Results 41 to 50 of about 707,343 (186)

MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention

Array
As one of the major threats in cybersecurity, malware has been growing continuously and steadily. In recent years, researchers have proposed a number of graph representation learning based malware detection methods by leveraging the intrinsic topological
Ruisheng Li, Qilong Zhang, Huimin Shen
doaj   +1 more source

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

, 2018
The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid ...
Huang, TonTon Hsien-De, Kao, Hung-Yu
core   +1 more source

An Improved Method of Detecting Macro Malware on an Imbalanced Dataset

IEEE Access, 2020
In spear-phishing attacks, macro malware written in VBA (Visual Basic for Applications) is often used to compromise the target computers. Macro malware is often obfuscated in several ways to evade detection.
Mamoru Mimura
doaj   +1 more source

Malware detection techniques for mobile devices

, 2017
Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications
Amro, Bela
core   +1 more source

Preliminary Evidence of the Association Between Binge Eating and Preeclampsia in Pregnant U.S. Military Active‐Duty Service Women

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objectives Binge eating is the most common disordered eating behavior among pregnant women. This study examined the association of binge‐eating frequency with the presence of a self‐reported current preeclampsia diagnosis in a sample of U.S. military active‐duty Service women. Methods Active‐duty Service women (N = 134), 20–27 weeks gestation,
Ruby Schrag, Jason M. Lavender, Autumn Mains, Stephanie Rioux, Isabel Thorstad, Zoe Sinkford, Victoria Thomas, David A. Klein, Mark Haigney, Marian Tanofsky‐Kraff, Katherine A. Thompson   +10 more
wiley   +1 more source

A3CM: Automatic Capability Annotation for Android Malware

IEEE Access, 2019
Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of so ...
Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, Yu Wang, Yang Xiang   +6 more
doaj   +1 more source

A Neural Network-Based Approach for Cryptographic Function Detection in Malware

IEEE Access, 2020
Cryptographic technology has been commonly used in malware for hiding their static characteristics and malicious behaviors to avoid the detection of anti-virus engines and counter the reverse analysis from security researchers.
Li Jia, Anmin Zhou, Peng Jia, Luping Liu, Yan Wang, Liang Liu   +5 more
doaj   +1 more source

Data-Driven Malware Detection for 6G Networks: A Survey From the Perspective of Continuous Learning and Explainability via Visualisation

IEEE Open Journal of Vehicular Technology, 2023
5G is inherently prone to security vulnerabilities. We witness that many today's networks contain 5G security flaws due to their reliance on the existing 4G network core.
Dilara T. Uysal, Paul D. Yoo, Kamal Taha
doaj   +1 more source

Artificial Intelligence-Based Malware Detection, Analysis, and Mitigation

Symmetry, 2023
Malware, a lethal weapon of cyber attackers, is becoming increasingly sophisticated, with rapid deployment and self-propagation. In addition, modern malware is one of the most devastating forms of cybercrime, as it can avoid detection, make digital ...
Amir Djenna, A. Bouridane, Saddaf Rubab, Ibrahim Moussa Marou   +3 more
semanticscholar   +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