Toward Automated Dynamic Malware Analysis Using CWSandbox
01 Mar 2007-Vol. 5, Iss: 2, pp 32-39
TL;DR: The design and implementation of CWSandbox is described, a malware analysis tool that fulfills the three design criteria of automation, effectiveness, and correctness for the Win32 family of operating systems.
Abstract: Malware is notoriously difficult to combat because it appears and spreads so quickly. In this article, we describe the design and implementation of CWSandbox, a malware analysis tool that fulfills our three design criteria of automation, effectiveness, and correctness for the Win32 family of operating systems
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TL;DR: An overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples and analysis programs that employ these techniques to assist human analysts in assessing whether a given sample deserves closer manual inspection due to its unknown malicious behavior is provided.
Abstract: Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware.This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.
815 citations
27 Oct 2008
TL;DR: Ether, a transparent and external approach to malware analysis, is proposed, which is motivated by the intuition that for a malware analyzer to be transparent, it must not induce any side-effects that are unconditionally detectable by malware.
Abstract: Malware has become the centerpiece of most security threats on the Internet. Malware analysis is an essential technology that extracts the runtime behavior of malware, and supplies signatures to detection systems and provides evidence for recovery and cleanup. The focal point in the malware analysis battle is how to detect versus how to hide a malware analyzer from malware during runtime. State-of-the-art analyzers reside in or emulate part of the guest operating system and its underlying hardware, making them easy to detect and evade. In this paper, we propose a transparent and external approach to malware analysis, which is motivated by the intuition that for a malware analyzer to be transparent, it must not induce any side-effects that are unconditionally detectable by malware. Our analyzer, Ether, is based on a novel application of hardware virtualization extensions such as Intel VT, and resides completely outside of the target OS environment. Thus, there are no in-guest software components vulnerable to detection, and there are no shortcomings that arise from incomplete or inaccurate systememulation. Our experiments are based on our study of obfuscation techniques used to create 25,000 recent malware samples. The results show that Ether remains transparent and defeats the obfuscation tools that evade existing approaches.
756 citations
TL;DR: An incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis is proposed, significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.
Abstract: Malicious software - so called malware - poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffective, such that millions of hosts in the Internet are infected with malware in the form of computer viruses, Internet worms and Trojan horses. While obfuscation and polymorphism employed by malware largely impede detection at file level, the dynamic analysis of malware binaries during run-time provides an instrument for characterizing and defending against the threat of malicious software.
In this article, we propose a framework for the automatic analysis of malware behavior using machine learning. The framework allows for automatically identifying novel classes of malware with similar behavior (clustering) and assigning unknown malware to these discovered classes (classification). Based on both, clustering and classification, we propose an incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis. The incremental analysis significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.
675 citations
10 Jul 2008
TL;DR: The effectiveness of the proposed method for learning and discrimination of malware behavior is demonstrated, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software.
Abstract: Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection. Yet variants of malware families share typical behavioral patternsreflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored in a sandbox environment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifieris trained using learning techniques and (c) discriminative features of the behavior models are ranked for explanation of classification decisions. Experiments with different heterogeneous test data collected over several months using honeypots demonstrate the effectiveness of our method, especially in detecting novelinstances of malware families previously not recognized by commercial anti-virus software.
648 citations
Patent•
14 Sep 2012TL;DR: In this paper, the authors present a system that allows mobile subscribers, and others, to submit mobile applications to be analyzed for anomalous and malicious behavior using data acquired during the execution of the application within a highly instrumented and controlled environment for which the analysis relies on per-execution as well as comparative aggregate data across many such executions from one or more subscribers.
Abstract: The present system includes a computer-networked system that allows mobile subscribers, and others, to submit mobile applications to be analyzed for anomalous and malicious behavior using data acquired during the execution of the application within a highly instrumented and controlled environment for which the analysis relies on per-execution as well as comparative aggregate data across many such executions from one or more subscribers.
625 citations
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Proceedings Article•
12 Jul 1999TL;DR: The Detours library is presented, a library for instrumenting arbitrary Win32 functions on x86 machines and is the first package on any platform to logically preserve the un-instrumented target function as a subroutine for use by the instrumentation.
Abstract: Innovative systems research hinges on the ability to easily instrument and extend existing operating system and application functionality. With access to appropriate source code, it is often trivial to insert new instrumentation or extensions by rebuilding the OS or application. However, in today's world of commercial software, researchers seldom have access to all relevant source code.
We present Detours, a library for instrumenting arbitrary Win32 functions on x86 machines. Detours intercepts Win32 functions by re-writing target function images. The Detours package also contains utilities to attach arbitrary DLLs and data segments (called payloads) to any Win32 binary.
While prior researchers have used binary rewriting to insert debugging and profiling instrumentation, to our knowledge, Detours is the first package on any platform to logically preserve the un-instrumented target function (callable through a trampoline) as a subroutine for use by the instrumentation. Our unique trampoline design is crucial for extending existing binary software.
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587 citations
Proceedings Article•
04 Aug 2003TL;DR: This paper discusses the methodology and design of privilege separation, a generic approach that lets parts of an application run with different levels of privilege, and illustrates how separation of privileges reduces the amount of OpenSSH code that is executed with special privilege.
Abstract: Many operating system services require special privilege to execute their tasks. A programming error in a privileged service opens the door to system compromise in the form of unauthorized acquisition of privileges. In the worst case, a remote attacker may obtain superuser privileges. In this paper, we discuss the methodology and design of privilege separation, a generic approach that lets parts of an application run with different levels of privilege. Programming errors occurring in the unprivileged parts can no longer be abused to gain unauthorized privileges. Privilege separation is orthogonal to capability systems or application confinement and enhances the security of such systems even further.
Privilege separation is especially useful for system services that authenticate users. These services execute privileged operations depending on internal state not known to an application confinement mechanism. As a concrete example, the concept of privilege separation has been implemented in OpenSSH. However, privilege separation is equally useful for other authenticating services. We illustrate how separation of privileges reduces the amount of OpenSSH code that is executed with special privilege. Privilege separation prevents known security vulnerabilities in prior OpenSSH versions including some that were unknown at the time of its implementation.
549 citations