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Titlebook: Detection of Intrusions and Malware, and Vulnerability Assessment; 15th International C Cristiano Giuffrida,Sébastien Bardin,Gregory Blanc

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楼主: 热爱
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M,: Compact Full-Trace Malware Recording for Retrospective Deep Analysiserstand a sample’s behavior and effect on a system. However, current systems face two limitations: first, for performance reasons, the amount of data they can collect is limited (typically to system call traces and memory snapshots). Second, they lack the ability to perform .—that is, to later extra
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M,S,: Time- and Space-Efficient Storage of Malware Sandbox Memory Dumps system state (i.e., a sandbox snapshot) to analyze malware. Therefore, memory dumps taken after malware execution inside the same sandbox are substantially similar to each other, which we can use to only store the differences introduced by the malware itself. Technically, we compare the pages of th
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Honey, I Shrunk Your App Security: The State of Android App Hardeningation. Android, however, suffers from a fragmented landscape of devices and versions, which leaves many devices unpatched by their manufacturers. This security gap has created a vital market of commercial solutions for . (RASP) to harden apps and ensure their integrity even on compromised devices. I
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B,A,: Scalable and Efficient Detection of Vulnerabilities in Firmware Images of Intelligent Electron firmware might greatly affect the security and functionality of the smart grid. Although general-purpose techniques exist for vulnerability detection in firmware, they usually cannot meet the specific needs, e.g., they lack the domain knowledge specific to IED vulnerabilities, and they are often no
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On the Weaknesses of Function Table Randomizationer is not able to find the position of the code she wants to reuse, hence thwarting successful attacks. Current state-of-the-art defenses achieve this by employing concepts such as execute-only memory combined with booby traps..In this paper, we show that an attacker is able to abuse symbol metadata
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FraudBuster: Temporal Analysis and Detection of Advanced Financial Fraudse of fraud consists in repeatedly stealing small amounts of funds over time. A reliable detection of these fraud schemes requires an accurate modeling of the user’s spending pattern over time. In this paper, we propose ., a framework that exploits the end user’s recurrent vs. non-recurrent spending
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