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Clustering is one important method for extending the network energy in WSNs. It mires grouping of sensor nodes into clusters and recruiting cluster Head (CHs) for all the clusters. CHs rally the data from relevant cluster’s nodes and forward the accumulated data to cluster Master. WSN are inclusive to various attacks in which Blackhole a kind of Denial of Service (DoS) attack is very crucial to encounter and defend the mechanism. The attackers elect a set of neighbor nodes in the network and reconstruct the route to downfall the received packets instead of forwarding them to the sink node, which will emerge in a situation where packets enter the blackhole area but never reaches the destination resulting in higher end-to-end delay and decline in the throughput. In this paper, We use SHA(Secure Hashing algorithm) Cluster based Futuristic Method for detecting black hole attack in WSNs. By using the proposed technique we adapts filtering technique to the attacker and prevent from the BlackHole attacker and improving the performance such as throughput, delay and packet delivery ratio.

The Vehicular Ad hoc Networks are a different type of ad hoc network, which contain communicating entities that are in motion with different velocity along with lacking in infrastructure. Hence, this work needs the establishment of more consistent end-to-end communicating pathways and efficiently transferring data. We have presented a new security-aware routing algorithm called Deep learning-based prediction. The presented scheme is more efficient and reliable against different kinds of attacks such as black hole and malicious node penetration attempts to the entire network. It basically depends upon route link error recover and shortest path by using Dijkstra’s algorithm. The aim of this scheme is to identify malicious data and black hole nodes. The simulation results of eDC-NC are compared with already existing techniques called COPE in terms of Energy consumption (EC), throughput and network lifetime (NLT). This helps to achieve better performance and filter unwanted data by applying deep learning filtering technique.

Any system or network is always susceptible to attacks. If an adversary has access to the network, they may disrupt the network in several ways. Sybil attack, selective forwarding, tampering, etc., are some of the attacks that disrupt the network. If the sensor network is employed in critical areas such as warzones or border protection systems, any attack or breach of security may prove to be fatal. Therefore, proper security mechanisms have to be integrated into the system to ensure that the data remains secure. In our project, we intend to identify and isolate the compromised nodes that selectively forwards data packets. The proposed protocol uses a reputation based system to determine the integrity of the neighboring nodes. Once a compromised node has been identified, that node is excluded from further communication after verification from base station.