# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Results
#### 5.2.1 Single Attacker  
Table 4 shows all simulation results in a single attacker case. All performance evaluation metrics in the first column are introduced in Section 5.1.4. When _DCTs_ of both trust-based routings (beta GRP and our hybrid GRP) are found within the maximum simulation time (three hours), for comparison, we show the value of each metric measured  
Figure 5: A random WSN topology in a 1 km \(\times\) 1 km area. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Setting** \\ \hline \multirow{4}{*}{**General**} & Terrain dimension & 1 km \(\times\) 1 km \\ \cline{2-3}  & Number of nodes & 50 \\ \cline{2-3}  & Topology & Random \\ \cline{2-3}  & Max. simulation time & 3 h (10,800 s) \\ \cline{2-3}  & Number of runs & 10 \\ \cline{2-3}  & Base routing algorithm & GRP \\ \hline \multirow{2}{*}{**Sensor**} & Transmission power & 0.001 W (OPNET default) \\ \cline{2-3}  & Max. retransmissions & 7 (OPNET default) \\ \hline \multirow{4}{*}{**Data packet**} & Start time–Stop time & 100 s–end of simulation \\ \cline{2-3}  & Destination & Base station \\ \cline{2-3}  & Packet arrival interval & Every 10 s \\ \cline{2-3}  & Packet size & 1024 bits \\ \hline \multirow{4}{*}{**Trust model**} & Type & None, Beta, and Hybrid \\ \cline{2-3}  & Initial trust value & 0.99 \\ \cline{2-3}  & Trust threshold (TH) & 0.6, 0.7, and 0.8 \\ \hline \multirow{4}{*}{**Attack model**} & Number of attackers & Single attacker and multiple attackers (3, 5, 8) \\ \cline{2-3}  & Attack type & Blackhole (attack rate 100\%) and grayhole (attack rate 20–80\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation setup parameters. at the slower \(DCT\). When any trust-based routing algorithms fail to find \(DCT\) within the maximum simulation time, we show all metrics measured at the maximum simulation time. The pure GRP is compared to see how seriously the attacker degrades the network performance when we do not use any trust model. We explain the results in detail in terms of detection speed, detection accuracy, routing reliability, and energy efficiency in turn. (1) _Detection speed and the damage to the network_  
To compare how _quickly_ each trust model detects the inside packet drop attacker, we first examine the \(DCT\) of each routing algorithm as shown in Table 4 and Figure 6. When \(DCT\) is not found within three hours, we show \(DR\) instead; we will explain \(DR\) in detail in (2) _detection accuracy_. In general, when \(TH\) is fixed and as the attack rate grows, \(DCT\) decreases because the attacker drops more packets, its trust value decreases faster, and thus it is detected quicker by trust models. When the attack rate is fixed, as \(TH\) grows, \(DCT\) decreases because a trust model with a higher \(TH\) detects the attacker faster than the same trust model with a lower \(TH\). The results show the following:  
First, our hybrid GRP can detect an inside packet drop attacker much faster than the beta GRP under various attack models, regardless of _TH_. When both trust models detect the attacker, our hybrid trust model can detect it at least 2.7 times and at most 4.7 times faster than the beta trust model. We found that when the attack rate is set to be \(100\times(1-TH)\), the beta trust model fails in detecting the attacker (i.e., _DCT_ was not found) but our trust model can detect it. However, when the attack rate is set to be too low, both trust models failed in detecting the attacker. Second, detection speed is closely related to the damage to the network caused by the attackers. The early detection capability of our hybrid trust model based on consecutive drops significantly reduces the damage to the network due to attacks compared to that of the beta trust model. In the case of the pure GRP, \(N_{A}\) continuously grows as the simulation proceeds since it does not have a trust mechanism and thus cannot defend itself against the attacker.