Results 61 to 70 of about 10,938 (187)

Transfer Learning for Image-Based Malware Detection for IoT

Sensors, 2023
The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household.
Pratyush Panda, Om Kumar C U, Suguna Marappan, Suresh Ma, Manimurugan S, Deeksha Veesani Nandi   +5 more
doaj   +1 more source

Art of singular vectors and universal adversarial perturbations

, 2017
Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has been attracting a lot of attention in recent studies. It has been shown that for many state of the art DNNs performing image classification there exist universal adversarial ...
Khrulkov, Valentin, Oseledets, Ivan
core   +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

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

, 2019
Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social ...
Biggio, Battista, Giacinto, Giorgio, Maiorca, Davide   +2 more
core   +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

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

Method of anti-confusion texture feature descriptor for malware images

Tongxin xuebao, 2018
It is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was ...
Yashu LIU, Zhihai WANG, Hanbing YAN, Yueran HOU, Yukun LAI   +4 more
doaj   +2 more sources

Tomtit‐Raven Evolutionary Selector‐Reinforced Attention‐Driven: A High‐Performance and Computationally Efficient Cyber Threat Detection Framework for Smart Grids

Energy Science &Engineering, Volume 14, Issue 3, Page 1431-1455, March 2026.
Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga, Praveen Kumar Balachandran, Umawathy Techanamurthy, Muhammad Ammirrul Atiqi Mohd Zainuri   +3 more
wiley   +1 more source