Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions
N Moustafa, N Koroniotis, M Keshk… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The field of Explainable Artificial Intelligence (XAI) has garnered considerable research
attention in recent years, aiming to provide interpretability and confidence to the inner …
attention in recent years, aiming to provide interpretability and confidence to the inner …
A deep learning-based intrusion detection system for MQTT enabled IoT
A large number of smart devices in Internet of Things (IoT) environments communicate via
different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely …
different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely …
RTIDS: A robust transformer-based approach for intrusion detection system
Z Wu, H Zhang, P Wang, Z Sun - IEEE Access, 2022 - ieeexplore.ieee.org
Due to the rapid growth in network traffic and increasing security threats, Intrusion Detection
Systems (IDS) have become increasingly critical in the field of cyber security for providing …
Systems (IDS) have become increasingly critical in the field of cyber security for providing …
STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment
The ability to process large amounts of data in real time using big data analytics tools brings
many advantages that can be used in intrusion detection systems. Deep learning …
many advantages that can be used in intrusion detection systems. Deep learning …
The role of machine learning in network anomaly detection for cybersecurity
A Yaseen - Sage Science Review of Applied Machine …, 2023 - journals.sagescience.org
This research introduces a theoretical framework for network anomaly detection in
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …
Ae-mlp: A hybrid deep learning approach for ddos detection and classification
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …
connectivity massively grows in recent years. Conventional shallow machine learning-based …
An optimized ensemble prediction model using AutoML based on soft voting classifier for network intrusion detection
Traditional ML based IDS cannot handle high-speed and ever-evolving attacks.
Furthermore, these traditional IDS face several common challenges, such as processing …
Furthermore, these traditional IDS face several common challenges, such as processing …
Composition of hybrid deep learning model and feature optimization for intrusion detection system
Recently, with the massive growth of IoT devices, the attack surfaces have also intensified.
Thus, cybersecurity has become a critical component to protect organizational boundaries …
Thus, cybersecurity has become a critical component to protect organizational boundaries …
Detection of real-time malicious intrusions and attacks in IoT empowered cybersecurity infrastructures
Computer viruses, malicious, and other hostile attacks can affect a computer network.
Intrusion detection is a key component of network security as an active defence technology …
Intrusion detection is a key component of network security as an active defence technology …
[HTML][HTML] An empirical assessment of ensemble methods and traditional machine learning techniques for web-based attack detection in industry 5.0
Cybersecurity attacks that target software have become profitable and popular targets for
cybercriminals who consciously take advantage of web-based vulnerabilities and execute …
cybercriminals who consciously take advantage of web-based vulnerabilities and execute …