Unmanned aerial vehicles likely know as drones have become the most optimistic solution with a bundle of applications in the field of monitoring environment, transportation, media live streaming, and military operations. The autopilot UAV system is normally used in exigent operations to acquire critical information. The basic UAV system consists of three major components which include aerial vehicles contain some sensors and actuators, ground control stations, and communication channels. UAV systems are vulnerable to different security threats due to their deployment in serval crucial domains. There exist numerous attack techniques includes, GPS spoofing, deauthentication, denial of services, injecting false information, damaging UAV sensors, Key loggers, which can be unfavorable for our security objectives. Over the last decade, their innumerable defense mechanisms are proposed to mitigate security risks from these kinds of attacks. In this review, we will discuss major components of UAV, prime vulnerabilities for cyber-attacks, and their defense solutions.
The huge growth and popularity of social media networks have created unprecedented research opportunities. Finding the affiliation networks and shared interest of user groups within the social network are important and well-studied problems. Graph algorithms provide a measure to characterize the social network structure. Bipartite graphs can be used as a representative model of these problems. The solution depends on efficient discovery of geodesic distance between any two random nodes. To this end, two algorithms are studied and parallelized for comparative performance analysis. In this paper we present the formulation of both algorithms on Graphic Processing Unit platform. The performance is compared on random-generated social network data-sets.
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