Black Hole Attack Detection in Wireless Sensor Networks Using Hybrid Optimization Algorithm

One of the types of denial of service attacks that target wireless sensor networks (WSNs) are black hole (BH) attacks, which are widely targeted at this form of network today. In this attack, data are blocked in the network, malware is installed on a group of nodes in the network, and ultimately, the data packet is blocked before reaching its destination. In other words, data cannot be transmitted in the vicinity of BH nodes. Because of the nature of WSNs that are readily available, these networks cannot be optimized without compromising energy consumption, and this problem becomes a non-deterministic polynomial-time hard problem. Despite some models that have been presented to resolve this issue, most of them have not had sufficient performance in dealing with BH attacks. Thus, we have presented a new and powerful model based on the hybrid meta-heuristic algorithm depending on the sine and cosine algorithm (SCA) and the whale optimization algorithm (WOA). This algorithm has been combined in such a way that the increase in computational load has been prevented, in addition, two algorithms are included in one algorithm in this case, using the positive features of these two algorithms, it escapes from the local optimal trap in the solution of the algorithm and also benefits from a very good convergence. Because the new production solutions have a good diversity and the intensification component also has a good performance the main goal of this article is to present a new type of robust optimization algorithm for BH detection in WSN. This model has been tested and evaluated using a network and compared with three other meta-heuristic algorithms to make a fair comparison. The results obtained from the proposed model indicate a high-quality performance of this model in detecting BH attacks. The proposed model can detect more than 85% of the BH nodes and the total warning rate in the proposed model is equal to 0.866.