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A. K. Trivedi, R. Kapoor, R. Arora, Sudip Sanyal and Sugata Sanyal, “RISM—Reputation Based Intrusion Detection System for Mobile Ad hoc Networks,” Third International Conference on Computers and Devices for Communications, Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, 18-20 December 2006, pp. 234-237.

has been cited by the following article:

• TITLE: A Heuristic Reputation Based System to Detect Spam Activities in a Social Networking Platform, HRSSSNP

  AUTHORS: Manoj Rameshchandra Thakur, Sugata Sanyal

  KEYWORDS: Spam; Social Graph; Collaborative Filtering; Weighted Graph; Localized Data-Set; Trust Level

  JOURNAL NAME: Social Networking, Vol.2 No.1, January 30, 2013

  ABSTRACT: The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.