In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman test to compare Genetic Algorithm (GA) and Tabu Search (TS). We found out that GA and TS have difference in their performance. Then, we used the implemented systems WMN-GA and WMN-TS to evaluate and compare the performance of the system for different distributions of mesh clients in terms of giant component and covered mesh clients. The simulation results shows that for big radius of communication distances WMN-GA performs better than WMN-TS for Uniform, Normal and Weibull distributions of mesh clients. For Exponential distribution WMN-TS performs better than WMN-GA for all radius of communication distances.

Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect the intrusion in Tor networks. In this paper, we present the application of Deep Recurrent Neural Networks (DRNNs) for prediction of user behavior in Tor networks. We constructed a Tor server and a Deep Web browser (Tor client) in our laboratory. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and thenused the DRNNs to make the prediction. The simulation results show that our simulation system has a good prediction of user behavior in Tor networks.