## 5 Performance Evaluation
### Simulation Results: Different Parameters' Effects on Replication Attack Detection
#### 5.2.1 Detection Accuracy  
The DT method evaluates key factors recognized as true positive and false positive. The effectively detected clone is identified as true positive, however, unsuccessfully rate is known as the false positive. The simulation execution of the proposed danger theory at multiple stage is illustrated in Figure 6. In the figure, the high accumulation rate of clone identification is at a distinct phase. It can also be observed that at each stage, the true positive rises. As the proposed method consists of multiple stages in mean to detect the clones, the first stage is designed for verification, in addition of the identification of the anomaly at cluster to establish the danger area (i.e., DZ). The second stage is towards mitigating the clone threat by contrasting the battery level and encountering the random number check while the third stage is to prevent the MWSN from dangerous attacks by transmitting messages through BS to others. To categorize the sheltered network from clone attacks, there is a framework analysis to improve the true positive (i.e., successfully replica detection) and decrease the false positive rate (i.e., wrongly detected replicas). Therefore, the proposed DT (multilevel detection) method has an impact on the development in clones' threats detection execution.  
It can be noticed that the first stage detection screens minor clone detection rate (i.e., in term of true positive), below 0.6 besides vast false positive rate, higher than 0.5. However, in the second and third stage, it validates the equivalent behavior of true positive rate that is approximately 0.95. Besides, the false positive percentage in the third stage is reduced to zero as likened with the first and second stage. The multiple stages simplify the clone detection application in terms of true positive. In relation to clone threats, it can be observed that a multilevel clone revealing is additionally proficient and competent. Moreover, the multi-level clone detection also confirms the minimum rate of false positive and negative. In the figure, it can be observed that a fewer percentage of 0.1% is for false negative.  
The cause of this outcome is that there is a probability that the CH does not encounter again through mobile node first stage of clone detection, which will increase the rate for false negative. Moreover, due to the mobility features in nodes, in the detection period, there is the risk that some may travel outer side of the specific area (i.e., cluster). Since the clone exists later than the real mobile node, it is obvious that the battery level of the clone must be higher than the genuine mobile node. Additionally, in the least time interval, the DT can identify the clone or fake mobile node in the exact cluster area. In this way, an attacker cannot take advantage of spreading clones in MWSN. The execution outcomes display that the DT technique is fast, competent, secure and intelligent for clone detection. A comparison with existing (centralized and distributed) approaches is discussed in Section 4. The DT approach is restricted with static battery types for mobile nodes. In the situation of rechargeable battery or solar cells, the clone recognition may be very tough and challenging with respect to their battery level.