Results 61 to 70 of about 134,256 (265)
Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm
IEEE Access, 2020 Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems.
S. Abijah Roseline +3 more
doaj +1 more source
Future Internet, 2023 Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware ...
Jinting Zhu +4 more
doaj +1 more source
On the Reverse Engineering of the Citadel Botnet [PDF]
, 2014 Citadel is an advanced information-stealing malware which targets financial information. This malware poses a real threat against the confidentiality and integrity of personal and business data. A joint operation was recently conducted by the FBI and the
A Rahimian +4 more
core +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
, 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
IEEE Transactions on Emerging Topics in Computing, 2023 Zero-day malicious software (malware) refers to a previously unknown or newly discovered software vulnerability. The fundamental objective of this paper is to enhance detection for analogous zero-day malware by efficient learning to plausible generated ...
Dong-Ok Won +2 more
semanticscholar +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
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
IEEE Transactions on Information Forensics and Security, 2019 With the widespread use of smartphones, the number of malware has been increasing exponentially. Among smart devices, android devices are the most targeted devices by malware because of their high popularity.
Taeguen Kim +4 more
semanticscholar +1 more source
Android malware category detection using a novel feature vector-based machine learning model
Cybersecurity, 2023 Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android ...
Hashida Haidros +2 more
semanticscholar +1 more source