Chang, Jian
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Publication Spam Mitigation Using Spatio-Temporal Reputations From Blacklist History(2010-12-01) West, Andrew G.; Aviv, Adam J.; Chang, Jian; Lee, InsupIP blacklists are a spam filtering tool employed by a large number of email providers. Centrally maintained and well regarded, blacklists can filter 80+% of spam without having to perform computationally expensive content-based filtering. However, spammers can vary which hosts send spam (often in intelligent ways), and as a result, some percentage of spamming IPs are not actively listed on any blacklist. Blacklists also provide a previously untapped resource of rich historical information. Leveraging this history in combination with spatial reasoning, this paper presents a novel reputation model (PreSTA), designed to aid in spam classification. In simulation on arriving email at a large university mail system, PreSTA is capable of classifying up to 50% of spam not identified by blacklists alone, and 93% of spam on average (when used in combination with blacklists). Further, the system is consistent in maintaining this blockage-rate even during periods of decreased blacklist performance. PreSTA is scalable and can classify over 500,000 emails an hour. Such a system can be implemented as a complementary blacklist service and used as a first-level filter or prioritization mechanism on an email server.Publication A Trust Model for Vehicular Network-Based Incident Reports(2013-06-02) Chang, Jian; Lee, Insup; Liao, Cong; Venkatasubramanian, Krishna K.Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks are ephemeral, short-duration wireless networks that have the potential to improve the overall driving experience through the exchange of information between vehicles. V2V and V2I networks operate primarily by distributing real-time incident reports regarding potential traffic problems such as traffic jams, accidents, bad roads and so on to other vehicles in their vicinity over a multi-hop network. However, given the presence of malicious entities, blindly trusting such incident reports (even the one received through a cryptographically secure channel) can lead to undesirable consequences. In this paper, we propose an approach to determine the likelihood of the accuracy of V2V incident reports based on the trustworthiness of the report originator and those vehicles that forward it. The proposed approach takes advantage of existing road-side units (RSU) based V2I communication infrastructure deployed and managed by central traffic authorities, which can be used to collect vehicle behavior information in a crowd-sourcedfashion for constructing a more comprehensive view of vehicle trustworthiness. For validating our scheme, we implemented a V2V/V2I trust simulator by extending an existing V2V simulator with trust management capabilities. Preliminary analysis of the model shows promising results. By combining our trust modeling technique with a threshold-based decision strategy, we observed on average 85% accuracy.