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Learning and Classification of Malware Behavior [PDF]
, 2008 Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection.
Konrad Rieck +4 more
openaire +3 more sources
A Data Mining Classification Approach for Behavioral Malware Detection [PDF]
Journal of Computer Networks and Communications, 2016 Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.
Monire Norouzi +2 more
doaj +2 more sources
Multimodal Techniques for Malware Classification
CoRRThe threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with multimodal machine learning approaches for malware classification, based on the structured nature of the Windows Portable Executable (PE) file format.
Jonathan Jiang, Mark Stamp 0001
openaire +3 more sources
Malware Classification with BERT
, 2022 Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the ...
Alvares, Joel Lawrence
openaire +3 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 +3 more sources
Applied Sciences, 2021 Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and ...
Dong-Kyu Chae +4 more
doaj +1 more source
Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
Applied Sciences, 2021 Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted ...
Muhammad Asam +8 more
doaj +1 more source
DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
Applied Sciences, 2023 Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher +4 more
doaj +1 more source
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