Results 81 to 90 of about 81,305 (190)

History of malware

, 2013
In past three decades almost everything has changed in the field of malware and malware analysis. From malware created as proof of some security concept and malware created for financial gain to malware created to sabotage infrastructure. In this work we
Milošević, Nikola
core  

Lightweight and Explainable Early Ransomware Detection Using Dynamic API‐Call Features and Ensemble Machine Learning

Engineering Reports, Volume 8, Issue 4, April 2026.
This study presents a lightweight and explainable ransomware detection framework using dynamic API‐call features and ensemble machine learning. The LightGBM model achieves high accuracy (AUC = 0.9937) with low false positives. SHAP‐based feature importance reveals key API behaviors enabling interpretable, efficient, and deployment‐ready ransomware ...
Zain ul Abideen Khan, Muhammad Abid Mughal, Muhammad Usman Hashmi, Raheem Sarwar, Irfan ul Haq   +4 more
wiley   +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, Volume 11, Issue 2, Page 447-463, April 2026.
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

Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques

Journal of Cybersecurity and Privacy
Banking malware poses a significant threat to users by infecting their computers and then attempting to perform malicious activities such as surreptitiously stealing confidential information from them.
Mohamed Ali Kazi
doaj   +1 more source

Understanding the Autonomous Electric Vehicle Cyber Threat Landscape: A Focus on Infrastructure, Threats and Ontology‐Based Modelling

Energy Internet, Volume 3, Issue 1, Page 39-51, April 2026.
ABSTRACT The development of autonomous electric vehicles (AEVs) represents the convergence of two simultaneous automotive revolutions: electric vehicles (EVs) and autonomous vehicles (AVs). AVs require sensors, decision‐making systems and actuation systems to achieve autonomous driving, whereas EVs require intelligent management and real‐time ...
Ohud Alsadi, Abdulrahman Almijlad, Trinadh Pamulapati, Rajiv Ranjan, Devki Jha, Tejal Shah   +5 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

DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis

Applied Sciences, 2020
Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of ...
Xin Su, Lijun Xiao, Wenjia Li, Xuchong Liu, Kuan-Ching Li, Wei Liang   +5 more
doaj   +1 more source

Testing Android Anti-Malware against Malware Obfuscations

International Journal of Computer Applications, 2015
is an increasing threat of malware on mobile. Since Android is the most popular and maximum sold mobile phone, the malware attack on Android mobile is increasing day by day. The commercial antimalware products available in the market can detect common and old malwares easily.
Aruna Gupta, Gunjan Kapse
openaire   +1 more source

MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification

IEEE Access
Malware classification with supervised learning requires a large dataset, which needs an expensive and time-consuming labeling process. In this paper, we explore the efficacy of self-supervised learning techniques for malware classification.
Setia Juli Irzal Ismail, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source

A Data Mining Classification Approach for Behavioral Malware Detection

Journal of Computer Networks and Communications, 2016
Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.
Monire Norouzi, Alireza Souri, Majid Samad Zamini   +2 more
doaj   +1 more source