Intrusion Detection Through Gradient in Digraphs

Unmanned autonomous vehicles (UAVs) driven by information obtained through sensor networks are believed to have a pivotal part in every area of life in the near future. In order to fulfil their objectives, the UAV will move through the data obtained by the associated network of sensors and of UAVs. The data includes heading to reach a destination, heading to hit a goal, the volume of the goal, and the min path to reach a destination. The accomplishment of such a task will be highly dependent upon the precision and legitimacy of any such data obtained on the ground. The significance of the data invites the adversary to interrupt the data collection network. The most shocking way to interrupt a data collection network is to insert malicious node(s) in the networks to contaminate the data and render it worthless. This sort of attacks upon networks are serious and quiet. Moreover, such attacks are straightforward to unveil as they need nominal resources from the adversary’s part. Gradient, level surfaces, and scalar fields are known concepts in thermodynamics and physics. This chapter extends these concepts to the networks. By using the extended concepts of gradients, level surfaces, and scalar point functions, this chapter provides a novel linear time algorithm to find the min path to a malicious node within a network.