Generation &amp; Evaluation of Adversarial Examples for Malware Obfuscation

With the increased deployment of IoT and edge devices into commercial and user networks, these devices have become a new threat vector for malware authors. It is imperative to protect these devices as they become more prevalent in commercial and personal networks. However, due to their limited computational power and storage space, especially in the case of battery-powered devices, it is infeasible to deploy state-of-the-art malware detectors onto these systems. In this work, we propose using and extracting features from Markov matrices constructed from opcode traces as a low cost feature for unobfuscated and obfuscated malware detection. We empirically show that our approach maintains a high detection rate while consuming less power than similar work.

show abstract

“…al. show that these simpler transformations are still effective against machine learning based malware detection and classification models [26].…”

Section: B Obfuscationmentioning

confidence: 99%

“…In this section, we will review the obfuscation techniques implemented in this study by summarizing definitions laid out in [26], [27]. The transformations that we considered are only those which create variants of themselves, affecting the sequence of opcodes in a binary.…”

Section: B Obfuscationmentioning

confidence: 99%

Towards Obfuscated Malware Detection for Low Powered IoT Devices

Park

Powers

Prashker

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Differently, to evade visualization based malware detectors, Park et al [96] propose another adversarial attack based on the adversarial malware alignment obfuscation (AMAO) algorithm. Specifically, a non-executable adversarial image is first generated by the off-the-shelf adversarial attacks in the field of image classification [14,46].…”

Section: White-box Adversarial Attacks Against Pe Malware Detectionmentioning

confidence: 99%

Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art

Ling¹,

Wu²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

The malware has been being one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against the ever-increasing and ever-evolving threats of malware, tremendous efforts have been made to propose a variety of malware detection methods that attempt to effectively and efficiently detect malware so as to mitigate possible damages as earlier as possible. Recent studies have shown that, one the one hand, existing machine learning (ML) and deep learning (DL) techniques enable the superior detection of newly emerging and previously unseen malware. However, on the other hand, ML and DL models are inherently vulnerable to adversarial attacks in the form of adversarial examples, which are maliciously generated by slightly and carefully perturbing the legitimate inputs to confuse the targeted models. Basically, adversarial attacks are initially extensively studied in the domain of computer vision like image classification, and some quickly expanded to other domains, including natural language processing, audio recognition and even malware detection which is extremely critical to computers.In this paper, we focus on malware with the file format of portable executable (PE) in the family of Windows operating systems, namely Windows PE malware, as a representative case to study the adversarial attack methods in such adversarial settings. To be specific, we start by first outlining the general learning framework of Windows PE malware detection based on ML/DL and subsequently highlighting three unique challenges of performing adversarial attacks in the context of Windows PE malware. We then conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection. We conclude the paper by first presenting other related attacks against Windows PE malware detection beyond the adversarial attacks and then shedding light on future research directions and opportunities. In addition, a curated resource list of adversarial attacks and defenses for Windows PE malware detection is also available at https://github.com/ryderling/adversarial-attacks-and-defenses-for-windows-pe-malware-detection. CCS Concepts: • Security and privacy → Intrusion/anomaly detection and malware mitigation; • Theory of computation → Adversarial learning.

show abstract

“…Second, regarding selected attack methods, a few notable attack methods include simple append attack [9], attacking using randomly generated perturbation [4], and attacking using specific perturbations that lowers a malware detector's score [5]. More advanced methods incorporate machine learning techniques (Genetic Programming [1] [6], Gradient Descent [3], and Dynamic Programming [7]) and implement advanced DL-based techniques (Generative Adversarial Networks [8], Deep Reinforcement Learning [10], and Generative Recurrent Neural Networks [2] [11]). Third, and most importantly, while a sizable amount of AMG research either do not limit the number of queries to the malware detector or allow conducting multiple queries, few studies (Suciu et al [9]) operate in a single-shot AMG evasion setting.…”

Section: A Adversarial Malware Generation (Amg)mentioning

confidence: 99%

Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach

Hu¹,

Ebrahimi²,

Chen³

2021

Preprint

View full text Add to dashboard Cite

Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security concerns. Generating such adversarial variants by the defender is crucial to improving the resistance of DL-based malware detectors against them. This necessity has given rise to an emerging stream of machine learning research, Adversarial Malware example Generation (AMG), which aims to generate evasive adversarial malware variants that preserve the malicious functionality of a given malware. Within AMG research, black-box method has gained more attention than white-box methods. However, most black-box AMG methods require numerous interactions with the malware detectors to generate adversarial malware examples. Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth. In this study, we show that a novel DL-based causal language model enables singleshot evasion (i.e., with only one query to malware detector) by treating the content of the malware executable as a byte sequence and training a Generative Pre-Trained Transformer (GPT). Our proposed method, MalGPT, significantly outperformed the leading benchmark methods on a real-world malware dataset obtained from VirusTotal, achieving over 24.51% evasion rate. MalGPT enables cybersecurity researchers to develop advanced defense capabilities by emulating large-scale realistic AMG.

show abstract

Generation & Evaluation of Adversarial Examples for Malware Obfuscation

Cited by 35 publications

References 21 publications

Towards Obfuscated Malware Detection for Low Powered IoT Devices

Towards Obfuscated Malware Detection for Low Powered IoT Devices

Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art

Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach

Contact Info

Product

Resources

About