# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Results
#### Detection accuracy
Our hybrid GRP has slightly higher _FAR_ than that of the beta GRP since our hybrid trust model gives a larger penalty to a node generating consecutive drops than the beta trust model. In practice, packets can be dropped consecutively due to some network problems as well as attacks. Therefore, this result was expected. In fact, as we will discuss below, this small _FAR_ does not degrade the packet delivery performance of our hybrid GRP but helps to improve its packet delivery performance. Based on this result, we consider this small _FAR_ acceptable to obtain high detection accuracy. (3) _Routing reliability_  
The final goal of a routing algorithm in a WSN is to deliver data packets from source nodes to the BS. Therefore, a good trust-based routing algorithm should be able to deliver packets to the destination successfully, even under insider packet drop attacks. We examine the routing reliability of each routing based on _PDR_. Recall that a single attacker is deployed  
Figure 8: Detection rate DR (TH = 0.7). (a) Attack rate = 100%. (b) Attack rate = 60%. (c) Attack rate = 30%. near the BS to receive a significant number of data packets from many nodes throughout the network. As shown in Table 4, our hybrid GRP has the highest \(PDR\) among the three routing algorithms and can deliver at least more than 89% of packets to the BS in all simulation setups. This result shows that our hybrid GRP can route packets very reliably even under various packet drop attacks. Specifically, our hybrid GRP improves the \(PDR\) of the pure GRP by at most 19% and the PDR of the beta GRP by at most 10% (when the attack rate is 100% and a \(TH\) of 0.6 is used, as shown in Figure 9a). Since \(PDR\) is determined by two metrics, \(N_{A}\) and \(N_{NA}\), we examine them in turn. First, as we explained in (1) _detection speed_, our hybrid GRP has the smallest \(N_{A}\) among the three routing algorithms, as shown in Figure 7, because our hybrid GRP detects the attacker quickly based on consecutive drops and finds a new path to the BS through a trustworthy node. The beta GRP has a larger \(N_{A}\) than our hybrid GRP because it has a slower detection speed than our hybrid GRP. Since the pure GRP does not use any trust model, it experiences the most significant amount of packet loss by the attacker. Second, our hybrid GRP has the least \(N_{NA}\) among the three routing algorithms. Packets can be naturally dropped in a WSN for of many reasons, such as collision and congestion. The OPNET simulates channel quality problems, such as collision and congestion. As shown in Table 4, our hybrid GRP and the beta GRP have less \(N_{NA}\) than that of the pure GRP. This is because a trust-based routing algorithm avoids bad communication links by classifying normal nodes (non-attackers) with many transmission errors as untrustworthy nodes. This causes both the beta GRP and our hybrid GRP to generate a small \(FAR\), as previously discussed, but it improves the \(PDR\) of a trust-based routing algorithm. For the same reason as discussed in (1) _detection speed_, when normal nodes in bad links generate many consecutive drops, our hybrid GRP penalizes them based on the number of consecutive drops and thus quickly avoids them. As a result, our hybrid GRP has less \(N_{NA}\) than the beta GRP, as shown in Figure 10. In practice, it is often assumed that WSNs have hundreds or thousands of sensors to be tolerant to many faulty sensors, so a small \(FAR\) may not be a problem. Figure 10: The number of packets dropped due to non-attacks \(\mathrm{N_{NA}}\) (TH = 0.7). (a) Attack rate = 100%. (b) Attack rate = 60%. (c) Attack rate = 30%. Figure 9: Packet delivery rate PDR. (a) TH = 0.6. (b) TH = 0.7. (c) TH = 0.8. (4) _Energy efficiency_  
We compared the energy efficiency of each routing algorithm. The results show the following. First, the total energy consumption of each routing algorithm is as follows: \(E_{T}\) of our hybrid GRP \(>E_{T}\) of the beta. GRP \(>E_{T}\) of the pure GRP as shown in Table 4. \(E_{T}\) is closely related to \(PDR\).