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Advancements in Network-based Intrusion Detection and Prevention in Emerging Environments

This comprehensive study by Yan Chen explores network-based intrusion detection and prevention systems in high-speed data center environments, Web 2.0 platforms, and social networks. It highlights significant findings from large-scale experiments on Facebook, which analyzed spam campaigns using 3.5 million user profiles and 187 million wall posts. The research has garnered attention from major publications like the Wall Street Journal and MIT Technology Review, emphasizing its importance in understanding cybersecurity challenges in modern digital landscapes.

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Advancements in Network-based Intrusion Detection and Prevention in Emerging Environments

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  1. Lab for Internet and Security TechnologyYan Chen Network-based Intrusion Detection and Prevention in Challenging and Emerging Environments: High-speed Data Center (SIGCOMM 2010) Web 2.0 (NDSS 2010) and Social Networks (SIGCOMM IMC 2010) Detecting and Characterizing Social Spam Campaigns Conduct the largest scale experiment on Facebook to confirm spam campaigns: 3.5M user profiles, 187M wall posts. Featured in Wall Street Journal, MIT Technology Review, ACM Tech News, and the McCormick Magazine

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