Results 71 to 80 of about 10,938 (187)

Data‐Based Detection of Antagonistic Agents in a Robot Swarm Solving a Dynamic Coverage Task

International Journal of Mechanical System Dynamics, Volume 6, Issue 1, Page 22-32, March 2026.
ABSTRACT Robot swarms can be deployed as moving surveillance systems, for instance, as mobile anti‐poaching systems for monitoring wildlife and detecting poaching activities. Since poachers have an interest in evading detection, robots are at risk of being hijacked and manipulated to behave antagonistically, for example, to prevent the correct ...
Ingeborg Wenger, Peter Eberhard, Henrik Ebel   +2 more
wiley   +1 more source

RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification

IEEE Access
Malware analysis and detection are still essential for maintaining the security of networks and computer systems, even as the threat landscape shifts.
S. Puneeth, Shyam Lal, Mahendra Pratap Singh, B. S. Raghavendra   +3 more
doaj   +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

Generative Adversarial Network for Global Image-Based Local Image to Improve Malware Classification Using Convolutional Neural Network

Applied Sciences, 2020
Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it.
Sejun Jang, Shuyu Li, Yunsick Sung
doaj   +1 more source

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 Machine Learning Framework for Detecting and Preventing Cyber‐Attacks in Industrial Cyber‐Physical Systems

Engineering Reports, Volume 8, Issue 1, January 2026.
Proposed cyber physical system security framework. ABSTRACT The increasing adoption of cyber‐physical systems (CPS) in Industry 4.0 has heightened vulnerability to cyber threats. This study proposes a machine learning–based intrusion detection framework, DBID‐Net, to effectively identify and prevent attacks in CPS environments. The framework integrates
Anurag Sinha, Rejuwan Shamim, Mihika Mahendra, Trupti Mohota, G. Madhukar Rao, Mohammad Nadeem Ahmed, Mohammad Rashid Hussain, Satyam Solanki, Tanish Ranjan, Bhaskar, Mini Kumari, Karan Dixit, Vyom Modi, Syed Immamul Ansarullah, Mohd Asif Shah   +14 more
wiley   +1 more source

Efficient Malware Classification using Transfer Learning and Stacked Ensemble Techniques [PDF]

International Journal of Mathematical, Engineering and Management Sciences
The exponential growth of internet usage and communication devices has led to heightened security vulnerabilities, including the proliferation of malware such as viruses, ransomware, trojans, and spyware. These increasingly sophisticated malware variants
Krishna Kumar, Hardwari Lal Mandoria, Rajeev Singh   +2 more
doaj   +1 more source

Explainable AI With Imbalanced Learning Strategies for Blockchain Transaction Fraud Detection

Engineering Reports, Volume 8, Issue 1, January 2026.
Research methodology pipeline for blockchain fraud detection. ABSTRACT Blockchain networks now support billions of dollars in daily transactions, making reliable and transparent fraud detection essential for maintaining user trust and financial stability.
Ahmed Abbas Jasim Al‐Hchaimi, M. A. Khalifa, Walid El‐Shafai   +2 more
wiley   +1 more source

GRASE: Granulometry Analysis With Semi Eager Classifier to Detect Malware.

International Journal of Interactive Multimedia and Artificial Intelligence
Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT).
Mahendra Deore, Manoj Tarambale, Jambi Ratna Raja Kumar, Sachin Sakhare   +3 more
doaj   +1 more source

Robust Intelligent Malware Detection Using Deep Learning

IEEE Access, 2019
Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks ...
R. Vinayakumar, Mamoun Alazab, K. P. Soman, Prabaharan Poornachandran, Sitalakshmi Venkatraman   +4 more
doaj   +1 more source