Titlebook: Detection of Intrusions and Malware, and Vulnerability Assessment; 12th International C Magnus Almgren,Vincenzo Gulisano,Federico Maggi Con

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Robust and Effective Malware Detection Through Quantitative Data Flow Graph Metricsverage detection rate of 98.01 % and a false positive rate of 0.48 %. Moreover, we show that our approach is able to detect new malware (i.e. samples from malware families not included in the training set) and that the consideration of quantities in itself significantly improves detection precision.

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Fine-Grained Control-Flow Integrity Through Binary Hardening-grained CFI checks using information from a trusted dynamic loader. A shadow stack enforces precise integrity for function returns. Our prototype implementation shows that Lockdown results in low performance overhead and a security analysis discusses any remaining gadgets.

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Capturing DDoS Attack Dynamics Behind the Scenesattack strategies, if any. Our study is based on 50,704 different Internet DDoS attacks. Our results indicate that attackers deliberately schedule their controlled bots in a dynamic fashion, and such dynamics can be well captured by statistical distributions.