A review on challenges and future research directions for machine learning-based intrusion detection system

A Thakkar, R Lohiya - Archives of Computational Methods in Engineering, 2023 - Springer
Research in the field of Intrusion Detection is focused on developing an efficient strategy that
can identify network attacks. One of the important strategies is to supervise the network …

Survey on Unified Threat Management (UTM) Systems for Home Networks

A Siddiqui, BP Rimal, M Reisslein… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Home networks increasingly support important networked applications with limited
professional network administration support, while sophisticated attacks pose enormous …

[HTML][HTML] Enhancing IoT network security through deep learning-powered Intrusion Detection System

SA Bakhsh, MA Khan, F Ahmed, MS Alshehri, H Ali… - Internet of Things, 2023 - Elsevier
The rapid growth of the Internet of Things (IoT) has brought about a global concern for the
security of interconnected devices and networks. This necessitates the use of efficient …

[HTML][HTML] An intrusion detection system using BoT-IoT

S Alosaimi, SM Almutairi - Applied Sciences, 2023 - mdpi.com
The rapid growth of the Internet of Things (IoT) has led to an increased automation and
interconnectivity of devices without requiring user intervention, thereby enhancing the …

[HTML][HTML] Anomaly detection in IoT-based healthcare: machine learning for enhanced security

MM Khan, M Alkhathami - Scientific Reports, 2024 - nature.com
Abstract Internet of Things (IoT) integration in healthcare improves patient care while also
making healthcare delivery systems more effective and economical. To fully realize the …

Fusion of linear and non-linear dimensionality reduction techniques for feature reduction in LSTM-based Intrusion Detection System

A Thakkar, N Kikani, R Geddam - Applied Soft Computing, 2024 - Elsevier
Securing networks is becoming increasingly crucial due to the widespread use of
information technology. Intrusion Detection System (IDS) plays a crucial role in network …

[HTML][HTML] Malicious traffic identification with self-supervised contrastive learning

J Yang, X Jiang, G Liang, S Li, Z Ma - Sensors, 2023 - mdpi.com
As the demand for Internet access increases, malicious traffic on the Internet has soared
also. In view of the fact that the existing malicious-traffic-identification methods suffer from …

Leveraging Gametic Heredity in Oversampling Techniques to Handle Class Imbalance for Efficient Cyberthreat Detection in IIoT

P Verma, JG Breslin, D O'Shea, N Mehta… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In recent years Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoT) have
gained significant attraction; however, it remains a vulnerable target for cyberattacks …

Recent endeavors in machine learning-powered intrusion detection systems for the Internet of Things

D Manivannan - Journal of Network and Computer Applications, 2024 - Elsevier
The significant advancements in sensors and other resource-constrained devices, capable
of collecting data and communicating wirelessly, are poised to revolutionize numerous …

CLSA: contrastive learning-based survival analysis for popularity prediction in MEC networks

Z Hajiakhondi-Meybodi, A Mohammadi… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Mobile-edge caching (MEC) integrated with deep neural networks (DNNs) is an innovative
technology with significant potential for the future generation of wireless networks, resulting …