A survey and analysis of intrusion detection models based on cse-cic-ids2018 big data
The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
A hybrid deep learning-driven SDN enabled mechanism for secure communication in Internet of Things (IoT)
The Internet of Things (IoT) has emerged as a new technological world connecting billions of
devices. Despite providing several benefits, the heterogeneous nature and the extensive …
devices. Despite providing several benefits, the heterogeneous nature and the extensive …
A systematic review on anomaly detection for cloud computing environments
The detection of anomalies in data is a far-reaching field of research which also applies to
the field of cloud computing in several different ways: from the detection of various types of …
the field of cloud computing in several different ways: from the detection of various types of …
A hybrid framework for intrusion detection in healthcare systems using deep learning
M Akshay Kumaar, D Samiayya… - Frontiers in Public …, 2022 - frontiersin.org
The unbounded increase in network traffic and user data has made it difficult for network
intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e …
intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e …
AutoLog: Anomaly detection by deep autoencoding of system logs
The use of system logs for detecting and troubleshooting anomalies of production systems
has been known since the early days of computers. In spite of the advances in the area, the …
has been known since the early days of computers. In spite of the advances in the area, the …
A hybrid intrusion detection system based on feature selection and weighted stacking classifier
R Zhao, Y Mu, L Zou, X Wen - IEEE Access, 2022 - ieeexplore.ieee.org
Cyber-attacks occur more frequently with the rapid growth in the Internet. Intrusion detection
systems (IDS) have become an important part of protecting system security. There are still …
systems (IDS) have become an important part of protecting system security. There are still …
Toward efficient intrusion detection system using hybrid deep learning approach
A Aldallal - Symmetry, 2022 - mdpi.com
The increased adoption of cloud computing resources produces major loopholes in cloud
computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital …
computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital …
Network anomaly detection with temporal convolutional network and U-Net model
Anomaly detection in network traffic is one of the key techniques to ensure security in future
networks. Today, the importance of this topic is even higher, since the network traffic is …
networks. Today, the importance of this topic is even higher, since the network traffic is …
Paying attention to cyber-attacks: A multi-layer perceptron with self-attention mechanism
Cyber-attacks cause huge monetary losses to the institutions that are victims of them. Cyber-
attack is becoming increasingly sophisticated. Therefore, the protection system against …
attack is becoming increasingly sophisticated. Therefore, the protection system against …
Benchmarking deep learning methods for behaviour-based network intrusion detection
Network security encloses a wide set of technologies dealing with intrusions detection.
Despite the massive adoption of signature-based network intrusion detection systems …
Despite the massive adoption of signature-based network intrusion detection systems …