Results 1 to 10 of about 707,324 (167)

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System [PDF]

IEEE Access, 2018
For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention.
Saba Arshad, Munam A. Shah, Abdul Wahid, Amjad Mehmood, Houbing Song, Hongnian Yu   +5 more
doaj   +3 more sources

Static Malware Detection Using Stacked BiLSTM and GPT-2

IEEE Access, 2022
In recent years, cyber threats and malicious software attacks have been escalated on various platforms. Therefore, it has become essential to develop automated machine learning methods for defending against malware.
Deniz Demirci, Nazenin Sahin, Melih Sirlancis, Cengiz Acarturk   +3 more
doaj   +3 more sources

Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity

Land Forces Academy Review
Abstract In the rapidly advancing digital age, the surge in malware complexity presents a formidable challenge to cybersecurity efforts, rendering traditional signature-based detection methods increasingly obsolete. These methods struggle to keep pace with the swift evolution of malware, particularly with the emergence of polymorphic and
Mahmoud Basharat, Marwan Omar
openaire   +2 more sources

Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data

Sensors
Android malware detection remains a critical issue for mobile security. Cybercriminals target Android since it is the most popular smartphone operating system (OS). Malware detection, analysis, and classification have become diverse research areas.
Mehwish Naseer, Farhan Ullah, Samia Ijaz, Hamad Naeem, Amjad Alsirhani, Ghadah Naif Alwakid, Abdullah Alomari   +6 more
doaj   +2 more sources

OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files

IEEE Access
The increasing reliance on compressed file formats for data storage and transmission has made them attractive vectors for malware propagation, as their structural complexity enables evasion of conventional detection mechanisms.
Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin   +3 more
doaj   +2 more sources

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Future Internet
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran, Annalisa Appice, Donato Malerba   +2 more
doaj   +2 more sources

A Holistic Intelligent Cryptojacking Malware Detection System

IEEE Access
Recent statistics indicate a continuous rise in cryptojacking malware. This malware covertly exploits users’ device resources to mine cryptocurrencies, such as Bitcoin, without their knowledge or consent.
Hadeel A. Almurshid, Iman Almomani, M. A. Khalifa, Walid El-Shafai   +3 more
doaj   +2 more sources

MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.

PLoS ONE, 2022
With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention.
Yuying Liu, Pin Yang, Peng Jia, Ziheng He, Hairu Luo   +4 more
doaj   +2 more sources

A Feasibility Study on Evasion Attacks Against NLP-Based Macro Malware Detection Algorithms

IEEE Access, 2023
Machine learning-based models for malware detection have gained prominence in order to detect obfuscated malware. These models extract malicious features and endeavor to classify samples as either malware or benign entities.
Mamoru Mimura, Risa Yamamoto
doaj   +1 more source

Global-Local Attention-Based Butterfly Vision Transformer for Visualization-Based Malware Classification

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, Divya Meena Sundaram   +1 more
doaj   +1 more source