Results 61 to 70 of about 46,394 (182)

Windows Malware Detection Under the Machine Learning Models and Neutrosophic Numbers [PDF]

Neutrosophic Sets and Systems
Significant cybersecurity risks are posed by malware assaults on Windows computers, which call for efficient detection and prevention systems.
Alber S. Aziz, Mohamed eassa, Ahmed Abdelhafeez, Ahmed A. Metwaly, Ashraf. M. Hussein , Nariman A. Khalil   +5 more
doaj   +1 more source

Enhanced Metamorphic Techniques-A Case Study Against Havex Malware

IEEE Access, 2021
Most of the commercial antiviruses are signature based, that is, they use existing database signature to detect the malware. Malware authors use code obfuscation techniques in their variety of malware with the aim of bypassing detection by antiviruses ...
Zainub Mumtaz, Mehreen Afzal, Waseem Iqbal, Waqas Aman, Naima Iltaf   +4 more
doaj   +1 more source

A Threat to Cyber Resilience : A Malware Rebirthing Botnet [PDF]

, 2011
This paper presents a threat to cyber resilience in the form of a conceptual model of a malware rebirthing botnet which can be used in a variety of scenarios. It can be used to collect existing malware and rebirth it with new functionality and signatures
Brand, Murray, Valli, Craig, Woodward, Andrew   +2 more
core   +2 more sources

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

An investigation of a deep learning based malware detection system

, 2018
We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware ...
David O. E., Kolosnjaji Bojan, Lin Chih-Ta, Pascanu R., Sahay Sanjay K., Siddiqui Muazzam, Srivastava Nitish   +6 more
core   +1 more source

AI‐Powered Anomaly Detection for Secure Internet of Things (IoT): Optimising XGBoost and Deep Learning With Bayesian Optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim, Lal Hussain, Mohammed A. Alqarni, Faisal S. Alsubaei, Rasha Jamal Atwah   +4 more
wiley   +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

TTGNet-AMD: Android malware detection based on multi-modal feature fusion [PDF]

PeerJ Computer Science
The application of static features for Android malware detection has been extensively studied and developed. Existing methods exhibit limitations in both the completeness and discriminability of feature representation, which affects the enhancement of ...
Jiayin Feng, Meiyu Wang, JinLing Song, Chenfeng Yang, Lingzu Zhang, Yang Shen   +5 more
doaj   +2 more sources

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

Cybersecurity Assurance for SMEs: A Conceptual Framework Integrating Organizational Culture, Fraud Risk Management and Forensic Accounting

Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration, Volume 43, Issue 1, March 2026.
ABSTRACT As digitalization accelerates across the global economy, small and medium enterprises (SMEs) face increasing exposure to cybersecurity threats, not due to flaws in external platforms, but because of internal organizational vulnerabilities. This paper presents a conceptual framework that integrates the resource‐based view (RBV) and dynamic ...
Ifedapo Francis Awolowo, Egena Ode, Adenike Abidoye, Oluwaseun Ajao, Olamide Jogunola   +4 more
wiley   +1 more source