Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …
been remarkable. Deep learning, in particular, has been extensively used to drive …
Iot malware analysis using federated learning: A comprehensive survey
The Internet of Things (IoT) has paved the way to a highly connected society where all things
are interconnected and exchanging information has become more accessible through the …
are interconnected and exchanging information has become more accessible through the …
A knowledge transfer-based semi-supervised federated learning for IoT malware detection
As the demand for Internet of Things (IoT) technologies continues to grow, IoT devices have
been viable targets for malware infections. Although deep learning-based malware …
been viable targets for malware infections. Although deep learning-based malware …
A Multi-View attention-based deep learning framework for malware detection in smart healthcare systems
Recent security attack reports show that the number of malware attacks is gradually growing
over the years due to the rapid adoption of smart healthcare systems. The development of a …
over the years due to the rapid adoption of smart healthcare systems. The development of a …
Deep learning for zero-day malware detection and classification: A survey
Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …
malware software can catch it. This novelty and the lack of existing mitigation strategies …
[HTML][HTML] Adversarial machine learning in industry: A systematic literature review
Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …
[HTML][HTML] Tools and Techniques for Collection and Analysis of Internet-of-Things malware: A systematic state-of-art review
IoT devices which include wireless sensors, software, actuators, and computer devices
operated through the Internet, enable the transfer of data among objects or people …
operated through the Internet, enable the transfer of data among objects or people …
A GNN-Based adversarial internet of things Malware Detection Framework for critical infrastructure: Studying Gafgyt, Mirai and Tsunami campaigns
B Esmaeili, A Azmoodeh… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Significant advancement in Deep learning (DL) has turned it into an integral part of robust
approaches for addressing cybersecurity problems in both current and aging infrastructures …
approaches for addressing cybersecurity problems in both current and aging infrastructures …
Cfgexplainer: Explaining graph neural network-based malware classification from control flow graphs
With the ever increasing threat of malware, extensive research effort has been put on
applying Deep Learning for malware classification tasks. Graph Neural Networks (GNNs) …
applying Deep Learning for malware classification tasks. Graph Neural Networks (GNNs) …
Hsesr: Hierarchical software execution state representation for ultra-low-latency threat alerting over internet of things
To reduce attack risks in Internet of Things (IoT), many security vendors conduct software
security analysis on IoT devices all the time. However, how to build an ultralow-latency …
security analysis on IoT devices all the time. However, how to build an ultralow-latency …