Today, unmanned aerial vehicles (UAVs), also known as drones, have become very popular in military applications, commercial applications, and academic research. Flying ad hoc network (FANET) is a new type of ad hoc network, which groups small drones into an ad hoc form. These networks have unique characteristics, including moving in a 3D space, high mobility, frequent topological changes, limited resources, low density of nodes, and so on, which impose various challenges when designing a proper and efficient routing scheme. In this paper, we present a fuzzy logic-based routing scheme for flying ad hoc networks. The proposed routing scheme has two phases: route discovery phase and route maintenance phase. In the first phase, we propose a technique for calculating the score of each node in the network to prevent the broadcast storm problem and control the flood of the control messages, which have been broadcast to discover a new route in the network. This score is calculated based on various parameters such as movement direction, residual energy of nodes, link quality, and node stability. Moreover, in the route selection process, we design a fuzzy system to select routes with more fitness, less delay, and fewer hops for data transfer. The second phase includes two steps: preventing route failure in order to detect and modify paths at the failure threshold, and reconstructing failed routes in order to recognize and quickly replace these routes. Finally, the proposed routing scheme is implemented in NS2 to evaluate its performance and determine its efficiency. The simulation results are compared with three routing methods, namely ECaD, LEPR, and AODV. These results show that the proposed routing method outperforms other routing schemes in terms of end to end delay, packet delivery rate, route stability, and energy consumption. However, it has slightly increased the routing overhead.

Spyware is the most complex, obfuscated and targeted class of malware, which has grown dramatically in recent years. Spyware is designed for secret, long-term and persistent missions. The present paper provides a novel method for detection, tracking and confronting the stealth and obfuscated spyware and ransomware including keyloggers, screen recorders, and blockers. The proposed method of this paper is based on a dynamic behavioral analysis through deep and transparent hooking of kernel-level routines. We used Linear Regression, JRIP and J48 decision tree algorithms as a classifier to recognize three classes of malware. This paper presents the main architectural plan of an anti-spyware application to track spyware footprints in order to detect and force terminate running processes, eliminate executable files and restrict network communications. The Efficiency of the proposed method was evaluated from the viewpoint of accuracy in detecting real-world samples of spyware by ROC curve analysis and from the viewpoint of success rate to confront effectively with active spyware. Our proposed method was able recognize spyware with an accuracy of about 93% and an error rate near 7%. In addition, the proposed system can disinfect an operating system from infection by spyware with a hit rate of about 82%.