Results 81 to 90 of about 20,046 (244)

Towards autonomous device protection using behavioural profiling and generative artificial intelligence

IET Cyber-Physical Systems: Theory &Applications, Volume 10, Issue 1, January/December 2025.
The article proposes a novel concept of autonomous device protection based on behavioural profiling by continuously monitoring internal resource usage and exploiting a large language model to distinguish between benign and malicious behaviour. Abstract Demand for autonomous protection in computing devices cannot go unnoticed, considering the rapid ...
Sandeep Gupta, Bruno Crispo
wiley   +1 more source

Bioinspired artificial intelligence based android malware detection and classification for cybersecurity applications

Alexandria Engineering Journal
With the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps; thus, it develops the aim of malware that uses the vulnerability of ...
Shoayee Dlaim Alotaibi, Bayan Alabduallah, Yahia Said, Siwar Ben Haj Hassine, Abdulaziz A. Alzubaidi, Maha Alamri, Samah Al Zanin, Jihen Majdoubi   +7 more
doaj   +1 more source

A Systematic Review of Sensor Vulnerabilities and Cyber‐Physical Threats in Industrial Robotic Systems

IET Cyber-Physical Systems: Theory &Applications, Volume 10, Issue 1, January/December 2025.
This paper addresses a critical gap in the literature of industrial robotics cybersecurity by presenting a comprehensive analysis of vulnerabilities in the sensing systems of industrial robots. In particular, we systematically explore how sensor performance limits, faults and biases can be exploited by attackers who can then turn these inherent ...
Abdul Kareem Shaik, Alireza Mohammadi, Hafiz Malik   +2 more
wiley   +1 more source

AEDroid: Adaptive Enhanced Android Malware Detection‐Based on Interpretability of Deep Learning

IET Information Security, Volume 2025, Issue 1, 2025.
As the most widely used operating system in the world, Android has naturally become the main target of malicious hackers. The current research on Android malware detection relies on manually defined sensitive API feature sets. With the continuous innovation and change of malicious behavior, new threats and attack methods have emerged.
Pengfei Liu, Guangquan Xu, Jian Sun, Wenxia Wang, Jie Chen, Peican Zhu   +5 more
wiley   +1 more source

Android Malware Detection Based on Feature Selection and Weight Measurement [PDF]

, 2022
Huizhong Sun, Guosheng Xu, Zhimin Wu, Ruijie Quan   +3 more
openalex   +1 more source

ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO

Cybernetics and Information Technologies
The Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown.
Nwasra Nedal, Daoud Mohammad, Qaisar Zahid Hussain   +2 more
doaj   +1 more source

Backdoor Attack and Defense Methods for AI–Based IoT Intrusion Detection System

IET Information Security, Volume 2025, Issue 1, 2025.
The Internet of Things (IoT) is an emerging technology that has attracted significant attention and triggered a technical revolution in recent years. Numerous IoT devices are directly connected to the physical world, such as security cameras and medical equipment, making IoT security a critical issue.
Bowen Ma, Jiangwei Shi, Ning Zhu, Chen Fang, Yongjin Hu, Bohui Wang   +5 more
wiley   +1 more source

AMALGAN: Image‐Based Android Malware Classification Using Generative Adversarial Network

The Journal of Engineering
The Android malware detection process requires analysing numerous files to ensure system security. Malware can also be embedded in media files and images.
Zahid Hussain Qaisar, Muhammad Faheem, Muhammad Waqar Ashraf   +2 more
doaj   +1 more source

XAI and Android Malware Models

Android malware detection based on machine learning (ML) and deep learning (DL) models is widely used for mobile device security. Such models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such learning models make decisions. As a result, these popular malware detection strategies are generally
Maithili Kulkarni, Mark Stamp
openaire   +2 more sources

Feature Graph Construction With Static Features for Malware Detection

IET Information Security, Volume 2025, Issue 1, 2025.
Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion‐based detection methods generally overlook the correlation between features. And mere concatenation of features will reduce the model’s characterization ability, lead to low detection accuracy. Moreover, these
Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun, Stelvio Cimato   +7 more
wiley   +1 more source