FlashDetect: ActionScript 3 Malware Detection
نویسندگان
چکیده
Adobe Flash is present on nearly every PC, and it is increasingly being targeted by malware authors. Despite this, research into methods for detecting malicious Flash files has been limited. Similarly, there is very little documentation available about the techniques commonly used by Flash malware. Instead, most research has focused on JavaScript malware. This paper discusses common techniques such as heap spraying, JIT spraying, and type confusion exploitation in the context of Flash malware. Where applicable, these techniques are compared to those used in malicious JavaScript. Subsequently, FlashDetect is presented, an offline Flash file analyzer that uses both dynamic and static analysis, and that can detect malicious Flash files using ActionScript 3. FlashDetect classifies submitted files using a naive Bayesian classifier based on a set of predefined features. Our experiments show that FlashDetect has high classification accuracy, and that its efficacy is comparable with that of commercial anti-virus products.
منابع مشابه
Comprehensive Analysis and Detection of Flash-Based Malware
Adobe Flash is a popular platform for providing dynamic and multimedia content on web pages. Despite being declared dead for years, Flash is still deployed on millions of devices. Unfortunately, the Adobe Flash Player increasingly suffers from vulnerabilities, and attacks using Flash-based malware regularly put users at risk of being remotely attacked—most prominently highlighted by numerous ex...
متن کاملDetection of Malicious Scripting Code Through Discriminant and Adversary-Aware API Analysis
JavaScript and ActionScript are powerful scripting languages that do not only allow the delivery of advanced multimedia contents, but that can be also used to exploit critical vulnerabilities of third-party applications. To detect both ActionScriptand JavaScript-based malware, we propose in this paper a machine-learning methodology that is based on extracting discriminant information from syste...
متن کاملDyVSoR: dynamic malware detection based on extracting patterns from value sets of registers
To control the exponential growth of malware files, security analysts pursue dynamic approaches that automatically identify and analyze malicious software samples. Obfuscation and polymorphism employed by malwares make it difficult for signature-based systems to detect sophisticated malware files. The dynamic analysis or run-time behavior provides a better technique to identify the threat. In t...
متن کاملMalware Detection using Classification of Variable-Length Sequences
In this paper, a novel method based on the graph is proposed to classify the sequence of variable length as feature extraction. The proposed method overcomes the problems of the traditional graph with variable length of data, without fixing length of sequences, by determining the most frequent instructions and insertion the rest of instructions on the set of “other”, save speed and memory. Acco...
متن کامل