A comprehensive survey on deep learning based malware detection techniques
M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …
harmful threats to the digital world. Malware mitigation techniques have evolved over the …
A systematic review of data-driven attack detection trends in IoT
The Internet of Things is perhaps a concept that the world cannot be imagined without today,
having become intertwined in our everyday lives in the domestic, corporate and industrial …
having become intertwined in our everyday lives in the domestic, corporate and industrial …
Improving IoT Security With Explainable AI: Quantitative Evaluation of Explainability for IoT Botnet Detection
Detecting botnets is an essential task to ensure the security of Internet of Things (IoT)
systems. Machine learning (ML)-based approaches have been widely used for this purpose …
systems. Machine learning (ML)-based approaches have been widely used for this purpose …
Modeling of botnet detection using chaotic binary Pelican Optimization Algorithm with deep learning on Internet of Things Environment
F Alrowais, MM Eltahir, SS Aljameel, R Marzouk… - IEEE …, 2023 - ieeexplore.ieee.org
Nowadays, there are ample amounts of Internet of Things (IoT) devices interconnected to the
networks, and with technological improvement, cyberattacks and security threads, for …
networks, and with technological improvement, cyberattacks and security threads, for …
Machine Learning Approaches for Botnet Detection in Network Traffic
YT Salih, A Fenjan, SR Ahmed, H Ali… - Proceedings of the …, 2024 - dl.acm.org
Botnets pose a significant challenge to network security, continually evolving and
threatening the integrity of digital infrastructure. Traditional botnet detection methodologies …
threatening the integrity of digital infrastructure. Traditional botnet detection methodologies …
A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software.
L Yuanming, R Latih - International Journal on Advanced …, 2024 - search.ebscohost.com
With the continuous development of technology, the types of malware and their variants
continue to increase, which has become an enormous challenge to network security. These …
continue to increase, which has become an enormous challenge to network security. These …
Enhancing IoT Botnet Attack Detection in SOCs with an Explainable Active Learning Framework
The widespread use of Internet of Things (IoT) devices has raised the threat of botnet
attacks, presenting significant challenges for security operations centres (SOCs). While …
attacks, presenting significant challenges for security operations centres (SOCs). While …
Process-aware security monitoring in industrial control systems: A systematic review and future directions
M ur Rehman, H Bahşi - International Journal of Critical Infrastructure …, 2024 - Elsevier
Due to the tight coupling between the cyber and physical components, control systems are
subjected to emerging cyberattacks. In addition to attacks based on networking and …
subjected to emerging cyberattacks. In addition to attacks based on networking and …
Machine learning enabled intrusion detection for edge devices in the Internet of Things
M Alsharif, DB Rawat - 2023 IEEE 13th Annual Computing and …, 2023 - ieeexplore.ieee.org
In this paper, we present recent approaches proposed to secure the Internet of Things (IoT)
devices against malicious cyber attacks and malware. As IoT devices have limited …
devices against malicious cyber attacks and malware. As IoT devices have limited …
Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge
A Nitish, J Hanumanthappa, SPS Prakash… - Computers & Security, 2024 - Elsevier
Heterogeneous networks (HetIoT) of high-capacity and resource-constrained IoT devices
and their edge associations for on-device distributed critical workloads—called the edge-of …
and their edge associations for on-device distributed critical workloads—called the edge-of …