Results 91 to 100 of about 5,558 (210)

Android malware detection method based on deep neural network

网络与信息安全学报, 2020
Android is increasingly facing the threat of malware attacks. It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine, method for Android malware detection and ...
CHAO Fan, YANG Zhi, DU Xuehui, SUN Yan
doaj   +1 more source

A Comprehensive Systematic Review of Cyber‐Physical Systems Security: Threats, Challenges, and Defense in ICS/OT Environments

Engineering Reports, Volume 8, Issue 7, July 2026.
Overview of the paper organization, illustrating the hierarchical structure of cybersecurity domains in ICS and CPS, including attack analysis, security approaches, offensive tactics, career guidance, and concluding discussions. ABSTRACT The convergence of operational technology (OT) with IP‐based information systems has exposed industrial control ...
M. A. Khalifa, Ahmad Taher Azar, Walid El‐Shafai   +2 more
wiley   +1 more source

Classification of Malware Images Using Fine-Tunned ViT

Sakarya University Journal of Computer and Information Sciences
Malware detection and classification have become critical tasks in ensuring the security and integrity of computer systems and networks. Traditional methods of malware analysis often rely on signature-based approaches, which struggle to cope with the ...
Özal Yıldırım, Oğuzhan Katar
doaj   +1 more source

File Entropy Signal Analysis Combined With Wavelet Decomposition for Malware Classification

IEEE Access, 2020
With the rapid development of the Internet, malware variants have increased exponentially, which poses a key threat to cyber security. Persistent efforts have been made to classify malware variants, but there are still many challenges, including the ...
Hui Guo, Shuguang Huang, Cheng Huang, Zulie Pan, Min Zhang, Fan Shi   +5 more
doaj   +1 more source

Harnessing Generative AI Within Human‐Aligned Constraints: Exploring Control Strategies and Existential Considerations

Engineering Reports, Volume 8, Issue 7, July 2026.
We propose the Powerful‐but‐Limited Generative AI theorem, demonstrating that embedding human‐inspired constraints, such as fixed utility functions and neuro‐symbolic submission layers, ensures generative AI remains controllable by preventing self‐improvement beyond designer intent.
Saeed Banaeian Far, Mohammad Reza Chalak Qazani, Falah Younis Hamode Hamod, Azadeh Imani Rad   +3 more
wiley   +1 more source

Graph–Time IoT IDS: Requirement‐Aligned Impact Evaluation

Engineering Reports, Volume 8, Issue 7, July 2026.
A multi‐view intrusion detection framework (IMPACT‐MVG) combines temporal behavior modeling and graph‐based interaction analysis to detect IoT network attacks. Impact‐centric evaluation using the ICSec score shows that the approach reduces operational damage from intrusions while maintaining efficient, explainable, and privacy‐aware security monitoring.
Kumkum Dubey, Isha Batra, Abhishek Bajpai, Arun Malik, Mohammed Ali Alshara, Tariqullah Jan, Naveed Ahmad, Ruhul Amin Khalil   +7 more
wiley   +1 more source

A Comprehensive Review of AI‐Powered Energy Systems

Energy Science &Engineering, Volume 14, Issue 7, Page 3554-3580, July 2026.
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo, Pedram Asef, Masoumeh Javadi, Mousa Marzband, Shady S. Refaat, Muhyaddin Rawa   +5 more
wiley   +1 more source

GA‐ANN: An Efficient Hybrid Deep Learning Scheme for Network Intrusion Detection in IoT

SECURITY AND PRIVACY, Volume 9, Issue 4, July/August 2026.
ABSTRACT Intrusion detection systems (IDS) are critical to the security of the dynamic internet of things (IoT) environment. The integration of Artificial Intelligence (AI) into IDS has substantially improved network security. Particularly, deep learning techniques have shown strong potential in addressing IoT security challenges.
Naveed Ahmed, M. Siraj Rathore, Azhar Mahmood, Muhammad Daniyal, Md Asri Ngadi   +4 more
wiley   +1 more source

Malware Generation and Classification using PixelCNN [PDF]

Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems ...
Karumudi, Mounika Krishna Teja
core   +1 more source

MtNet: A Multi-Task Neural Network for Dynamic Malware Classification [PDF]

, 2020
. In this paper, we propose a new multi-task, deep learning architecture for malware classification for the binary (i.e. malware versus benign) malware classification task. All models are trained with data extracted from dynamic analysis of malicious and
Jack W Stokes, Wenyi Huang
core