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Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
A Khraisat, A Alazab - Cybersecurity, 2021 - Springer
Abstract The Internet of Things (IoT) has been rapidly evolving towards making a greater
impact on everyday life to large industrial systems. Unfortunately, this has attracted the …
impact on everyday life to large industrial systems. Unfortunately, this has attracted the …
Comparative analysis of intrusion detection systems and machine learning-based model analysis through decision tree
Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …
Machine learning technology in biodiesel research: A review
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
An improved anomaly detection model for IoT security using decision tree and gradient boosting
M Douiba, S Benkirane, A Guezzaz… - The Journal of …, 2023 - Springer
Abstract Internet of Things (IoT) represents a massive deployment of connected, intelligent
devices that communicate directly in private, public, and professional environments without …
devices that communicate directly in private, public, and professional environments without …
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 …
Survey of intrusion detection systems: techniques, datasets and challenges
Cyber-attacks are becoming more sophisticated and thereby presenting increasing
challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade …
challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade …
lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning
Smart cities are being enabled all around the world by Internet of Things (IoT) applications.
A smart city idea necessitates the integration of information and communication …
A smart city idea necessitates the integration of information and communication …
Anomaly-based intrusion detection from network flow features using variational autoencoder
The rapid increase in network traffic has recently led to the importance of flow-based
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …
Survey on SDN based network intrusion detection system using machine learning approaches
Abstract Software Defined Networking Technology (SDN) provides a prospect to effectively
detect and monitor network security problems ascribing to the emergence of the …
detect and monitor network security problems ascribing to the emergence of the …