Results 11 to 20 of about 29,821 (199)
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 +6 more sources
MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware
2023 IEEE International Conference on Intelligence and Security Informatics (ISI), 2023 Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem.
Eren, Maksim E. +4 more
openaire +2 more sources
On Deceiving Malware Classification with Section Injection
Machine Learning and Knowledge Extraction, 2023 We investigate how to modify executable files to deceive malware classification systems. This work’s main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but ...
Adeilson Antonio da Silva +1 more
doaj +1 more source
A multilabel fuzzy relevance clustering system for malware attack attribution in the edge layer of cyber-physical networks [PDF]
, 2020 The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users’ systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would ...
Alaeiyan, M +4 more
core +2 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
Frontiers in Plant Science, 2023 Due to developments in science and technology, the field of plant protection and the information industry have become increasingly integrated, which has resulted in the creation of plant protection information systems.
Zhiguo Chen +5 more
doaj +1 more source
Deep learning based Sequential model for malware analysis using Windows exe API Calls [PDF]
PeerJ Computer Science, 2020 Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection.
Ferhat Ozgur Catak +3 more
doaj +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
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
Mathematics, 2022 The rapid increase of malware attacks has become one of the main threats to computer security. Finding the best way to detect malware has become a critical task in cybersecurity. Previous work shows that machine learning approaches could be a solution to
Wei-Cheng Lin, Yi-Ren Yeh
doaj +1 more source