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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
Effective and Reliable Malware Group Classification for a Massive Malware Environment
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 +1 more source
Reverse Engineering untuk Analisis Malware Remote Access Trojan
JEPIN (Jurnal Edukasi dan Penelitian Informatika), 2019 Para hacker menggunakan malware Remote Access Trojan untuk merusak sistem kemudian mencuri data para korbannya. Diperlukan analisis mendalam mengenai malware baru-baru ini karena malware dapat berkamuflase seperti sistem tidak dicurigai.
Tesa Pajar Setia +2 more
doaj +1 more source
FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis
IEEE Access, 2022 To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection,
Younghoon Ban +4 more
doaj +1 more source
Malware Detection using Machine Learning and Deep Learning
, 2019 Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware.
A Nappa +6 more
core +1 more source
Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection
International Journal of Computational Intelligence Systems, 2018 Nowadays, security of the computer systems has become a major concern of security experts. In spite of many antivirus and malware detection systems, the number of malware incidents are increasing day by day.
Hongwei Zhao +3 more
doaj +1 more source
Mal-Detect: An intelligent visualization approach for malware detection
Journal of King Saud University: Computer and Information Sciences, 2022 Recent outbreaks of pandemics have deepened the adoption and use of IT-based systems. This development has led to an exponential increase in cyberattacks caused by malware.
Olorunjube James Falana +3 more
doaj +1 more source
, 2013 Malware family classification is an age old problem that many Anti-Virus (AV) companies have tackled. There are two common techniques used for classification, signature based and behavior based.
Alrawi, Omar, Mohaisen, Abedelaziz
core +1 more source
Advanced Intelligent Systems, EarlyView.OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti +4 more
wiley +1 more source
Applied Sciences, 2020 Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it.
Sejun Jang, Shuyu Li, Yunsick Sung
doaj +1 more source