors during its attack period when it acts like blackhole attacker, and thus can be detected easily when the attack period is long or the on-off pattern is discovered.
3. Grayhole attack (selective forwarding attack): This attacker will drop some of the received packets, either randomly or selectively. For example, each time the attacker receives a packet, it may decide whether to drop it according to a predetermined attack rate (or drop rate). Another example is that the attacker may only drop packets of a specific type or those generated by a specific source node.  
As described above, it is easy to catch blackhole attackers and there are also some relatively effective means to detect on-off attackers. The grayhole attackers remain the most difficult to detect because most of the time their behaviors are very similar to those of the normal nodes. Existing mitigation approaches against grayhole attackers are either detection-based or avoidance-based approaches.  
Most of the detection-based approaches focus on random selective forwarding attacks [20; 21; 22; 23]. For example, in the multi-hop acknowledgment scheme [20], each node in the forwarding path is responsible for detecting attackers. Specifically, some randomly chosen nodes (called ACK nodes) will report ACKs back to the source node (hop by hop) using the same but reversed routing path when they receive a packet. However, this approach has several problems. First, it is unclear how to locate the exact attacker. Second, their detection scheme depends on other nodes' observations, and thus is vulnerable to false accusation from malicious neighbors. The trust mechanisms with watchdogs, on which we will elaborate in the next section, solve these two problems by monitoring whether the next node in the routing path forwards the packets or not [4; 8; 11; 12].  
Instead of detecting the attackers, the avoidance approaches focus on how to deliver the packets successfully with the existence of the attackers. A popular way to achieve this is to use multipath routing paths [3; 24]. In [3], the authors pointed out that k disjoint multipath routing can completely defend against selective forwarding attacks with no more than \(k-1\) compromised nodes. However, the multipath routing approach has a couple of drawbacks [20]. First, communication overhead increases significantly as the number of paths increases, and thus it may lead to more collision and interference. As a result, the packet delivery performance of a routing can be dramatically degraded. Second, since this approach cannot catch and discard the attackers and this approach can be compromised if an adversary locates at least one attacker in each routing path. Similarly, a multiple data flow scheme using multiple disjoint topologies was introduced in [24].