Results 71 to 80 of about 29,821 (199)

File Entropy Signal Analysis Combined With Wavelet Decomposition for Malware Classification

IEEE Access, 2020
With the rapid development of the Internet, malware variants have increased exponentially, which poses a key threat to cyber security. Persistent efforts have been made to classify malware variants, but there are still many challenges, including the ...
Hui Guo, Shuguang Huang, Cheng Huang, Zulie Pan, Min Zhang, Fan Shi   +5 more
doaj   +1 more source

Generating Pattern‐Based Datasets for Cyber Attack Detection Using Machine‐Learning Techniques

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
The aim of this work is to review the state of the art in the design, generation, and labeling of attack pattern datasets for training of detection systems based on machine learning. ABSTRACT This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on ...
Pedro Díaz García, Diego López Abril, Javier Bermejo Higuera, Víctor Gonzalez Llorente, Miguel Angel Sicilia Urbán   +4 more
wiley   +1 more source

Malware Classification Using Ensemble Classifiers [PDF]

Advanced Science Letters, 2018
Antimalware offers detection mechanism to detect and take appropriate action against malware detected. To evade detection, malware authors had introduced polymorphism to malware. In order to be effectively analyzing and classifying large amount of malware, it is necessary to group and identify them into their corresponding families.
Mohd Hanafi Ahmad Hijazi, Tan Choon Beng, Lim, Yuto, Kashif Nisar, James Mountstephen   +4 more
openaire   +1 more source

MCPDS: image-based malware classification method using PE metadata alone

Cybersecurity
In response to the increasing threat posed by the exponential growth of malware in cybersecurity, researchers have developed a number of malware classification methods based on malware images and deep learning in recent years.
Yonglin Zhao, Chun Guo, Yuan Ping, Yi Chen, Yunhe Cui, Guowei Shen   +5 more
doaj   +1 more source

Accelerated‐USE: A Benchmark Framework for GPU‐Driven Graph Neural Network Training

Concurrency and Computation: Practice and Experience, Volume 38, Issue 9, May 2026.
ABSTRACT Graph processing is used in many domains to extract knowledge from real‐world data. With the rise of deep neural networks and scaled compute infrastructure in artificial intelligence (AI), specialized techniques emerged to leverage graphs in applications such as recommendation systems and social networks.
Lucas de Angelo Martins Ribeiro, Willian Borges de Lemos, Vanessa Telles da Silva, Marcelo de Gomensoro Malheiros, Nelson Lopes Duarte Filho, Marcelo Rita Pias   +5 more
wiley   +1 more source

Classification of Malware Images Using Fine-Tunned ViT

Sakarya University Journal of Computer and Information Sciences
Malware detection and classification have become critical tasks in ensuring the security and integrity of computer systems and networks. Traditional methods of malware analysis often rely on signature-based approaches, which struggle to cope with the ...
Özal Yıldırım, Oğuzhan Katar
doaj   +1 more source

CAR‐T Cells: Current Status, Challenges, and Future Prospects

MedComm, Volume 7, Issue 5, May 2026.
This graphical abstract outlines the current status, challenges, and future prospects of CAR‐T cells. The biological basis of CAR‐T cell therapy is the elegant redirection of adaptive immunity. Its initial successes have exposed a landscape of multifaceted challenges.
Aya Sedky Adly, Guillaume Cartron, Afnan Sedky Adly, Jean‐Christophe Egea, Pierre‐Yves Collart Dutilleul, Mahmoud Sedky Adly, Martin Villalba   +6 more
wiley   +1 more source

RogueGPT: Unleashing Jailbreak Prompts on LLMs

Engineering Reports, Volume 8, Issue 4, April 2026.
ABSTRACT Large Language Models (LLMs) have seen a remarkable surge in popularity since the latter part of 2022. These models have become vital in the lives of individuals from varying professions. While some users leverage LLMs for academic or informational purposes, others exploit them for illicit activities.
Arpitha Shivaswaroopa, Vanshika Sood, H. L. Gururaj, J. Shreyas, Fadi Al‐Turjman   +4 more
wiley   +1 more source

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