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Adaptive Semantics-Aware Malware Classification [PDF]
, 2016 Automatic malware classification is an essential improvement over the widely-deployed detection procedures using manual signatures or heuristics. Although there exists an abundance of methods for collecting static and behavioral malware data, there is a lack of adequate tools for analysis based on these collected features.
Bojan Kolosnjaji +4 more
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Humans vs. Machines in Malware Classification. [PDF]
, 2022 Today, the classification of a file as either benign or malicious is performed by a combination of deterministic indicators (such as antivirus rules), Machine Learning classifiers, and, more importantly, the judgment of human experts. However, to compare the difference between human and machine intelligence in malware analysis, it is first necessary to
Aonzo, Simone +3 more
openaire +4 more sources
Effective and Reliable Malware Group Classification for a Massive Malware Environment [PDF]
International Journal of Distributed Sensor Networks, 2016 Most of the cyber-attacks are caused by malware, and damage from them has escalated from cyber space to home appliances and infrastructure, thus affecting the daily living of the people. As such, anticipative analysis and countermeasures for malware have
Taejin Lee, Jin Kwak
doaj +3 more sources
An Analysis of Android Malware Classification Services. [PDF]
Sensors (Basel), 2021 The increasing number of Android malware forced antivirus (AV) companies to rely on automated classification techniques to determine the family and class of suspicious samples. The research community relies heavily on such labels to carry out prevalence studies of the threat ecosystem and to build datasets that are used to validate and benchmark novel ...
Rashed M, Suarez-Tangil G.
europepmc +5 more sources
Malware-on-the-Brain: Illuminating Malware Byte Codes With Images for Malware Classification
IEEE Transactions on Computers, 2023 Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc.
Fangtian Zhong +5 more
openaire +2 more sources
Malware detection and classification using low-level features [PDF]
, 2023 Nowadays, computers and computer systems are involved in most areas of our lives. Employees and users of manufacturing and transportation, banking and healthcare, education, and entertainment rely on computers and networks which allow for better, faster,
Banin, Sergii
core +4 more sources
Applied Sciences, 2020 Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes.
Zhijie Tang, Peng Wang, Junfeng Wang
doaj +1 more source
A deep learning framework for malware classification [PDF]
, 2020 Copyright © 2020, IGI Global. In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial ...
Rochan, Mrigank +11 more
core +4 more sources
On the effectiveness of binary emulation in malware classification
Journal of Information Security and Applications, 2022 Malware authors are continuously evolving their code base to include counter-analysis methods that can significantly hinder their detection and blocking. While the execution of malware in a sandboxed environment may provide a lot of insightful feedback about what the malware actually does in a machine, anti-virtualisation and hooking evasion methods ...
Vouvoutsis, Vasilis +2 more
openaire +2 more sources
IEEE Access, 2023 In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic techniques for visualization-based malware classification and detection.
Mohamad Mulham Belal +1 more
doaj +1 more source