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Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art
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 …
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
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 …
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
The continuous proliferation of malware poses a formidable threat to the cyberspace
landscape. Researchers have proffered a multitude of sophisticated defense mechanisms …
landscape. Researchers have proffered a multitude of sophisticated defense mechanisms …
Challenges and pitfalls in malware research
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 …
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 …
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
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 …
computer or computer network. Malware can take many forms, such as viruses, worms …
Unraveling the key of machine learning solutions for android malware detection
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 …
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 …
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
Given the remarkable achievements of existing learning-based malware detection in both
academia and industry, this paper presents MalGuise, a practical black-box adversarial …
academia and industry, this paper presents MalGuise, a practical black-box adversarial …
[PDF][PDF] A Categorical Data Approach for Anomaly Detection in WebAssembly Applications.
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 …
new format that has gained attention in this type of environment over the years, new …