Are your friends who they say they are?: data mining online identities
Crossroads - The Social Web
Spectrum based fraud detection in social networks
Proceedings of the 17th ACM conference on Computer and communications security
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Foundations and Trends in Information Retrieval
Node protection in weighted social networks
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Design and analysis of a social botnet
Computer Networks: The International Journal of Computer and Telecommunications Networking
A semi-supervised graph-based algorithm for detecting outliers in online-social-networks
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Community-based features for identifying spammers in online social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Three-objective subgraph mining using multiobjective evolutionary programming
Journal of Computer and System Sciences
A spectral approach to detecting subtle anomalies in graphs
Journal of Intelligent Information Systems
Graph publication when the protection algorithm is available
Data & Knowledge Engineering
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Modern communication networks are vulnerable to attackers who send unsolicited messages to innocent users, wasting network resources and user time. Some examples of such attacks are spam emails, annoying tele-marketing phone calls, viral marketing in social networks, etc. Existing techniques to identify these attacks are tailored to certain specific domains (like email spam filtering), but are not applicable to a majority of other networks. We provide a generic abstraction of such attacks, called the Random Link Attack (RLA), that can be used to describe a large class of attacks in communication networks. In an RLA, the malicious user creates a set of false identities and uses them to communicate with a large, random set of innocent users. We mine the social networking graph extracted from user interactions in the communication network to find RLAs. To the best of our knowledge, this is the first attempt to conceptualize the attack definition, applicable to a variety of communication networks. In this paper, we formally define RLA and show that the problem of finding an RLA is NP-complete. We also provide two efficient heuristics to mine subgraphs satisfying the RLA property; the first (GREEDY) is based on greedy set-expansion, and the second (TRWALK) on randomized graph traversal. Our experiments with a real-life data set demonstrate the effectiveness of these algorithms.