Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods
YL Khaleel, MA Habeeb, AS Albahri… - Journal of Intelligent …, 2024 - degruyter.com
This study aims to perform a thorough systematic review investigating and synthesizing
existing research on defense strategies and methodologies in adversarial attacks using …
existing research on defense strategies and methodologies in adversarial attacks using …
[HTML][HTML] A systematic literature review on windows malware detection: Techniques, research issues, and future directions
The aim of this systematic literature review (SLR) is to provide a comprehensive overview of
the current state of Windows malware detection techniques, research issues, and future …
the current state of Windows malware detection techniques, research issues, and future …
Deep malware detection framework for IoT-based smart agriculture
The advancement in smart agriculture through the Internet of Things (IoT) devices has
increased the risk of cyber-attacks. Most of the existing malware detection techniques are …
increased the risk of cyber-attacks. Most of the existing malware detection techniques are …
FCG-MFD: Benchmark function call graph-based dataset for malware family detection
Cyber crimes related to malware families are on the rise. This growth persists despite the
prevalence of various antivirus software and approaches for malware detection and …
prevalence of various antivirus software and approaches for malware detection and …
IIoT malware detection using edge computing and deep learning for cybersecurity in smart factories
H Kim, K Lee - Applied Sciences, 2022 - mdpi.com
The smart factory environment has been transformed into an Industrial Internet of Things
(IIoT) environment, which is an interconnected and open approach. This has made smart …
(IIoT) environment, which is an interconnected and open approach. This has made smart …
Global-local attention-based butterfly vision transformer for visualization-based malware classification
MM Belal, DM Sundaram - IEEE Access, 2023 - ieeexplore.ieee.org
In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic
techniques for visualization-based malware classification and detection. Though vision …
techniques for visualization-based malware classification and detection. Though vision …
[HTML][HTML] An enhanced static taint analysis approach to detect input validation vulnerability
The detection of feasible paths helps to minimize the false positive rate. However, the
previous works did not consider the feasibility of the program paths during the analysis …
previous works did not consider the feasibility of the program paths during the analysis …
[HTML][HTML] Using 3D-VGG-16 and 3D-Resnet-18 deep learning models and FABEMD techniques in the detection of malware
W Al-Khater, S Al-Madeed - Alexandria Engineering Journal, 2024 - Elsevier
Currently, the detection of malware to prevent cybersecurity breaches is a raising a concern
for millions of people around the globe. Even with the most recent updates, antivirus …
for millions of people around the globe. Even with the most recent updates, antivirus …
[HTML][HTML] A novel machine learning approach for detecting first-time-appeared malware
Conventional malware detection approaches have the overhead of feature extraction, the
requirement of domain experts, and are time-consuming and resource-intensive. Learning …
requirement of domain experts, and are time-consuming and resource-intensive. Learning …
[HTML][HTML] Android Malware Detection and Identification Frameworks by Leveraging the Machine and Deep Learning Techniques: A Comprehensive Review
The ever-increasing growth of online services and smart connectivity of devices have posed
the threat of malware to computer system, android-based smart phones, Internet of Things …
the threat of malware to computer system, android-based smart phones, Internet of Things …