Results 91 to 100 of about 97,198 (223)

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

Modeling and Stability Analysis of Malware Propagation in Hierarchically Protected WSNs Based on Epidemiological Theory

IET Control Theory &Applications, Volume 20, Issue 1, January/December 2026.
This paper proposes a novel malware propagation model based on epidemiological theory, specifically tailored for hierarchically protected wireless sensor networks (WSNs). We classify nodes into strongly and weakly protected categories and establish a four‐state propagation dynamics model (susceptible, exposed, infected, and recovered) to simulate ...
Xuejin Zhu, Nan Fu
wiley   +1 more source

Retracted: Analysis of Malware Detection and Signature Generation Using a Novel Hybrid Approach [PDF]

, 2023
Mathematical Problems in Engineering
openalex   +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

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

hybrid-Falcon: Hybrid Pattern Malware Detection and Categorization with Network Traffic and Program Code [PDF]

, 2021
Peng Xu, Claudia Eckert, Apostolis Zarras   +2 more
openalex   +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

MALWARE DETECTION BASED ON OPCODE FREQUENCY

, 2023
Elshan Baghirov
openalex   +1 more source

CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing

Sensors
With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare.
Wei Wu, Haipeng Peng, Haotian Zhu, Derun Zhang   +3 more
doaj   +1 more source

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