Faculty Publications
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Publications by NITK Faculty
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Item Reputation-based cross-layer intrusion detection system for wormhole attacks in wireless mesh networks(John Wiley and Sons Inc 410 Park Avenue, 15th Floor, 287 pmb New York NY 10022, 2014) Karri, G.R.; Santhi Thilagam, P.Wireless mesh networks provide long-distance wireless network connectivity over heterogeneous devices for greater scalability and availability. However, protecting legitimate long-distance wireless links from wormhole attacks is an important yet challenging security issue in wireless mesh networks. In this paper, we propose a reputation-based cross-layer intrusion detection system to effectively detect various wormhole attacks. The proposed system analyses the behaviours of the routing paths in wireless mesh networks to correctly isolate the malicious wormhole paths from legitimate long-distance wireless links. It uses reputation and cross-layer parameters for comprehensive ability to isolate the wormhole attacks in routing paths. This isolation ensures full utilisation of legitimate long-distance wireless links in wireless mesh networks, which is not possible with the existing wormhole attack detection approaches. Experimental results show that the proposed system increases the detection rate, decreases the false alarm rate, and secures legitimate long-distance wireless links in wireless mesh networks. © 2014 John Wiley & Sons, Ltd.Item Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers(Elsevier Ltd, 2019) Srinivasa, S.; Santhi Thilagam, P.In the current era of internet, information related to crime is scattered across many sources namely news media, social networks, blogs, and video repositories, etc. Crime reports published in online newspapers are often considered as reliable compared to crowdsourced data like social media and contain crime information not only in the form of unstructured text but also in the form of images. Given the volume and availability of crime-related information present in online newspapers, gathering and integrating crime entities from multiple modalities and representing them as a knowledge base in machine-readable form will be useful for any law enforcement agencies to analyze and prevent criminal activities. Extant research works to generate the crime knowledge base, does not address extraction of all non-redundant entities from text and image data present in multiple newspapers. Hence, this work proposes Crime Base, an entity relationship based system to extract and integrate crime related text and image data from online newspapers with a focus towards reducing duplicity and loss of information in the knowledge base. The proposed system uses a rule-based approach to extract the entities from text and image captions. The entities extracted from text data are correlated using contextual as-well-as semantic similarity measures and image entities are correlated using low-level and high-level image features. The proposed system also presents an integrated view of these entities and their relations in the form of a knowledge base using OWL. The system is tested for a collection of crime related articles from popular Indian online newspapers. © 2019 Elsevier Ltd