An official website of the United States government
Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant works offer subtle perturbations or additions to executable files and do not explore full-file obfuscation. In this study, we show that an open-source encryption tool coupled with a Reinforcement Learning (RL) framework can successfully obfuscate malware to evade state-of-the-art malware detection engines and outperform techniques that use advanced modification methods. Our results show that the proposed method improves the evasion rate from 27%-49% compared to widely-used state-of-the-art reinforcement learning-based methods.
more »
« less
Detecting and Classifying Self-Deleting Windows Malware Using Prefetch Files
Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.
more »
« less
- PAR ID:
- 10322196
- Date Published:
- Journal Name:
- 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Since malware has caused serious damages and evolving threats to computer and Internet users, its detection is of great interest to both anti-malware industry and researchers. In recent years, machine learning-based systems have been successfully deployed in malware detection, in which different kinds of classifiers are built based on the training samples using different feature representations. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the adversarial machine learning in malware detection. In particular, on the basis of a learning-based classifier with the input of Windows Application Programming Interface (API) calls extracted from the Portable Executable (PE) files, we present an effective evasion attack model (named EvnAttack) by considering different contributions of the features to the classification problem. To be resilient against the evasion attack, we further propose a secure-learning paradigm for malware detection (named SecDefender), which not only adopts classifier retraining technique but also introduces the security regularization term which considers the evasion cost of feature manipulations by attackers to enhance the system security. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed methods.more » « less
-
null (Ed.)Android applications rely heavily on strings for sensitive operations like reflection, access to system resources, URL connections, database access, among others. Thus, insight into application behavior can be gained through not only an analysis of what strings an application creates but also the structure of the computation used to create theses strings, and in what manner are these strings used. In this paper we introduce a static analysis of Android applications to discover strings, how they are created, and their usage. The output of our static analysis contains all of this information in the form of a graph which we call a string computation. We leverage the results to classify individual application behavior with respect to malicious or benign intent. Unlike previous work that has focused only on extraction of string values, our approach leverages the structure of the computation used to generate string values as features to perform classification of Android applications. That is, we use none of the static analysis computed string values, rather using only the graph structures of created strings to do classification of an arbitrary Android application as malware or benign. Our results show that leveraging string computation structures as features can yield precision and recall rates as high as 97% on modern malware. We also provide baseline results against other malware detection tools and techniques to classify the same corpus of applications.more » « less
-
Prof. Ninghui Li Editor in Chief, ACM Transactions (Ed.)Malware analysis is an essential task to understand infection campaigns, the behavior of malicious codes, and possible ways to mitigate threats. Malware analysis also allows better assessment of attacker’s capabilities, techniques, and processes. Although a substantial amount of previous work provided a comprehensive analysis of the international malware ecosystem, research on regionalized, country, and population-specific malware campaigns have been scarce. Moving towards addressing this gap, we conducted a longitudinal (2012-2020) and comprehensive (encompassing an entire population of online banking users) study of MS Windows desktop malware that actually infected Brazilian bank’s users. We found that the Brazilian financial desktop malware has been evolving quickly: it started to make use of a variety of file formats instead of typical PE binaries, relied on native system resources, and abused obfuscation technique to bypass detection mechanisms. Our study on the threats targeting a significant population on the ecosystem of the largest and most populous country in Latin America can provide invaluable insights that may be applied to other countries’ user populations, especially those in the developing world that might face cultural peculiarities similar to Brazil’s. With this evaluation, we expect to motivate the security community/industry to seriously considering a deeper level of customization during the development of next generation anti-malware solutions, as well as to raise awareness towards regionalized and targeted Internet threats.more » « less
-
Malware written in dynamic languages such as PHP routinely employ anti-analysis techniques such as obfuscation schemes and evasive tricks to avoid detection. On top of that, attackers use automated malware creation tools to create numerous variants with little to no manual effort. This paper presents a system called Cubismo to solve this pressing problem. It processes potentially malicious files and decloaks their obfuscations, exposing the hidden malicious code into multiple files. The resulting files can be scanned by existing malware detection tools, leading to a much higher chance of detection. Cubismo achieves improved detection by exploring all executable statements of a suspect program counterfactually to see through complicated polymorphism, metamorphism and, obfuscation techniques and expose any malware. Our evaluation on a real-world data set collected from a commercial web hosting company shows that Cubismo is highly effective in dissecting sophisticated metamorphic malware with multiple layers of obfuscation. In particular, it enables VirusTotal to detect 53 out of 56 zero-day malware samples in the wild, which were previously undetectable.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCWC54503.2022.9720874
