DeepDetectNet vs RLAttackNet: An adversarial method to improve deep learning-based static malware detection model

Fang, Yong; Yuetian, Zeng; Li, Beibei; Liu, Liang; Zhang, Lei

doi:10.1371/journal.pone.0231626

Cited by 30 publications

(16 citation statements)

References 30 publications

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“… Nasiri, Khosravani & Weinberg (2017) noted that artificial neural networks (ANNs) have been used to detect faults in the rotor system of helicopters and can predict crack growth for sheet material. Fang et al (2020) reported on the use of deep learning in malware detection that achieves better results with a high effectiveness. Moreover, feature extraction for malware detection performs better with deep learning ( Fang et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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Intelligent systems for additive manufacturing-based repair in remanufacturing: a systematic review of its potential

Yusoh

Wahab

Habeeb

et al. 2021

PeerJ Computer Science

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The conventional component repair in remanufacturing involves human decision making that is influenced by several factors such as conditions of incoming cores, modes of failure, severity of damage, features and geometric complexities of cores and types of reparation required. Repair can be enhanced through automation using additive manufacturing (AM) technology. Advancements in AM have led to the development of directed energy deposition and laser cladding technology for repair of damaged parts and components. The objective of this systematic literature review is to ascertain how intelligent systems can be integrated into AM-based repair, through artificial intelligence (AI) approaches capable of supporting the nature and process of decision making during repair. The integration of intelligent systems in AM repair is expected to enhance resource utilization and repair efficiency during remanufacturing. Based on a systematic literature review of articles published during 2005–2021, the study analyses the activities of conventional repair in remanufacturing, trends in the applications of AM for repair using the current state-of-the-art technology and how AI has been deployed to facilitate repair. The study concludes with suggestions on research areas and opportunities that will further enhance the automation of component repair during remanufacturing using intelligent AM systems.

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Section: Resultsmentioning

confidence: 99%

“… Fang et al (2020) reported on the use of deep learning in malware detection that achieves better results with a high effectiveness. Moreover, feature extraction for malware detection performs better with deep learning ( Fang et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Intelligent systems for additive manufacturing-based repair in remanufacturing: a systematic review of its potential

Yusoh

Wahab

Habeeb

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

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“…On the basis of gym-malware, there are multiple follow-up work [20,39,41,42,72,76,139] proposing problem-space black-box adversarial attacks against static PE malware detection models.…”

Section: 22mentioning

confidence: 99%

“…1) DQEAF uses a subset of modifications employed in gym-malware and guarantees that all of them would not lead to corruptions in the modified malware; 2) DQEAF uses a vector with 513 dimensions as the observed state, which is much lower than that in gym-malware; 3) DQEAF makes the priority into consideration during the replay of past transitions. Fang et al [41] also observe that the modifications in the action space of gym-malware have some randomness and further found that most effective adversarial malware from gymmalware are generated by UPX pack/unpacked modifications, which could lead to some training problems with RL due to the non-repeatability of those modifications. Thus, they first reduce the action space to 6 categories having certain deterministic parameters and then propose an improved black-box adversarial attack, namely RLAttackNet, based on the gym-malware implementation.…”

Section: 22mentioning

confidence: 99%

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

Ling¹,

Wu²,

Zhang³

et al. 2021

Preprint

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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.

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“…Machine learning algorithms are developed to assume that the environment is benign, but they fail when even a small adversary can modify their inputs. This is where adversarial machines come in handy [42]. Adversarial machine learning is a branch of machine learning that studies a set of assaults aimed at degrading the performance of classifiers on certain tasks.…”

Section: Adversarial Machine Learningmentioning

confidence: 99%

Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning

et al. 2021

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Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology. There is a drastic rise in the new types of attacks where the conventional signature-based systems cannot keep up with these attacks. Machine learning seems to be a solution to solve many problems, including problems in cyber security. It is proven to be a very useful tool in the evolution of malware detection systems. However, the security of AI-based malware detection models is fragile. With advancements in machine learning, attackers have found a way to work around such detection systems using an adversarial attack technique. Such attacks are targeted at the data level, at classifier models, and during the testing phase. These attacks tend to cause the classifier to misclassify the given input, which can be very harmful in real-time AI-based malware detection. This paper proposes a framework for generating the adversarial malware images and retraining the classification models to improve malware detection robustness. Different classification models were implemented for malware detection, and attacks were established using adversarial images to analyze the model’s behavior. The robustness of the models was improved by means of adversarial training, and better attack resistance is observed.

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DeepDetectNet vs RLAttackNet: An adversarial method to improve deep learning-based static malware detection model

Cited by 30 publications

References 30 publications

Intelligent systems for additive manufacturing-based repair in remanufacturing: a systematic review of its potential

Intelligent systems for additive manufacturing-based repair in remanufacturing: a systematic review of its potential

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

Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning

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