Results 71 to 80 of about 46,394 (182)

Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework

IEEE Access
The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data.
Faiza Babar Khan, Muhammad Hanif Durad, Asifullah Khan, Farrukh Aslam Khan, Muhammad Rizwan, Aftab Ali   +5 more
doaj   +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, Volume 59, Issue 3, Page 588-594, March 2026.
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

BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices [PDF]

PeerJ Computer Science
Due to the increase in the volume and diversity of malware targeting Android systems, research on detecting this harmful software is steadily growing. Traditional malware detection studies require significant human intervention and resource consumption ...
Emre Şafak, İbrahim Alper Doğru, Necaattin Barışçı, İsmail Atacak   +3 more
doaj   +2 more sources

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

Energy Science &Engineering, Volume 14, Issue 3, Page 1431-1455, March 2026.
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

Mission Aware Cyber‐Physical Security

Systems Engineering, Volume 29, Issue 2, Page 354-367, March 2026.
ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis, Bryan Carter, Cody H. Fleming, Carl R. Elks   +3 more
wiley   +1 more source

Malware traffic detection based on type II fuzzy recognition

Frontiers in Physics
In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit.
Weisha Zhang, Jiajia Liu, Jimin Peng, Qiang Liu, Kun Yu   +4 more
doaj   +1 more source

A machine learning technique for Android malicious attacks detection based on API calls [PDF]

Decision Science Letters
Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and ...
Mousa AL-Akhras, Saud Alghamdi, Hani Omar, Hazzaa Alshareef   +3 more
doaj   +1 more source

Veto-based Malware Detection

2012 Seventh International Conference on Availability, Reliability and Security, 2012
Malicious software (malware) represents a threatto the security and privacy of computer users. Traditionalsignature-based and heuristic-based methods are unsuccessfulin detecting some forms of malware. This paper presents amalware detection approach based on supervised learning.
Shahzad, Raja Khurram, Lavesson, Niklas
openaire   +4 more sources

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +3 more
wiley   +1 more source

A cybersecurity risk analysis framework for systems with artificial intelligence components

International Transactions in Operational Research, Volume 33, Issue 2, Page 798-825, March 2026.
Abstract The introduction of the European Union Artificial Intelligence (AI) Act, the NIST AI Risk Management Framework, and related international norms and policy documents demand a better understanding and implementation of novel risk analysis issues when facing systems with AI components: dealing with new AI‐related impacts; incorporating AI‐based ...
J.M. Camacho, A. Couce‐Vieira, D. Arroyo, D.R. Insua   +3 more
wiley   +1 more source