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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

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

Mi-maml: classifying few-shot advanced malware using multi-improved model-agnostic meta-learning

Cybersecurity
Malware classification has been successful in utilizing machine learning methods. However, it is limited by the reliance on a large number of high-quality labeled datasets and the issue of overfitting. These limitations hinder the accurate classification
Yulong Ji, Kunjin Zou, Bin Zou
doaj   +1 more source

Efficient malware detection using NLP and deep learning model

Alexandria Engineering Journal
Malware has emerged as a significant challenge in contemporary society, growing in tandem with technological advancements. Consequently, the classification of malware has become a pressing concern for various services.
Umesh Gupta, Shubham Kandpal, Hayam Alamro, Mashael M. Asiri, Meshari H. Alanazi, Ali M. Al-Sharafi, Shaymaa Sorour   +6 more
doaj   +1 more source

MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention

Array
As one of the major threats in cybersecurity, malware has been growing continuously and steadily. In recent years, researchers have proposed a number of graph representation learning based malware detection methods by leveraging the intrinsic topological
Ruisheng Li, Qilong Zhang, Huimin Shen
doaj   +1 more source

An empirical study of problems and evaluation of IoT malware classification label sources

Journal of King Saud University: Computer and Information Sciences
With the proliferation of malware on IoT devices, research on IoT malicious code has also become more mature. Most studies use learning models to detect or classify malware.
Tianwei Lei, Jingfeng Xue, Yong Wang, Thar Baker, Zequn Niu   +4 more
doaj   +1 more source

Effective and Reliable Malware Group Classification for a Massive Malware Environment

International Journal of Distributed Sensor Networks, 2016
Most of the cyber-attacks are caused by malware, and damage from them has escalated from cyber space to home appliances and infrastructure, thus affecting the daily living of the people. As such, anticipative analysis and countermeasures for malware have
Taejin Lee, Jin Kwak
doaj   +1 more source

Image-based Malware Classification: A Space Filling Curve Approach

2019 IEEE Symposium on Visualization for Cyber Security (VizSec), 2019
Anti-virus (AV) software is effective at distinguishing between benign and malicious programs yet lack the ability to effectively classify malware into their respective family classes. AV vendors receive considerably large volumes of malicious programs daily and so classification is crucial to quickly identify variants of existing malware that would ...
openaire   +3 more sources

AN EFFECTIVE MALWARE CLASSIFICATION METHOD BASED ON BYTE-TO-IMAGE TRANSFORMATION AND INTEGRATION OF THE VISION TRANSFORMER MODEL

Tạp chí Khoa học
Malware classification is a critical problem in cybersecurity, characterized by numerous challenges due to the complexity and diversity of malware variants. In this study, we propose a novel approach that transforms bytecode into image representations
Nguyen Thi Thu Thuy*, Do Thi Hong Linh, Hoang Thi Hong Ha, Pham Thi Cuc, Pham Anh Binh
doaj   +1 more source

Exploring Timeline-Based Malware Classification [PDF]

, 2013
Over the decades or so, Anti-Malware (AM) communities have been faced with a substantial increase in malware activity, including the development of ever-more-sophisticated methods of evading detection. Researchers have argued that an AM strategy which is successful in a given time period cannot work at a much later date due to the changes in malware ...
Islam, Rafiqul, Altas, Irfan, Islam, Md Saiful   +2 more
openaire   +3 more sources