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Present-day malware analysis techniques use both virtualized and emulated environments to analyze malware. The reason is that such environments provide isolation and system restoring capabilities, which facilitate automated analysis of malware samples. However, there exists a class of malware, called VM-aware malware, which is capable of detecting such(More)
To protect Android users, researchers have been analyzing unknown, potentially-malicious applications by using systems based on emulators, such as the Google's Bouncer and Andrubis. Emulators are the go-to choice because of their convenience: they can scale horizontally over multiple hosts, and can be reverted to a known, clean state in a matter of seconds.(More)
The volume and the sophistication of malware are continuously increasing and evolving. Automated dynamic malware analysis is a widely-adopted approach for detecting malicious software. However, many recent mal-ware samples try to evade detection by identifying the presence of the analysis environment itself, and refraining from performing malicious actions.(More)
In this work, we propose SigMal, a fast and precise malware detection framework based on signal processing techniques. SigMal is designed to operate with systems that process large amounts of binary samples. It has been observed that many samples received by such systems are variants of previously-seen malware, and they retain some similarity at the binary(More)
Security competitions have become a popular way to foster security education by creating a competitive environment in which participants go beyond the effort usually required in traditional security courses. Live security competitions (also called " Capture The Flag, " or CTF competitions) are particularly well-suited to support hands-on experience, as they(More)
Automated dynamic malware analysis is a common approach for detecting malicious software. However, many malware samples identify the presence of the analysis environment and evade detection by not performing any malicious activity. Recently, an approach to the automated detection of such evasive malware was proposed. In this approach, a malware sample is(More)
Sophisticated cyber security threats, such as advanced persistent threats, rely on infecting end points within a targeted security domain and embedding malware. Typically, such malware periodically reaches out to the command and control infrastructures controlled by adversaries. Such callback behavior, called beaconing, is challenging to detect as (a)(More)
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