Titlebook: Research in Attacks, Intrusions, and Defenses; 16th International S Salvatore J. Stolfo,Angelos Stavrou,Charles V. Wri Conference proceedin

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Leisureliness

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SILVER: Fine-Grained and Transparent Protection Domain Primitives in Commodity OS Kernelions in terms of security, granularity and flexibility, primarily due to the absence of secure resource management and communication methods. This paper presents SILVER, a framework that offers transparent protection domain primitives to achieve fine-grained access control and secure communication b

deadlock

API Chaser: Anti-analysis Resistant Malware Analyzer in many malware countermeasures as their base. However, malware authors are now aware of the situation and they develop malware using several anti-analysis techniques to evade API monitoring. In this paper, we present our design and implementation of an API monitoring system, ., which is resistant

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FIRMA: Malware Clustering and Network Signature Generation with Mixed Network Behaviorsnto families and generate behavioral signatures for their detection. Among these, network traffic is a powerful behavioral signature and network signatures are widely used by network administrators. In this paper we present FIRMA, a tool that given a large pool of network traffic obtained by executi

Affiliation

歌剧等

Detecting Traditional Packers, Decisivelyse more realistic assumptions about machine and attacker resources. While seminal results such as [1–5] remain true for Turing machines, we show under more realistic assumptions, important tasks are decidable instead of undecidable. Specifically, we show that detecting traditional malware unpacking

Peak-Bone-Mass

Side-Channel Attacks on the Yubikey 2 One-Time Password Generator the credentials, e.g., by guessing passwords using a dictionary, by eavesdropping on network traffic, or by installing malware on the system of the target user. To overcome this problem, numerous solutions incorporating a second factor in the authentication process have been proposed. A particularl

CURB

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Behavior Decomposition: Aspect-Level Browser Extension Clustering and Its Security Implications or safety scrutiny, which keeps users from differentiating benign extensions from malicious ones. In this paper, we propose an aspect-level behavior clustering approach to enhancing the safety management of extensions. We decompose an extension’s runtime behavior into several pieces, denoted as AEB