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Survey of Machine Learning Techniques for Malware Analysis [PDF]

, 2018
Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind ...
Aniello, Leonardo, Baldoni, Roberto, Ucci, Daniele   +2 more
core   +2 more sources

Securing Linux Cloud Environments: Privacy-Aware Federated Learning Framework for Advanced Malware Detection in Linux Clouds

IEEE Access
Cloud computing is integral to modern IT infrastructure, with Linux-based virtual machines (VMs) comprising 95% of public cloud environments. This widespread use makes Linux VMs a prime target for cyberattacks, particularly advanced malware designed for ...
Tom Landman, Nir Nissim
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, A Singh, C Eagle, J Seitz, M Sikorski   +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, Zekai Chen, Minghui Xu, Guoming Zhang, Dongxiao Yu, Xiuzhen Cheng   +5 more
openaire   +2 more sources

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, Aldy Putra Aldya, Nur Widiyasono   +2 more
doaj   +1 more source

Unveiling Zeus

, 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

Defending against OS-Level Malware in Mobile Devices via Real-Time Malware Detection and Storage Restoration

Journal of Cybersecurity and Privacy, 2022
Combating the OS-level malware is a very challenging problem as this type of malware can compromise the operating system, obtaining the kernel privilege and subverting almost all the existing anti-malware tools.
Niusen Chen, Bo Chen
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, Sunjun Lee, Dokyung Song, Haehyun Cho, Jeong Hyun Yi   +4 more
doaj   +1 more source

Tomtit‐Raven Evolutionary Selector‐Reinforced Attention‐Driven: A High‐Performance and Computationally Efficient Cyber Threat Detection Framework for Smart Grids

Energy Science &Engineering, EarlyView.
Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga, Praveen Kumar Balachandran, Umawathy Techanamurthy, Muhammad Ammirrul Atiqi Mohd Zainuri   +3 more
wiley   +1 more source

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

Telematics and Informatics Reports
The ever-increasing growth of online services and smart connectivity of devices have posed the threat of malware to computer system, android-based smart phones, Internet of Things (IoT)-based systems.
Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar   +2 more
doaj   +1 more source