Results 61 to 70 of about 1,405 (158)

Guardians of ICS: A Comparative Analysis of Anomaly Detection Techniques

IET Cyber-Physical Systems: Theory &Applications, Volume 11, Issue 1, January/December 2026.
This study presents a comparative evaluation of supervised and unsupervised learning models for anomaly detection in industrial control systems (ICS), using data from the SWaT testbed. Results show that although supervised models offer higher precision, they miss more unknown attacks, whereas unsupervised models achieve better recall but with increased
Zequn Wang, Muhammad Azmi Umer, Haibo Zhang, Naveed ul Hassan, Chuadhry Mujeeb Ahmed   +4 more
wiley   +1 more source

A Design‐Driven Machine Learning Approach for Invariant Mining in a Smart Grid

IET Cyber-Physical Systems: Theory &Applications, Volume 11, Issue 1, January/December 2026.
An ICS is vulnerable to cyber‐attacks arising from within its communication network or directly from the SCADA and devices such as PLCs. The study reported here presents a scenario‐specific invariant mining approach to detect anomalies in plant behaviour.
Danish Hudani, Muhammad Haseeb, Muhammad Taufiq, Muhammad Azmi Umer, Nandha Kumar Kandasamy, Aditya P. Mathur   +5 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

Design and Implementation of XGBoost‐Based Cyberattack Detection and Blockchain‐Enabled Charging Station

Digital Twins and Applications, Volume 3, Issue 1, January/December 2026.
The charging station (CS) plays a crucial role in charging electric vehicles. Therefore, it is necessary to protect the CS from cyberattacks. This paper proposes an architecture for the security of the EV fleet during charging using the XGBoost model and Hyperledger Fabric to protect battery management systems (BMS) from cyberattacks.
Gaurav Kumar, Suresh Mikkili
wiley   +1 more source

FedAnomDetect a Clustered Federated Learning Framework for Robust Anomaly Detection With Enhanced Privacy and Communication Efficiency

IET Information Security, Volume 2026, Issue 1, 2026.
Anomaly detection in distributed environments poses significant challenges, particularly in balancing privacy, communication overhead, and detection accuracy. This paper presents FedAnomDetect, a novel federated learning (FL‐based framework designed for anomaly detection across large‐scale, distributed systems.
Abeer Abdullah Alsadhan, Peican Zhu
wiley   +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

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

Profiling and Visualizing Android Malware Datasets

, 2022
Mobile devices are ubiquitous: nowadays most people own a mobile telephone.Because of this, it is a target of interest for attackers.Researchers in malware analysis put their effort to recognize these types of programs before they are installed on a user device.To do this, they perform experiments to automatically detect malware, for example with ...
openaire   +1 more source

Through the static: Demystifying malware visualization via explainability

Journal of Information Security and Applications
Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs) have emerged as vital tools for this task.
Brosolo, Matteo, P., Vinod, Conti, Mauro
openaire   +2 more sources

Deep visualization classification method for malicious code based on Ngram-TFIDF

Tongxin xuebao
With the continuous increase in the scale and variety of malware, traditional malware analysis methods, which relied on manual feature extraction, become time-consuming and error-prone, rendering them unsuitable.
WANG Jinwei, CHEN Zhengjia, XIE Xue, LUO Xiangyang, MA Bin   +4 more
doaj   +2 more sources