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Ben Feinstein & Daniel Peck: CaffeineMonkey: Automated Collection, Detection and Analysis of Malicious JavaScript
Manage episode 152211987 series 1053194
A tartalmat a Black Hat Briefings, USA 2007 [Video] Presentations from the security conference. biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Black Hat Briefings, USA 2007 [Video] Presentations from the security conference. vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
The web browser is ever increasing in its importance to many organizations. Far from its origin as an application for fetching and rendering HTML, today?s web browser offers an expansive attack surface to exploit. All the major browsers now include full-featured runtime engines for a variety of interpreted scripting languages, including the popular JavaScript. The web experience now depends more than ever on the ability of the browser to dynamically interpret JavaScript on the client.
The authors present a software framework for the automated collection of JavaScript from the wild, the subsequent identification of malicious code, and characteristic analysis of malicious code once identified. Building on the work of several existing client honeypot implementations, our goal is to largely automate the painstaking work of malicious software collection. Our focus is on attacks using JavaScript for obfuscation or exploitation.
The authors will present findings based on the deployment of a distributed network of CaffeineMonkeys. The analysis and conclusions will focus on identifying new in-the-wild obfuscation / evasion techniques and JavaScript browser exploits, quantifying the prevalence and distribution of well-known and newly discovered obfuscation and evasion techniques, as well as quantifying the prevalence and distribution of known and newly discovered JavaScript browser exploits.
The authors will release a previously unpublished JavaScript evasion technique and demonstrate its use in evading a variety of present-day defensive technologies. Where present-day defenses have been demonstrated to be insufficient, the authors will present new ideas for ways mitigate the new threats.
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The authors present a software framework for the automated collection of JavaScript from the wild, the subsequent identification of malicious code, and characteristic analysis of malicious code once identified. Building on the work of several existing client honeypot implementations, our goal is to largely automate the painstaking work of malicious software collection. Our focus is on attacks using JavaScript for obfuscation or exploitation.
The authors will present findings based on the deployment of a distributed network of CaffeineMonkeys. The analysis and conclusions will focus on identifying new in-the-wild obfuscation / evasion techniques and JavaScript browser exploits, quantifying the prevalence and distribution of well-known and newly discovered obfuscation and evasion techniques, as well as quantifying the prevalence and distribution of known and newly discovered JavaScript browser exploits.
The authors will release a previously unpublished JavaScript evasion technique and demonstrate its use in evading a variety of present-day defensive technologies. Where present-day defenses have been demonstrated to be insufficient, the authors will present new ideas for ways mitigate the new threats.
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