Results 81 to 90 of about 5,547 (215)

Survey on Visualization of Information Diffusion over Networks

Computer Graphics Forum, EarlyView.
Abstract Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision‐making as well as ...
T. Baumgartl, M. Sondag, V. Filipov, M. Tuscher, S. Rajendran, S. Miksch, D. Archambault, A. Arleo, T. von Landesberger   +8 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

Cyberattacks on Small Banks and the Impact on Local Banking Markets

Journal of Money, Credit and Banking, EarlyView.
Abstract Cyberattacks on small banks have direct and spillover effects in local markets. Following successful cyberattacks, hacked small banks experience a decline in deposit growth rates. This effect of cyberattacks is not observed in hacked large banks.
FABIAN GOGOLIN, IVAN LIM, FRANCESCO VALLASCAS   +2 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

Fighting fire with fire – a Pre-emptive approach to restore control over IT assets from malware infection

, 2012
Malware is a major threat as they induce multiple risks to infected organizations. Current Anti-Malware solutions meant to keep Malware away are challenged on how to keep the risks at bay effectively. When a Malware manages to penetrate an organization’s
Pan, J.Y.
core  

Malware Classification using Km-SVM

, 2020
Malware identification and classification is a problem faced even in this decade. This is majorly due to the fact that advance malware are more sophisticated in nature and have state of the art abilities to remain hidden or change their code/behaviour ...
Ghorpade, Ashish
core  

Governing Cybersecurity in the Digital Age: Mapping Comprehensive Policy Mixes With the Nodality‐Authority‐Treasure‐Organization Lens

Review of Policy Research, Volume 43, Issue 4, July 2026.
ABSTRACT The accelerated digitalisation of society has amplified cybersecurity threats and revealed their cross‐sectoral nature. Yet, the policy instruments used to address these challenges remain insufficiently examined. This study conducts a scoping review of 980 academic articles (2007–2024) and applies Hood's NATO framework (Nodality, Authority ...
Benedetta Cotta, Maria Stella Righettini
wiley   +1 more source

Advancing Malware Classification With an Evolving Clustering Method

, 2018
This article describes how honeypots and intrusion detection systems serve as major mechanisms for security administrators to collect a variety of sample viruses and malware for further analysis, classification, and system protection.
Chia-Mei Chen, Shi-Hao Wang
core   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

Quantum Activation Functions in Neural Networks: A Systematic Review of Approaches, Performance Impacts, and Practical Applications

Advanced Quantum Technologies, Volume 9, Issue 6, June 2026.
This review explores how quantum activation functions can contribute to the evolution of neural networks toward quantum computing. The results show that classical‐quantum hybrid architectures are being tested in some practical applications, while fully quantum models are still in the development phase. These functions represent an important step toward
Petterson Pina dos Santos, Mônica Silva de Oliveira, Adonney Allan de Oliveira Veras   +2 more
wiley   +1 more source