S Ramakrishna

Work place: Department of Computer Science, Sri Venkateswara University, Tirupati-57501, Andhra Pradesh, India



Research Interests: Data Mining, Computer Networks


Dr. S. Rama Krishna is a Professor, Department of Computer Science and Applications in Sri Venkateswara University. He has guided about 23 Ph.D. students and 22 M.Phil. Students so far. His areas of interests are Fluid Dynamics, Computer Networks and Data Mining.

Author Articles
A Node Confident based IDS to Avoid Packet Drop Attacks for Wireless Sensor Network

By Kareti Madhava Rao S Ramakrishna

DOI: https://doi.org/10.5815/ijcnis.2021.06.04, Pub. Date: 8 Dec. 2021

Because of the great characteristics of Wireless Sensor Networks like easier to use and less cost of deployment, they have attracted the researchers to conduct the investigations and received the importance in various civilian and military applications. A number of security attacks have been involved due to the lack of centralized management in these networks. The packet drop attack is one of the attacks and it has a compromised node which drops the malicious packets. In WSNs, different techniques have been implemented to identify the packet drop attack but none of them provides the feasibility to stop or isolate their occurrence in the future. In recent times, the reputation systems provide the way to identify the trustworthy nodes for data forwarding. But the lack of data classification in the reputation systems affects the false positive rate. In this paper, a novel CONFIDENT SCORE based BAYESIAN FILTER NODE MONITORING AGENT (CFS-BFNMA) mechanism is introduced to identify & avoid the packet drop nodes and also to monitor the node behaviours to improve the false positive rate. The final CFS of a node is estimated based on the node past and threshold CFS values. The node monitoring agents (BFNMA) constantly monitors the forwarding behaviour of the nodes and assigns CFS based on the successful forwards. The NMA saves the copy of the data packets in their buffers before forwarding to the neighbour nodes to compare them. Also, this BFNMA analyses the traffic pattern of every round of transmission to improve the false positive rate. By comparing with other conventional security algorithms, the proposed mechanism has been improved the network security & false positive rate drastically based on the simulation results.

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