Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art

X Ling, L Wu, J Zhang, Z Qu, W Deng, X Chen… - Computers & …, 2023 - Elsevier
Malware has been one of the most damaging threats to computers that span across multiple
operating systems and various file formats. To defend against ever-increasing and ever …

A survey on semi-supervised learning for delayed partially labelled data streams

HM Gomes, M Grzenda, R Mello, J Read… - ACM Computing …, 2022 - dl.acm.org
Unlabelled data appear in many domains and are particularly relevant to streaming
applications, where even though data is abundant, labelled data is rare. To address the …

A survey of strategy-driven evasion methods for PE malware: Transformation, concealment, and attack

J Geng, J Wang, Z Fang, Y Zhou, D Wu, W Ge - Computers & Security, 2024 - Elsevier
The continuous proliferation of malware poses a formidable threat to the cyberspace
landscape. Researchers have proffered a multitude of sophisticated defense mechanisms …

Challenges and pitfalls in malware research

M Botacin, F Ceschin, R Sun, D Oliveira, A Grégio - Computers & Security, 2021 - Elsevier
As the malware research field became more established over the last two decades, new
research questions arose, such as how to make malware research reproducible, how to …

Ransomware detection with a 2-tier machine learning approach using a novel clustering algorithm

R Zhang, Y Liu - 2024 - researchsquare.com
Ransomware poses a significant threat to cybersecurity, causing extensive financial and
operational damage by encrypting critical data and demanding ransom for its release. The …

MalwD&C: A Quick and Accurate Machine Learning-Based Approach for Malware Detection and Categorization

A Buriro, AB Buriro, T Ahmad, S Buriro, S Ullah - Applied Sciences, 2023 - mdpi.com
Malware, short for malicious software, is any software program designed to cause harm to a
computer or computer network. Malware can take many forms, such as viruses, worms …

Unraveling the key of machine learning solutions for android malware detection

J Liu, J Zeng, F Pierazzi, L Cavallaro… - arxiv preprint arxiv …, 2024 - arxiv.org
Android malware detection serves as the front line against malicious apps. With the rapid
advancement of machine learning (ML), ML-based Android malware detection has attracted …

A comparison of neural-network-based intrusion detection against signature-based detection in iot networks

M Schrötter, A Niemann, B Schnor - Information, 2024 - mdpi.com
Over the last few years, a plethora of papers presenting machine-learning-based
approaches for intrusion detection have been published. However, the majority of those …

A wolf in sheep's clothing: practical black-box adversarial attacks for evading learning-based windows malware detection in the wild

X Ling, Z Wu, B Wang, W Deng, J Wu, S Ji… - 33rd USENIX Security …, 2024 - usenix.org
Given the remarkable achievements of existing learning-based malware detection in both
academia and industry, this paper presents MalGuise, a practical black-box adversarial …

[PDF][PDF] A Categorical Data Approach for Anomaly Detection in WebAssembly Applications.

T Heinrich, NC Will, RR Obelheiro, CA Maziero - ICISSP, 2024 - pdfs.semanticscholar.org
The security of Web Services for users and developers is essential; since WebAssembly is a
new format that has gained attention in this type of environment over the years, new …