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In this paper, we present a large-scale study of global trends in software download events, with an analysis of both benign and malicious downloads, and a categorization of events for which no ground truth is currently available. Our measurement study is based on a unique, real-world dataset collected at Trend Micro containing more than 3 million in-the-wild web-based software download events involving...
In this paper, we propose Segugio, a novel defense system that allows for efficiently tracking the occurrence of new malware-control domain names in very large ISP networks. Segugio passively monitors the DNS traffic to build a machine-domain bipartite graph representing who is querying what. After labelling nodes in this query behavior graph that are known to be either benign or malware-related,...
In this paper we present PeerRush, a novel system for the identification of unwanted P2P traffic. Unlike most previous work, PeerRush goes beyond P2P traffic detection, and can accurately categorize the detected P2P traffic and attribute it to specific P2P applications, including malicious applications such as P2P botnets. PeerRush achieves these results without the need of deep packet inspection,...
Notwithstanding the fact that data mining and particularly sequence mining have come to the help of database proprietors and experts in variety of fields by extracting the hidden knowledge in their raw data, they have provided an opportunity for adversaries to take advantage from this sensitive knowledge. So this work addresses the problem of sequential pattern hiding, and proposes an efficient and...
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