A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis
Intrusion detection (ID) is critical in securing computer networks against various malicious
attacks. Recent advancements in machine learning (ML), deep learning (DL), federated …
attacks. Recent advancements in machine learning (ML), deep learning (DL), federated …
The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review
Given the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity
experts are seriously considering artificial intelligence as a potential field that can aid …
experts are seriously considering artificial intelligence as a potential field that can aid …
An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset
H Zhang, L Huang, CQ Wu, Z Li - Computer Networks, 2020 - Elsevier
Abstract Network Intrusion Detection System (NIDS) is a key security device in modern
networks to detect malicious activities. However, the problem of imbalanced class …
networks to detect malicious activities. However, the problem of imbalanced class …
Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features
PR Kanna, P Santhi - Knowledge-Based Systems, 2021 - Elsevier
Intrusion detection systems (IDS) differentiate the malicious entries from the legitimate
entries in network traffic data and helps in securing the networks. Deep learning algorithms …
entries in network traffic data and helps in securing the networks. Deep learning algorithms …
Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network
Y Yang, K Zheng, C Wu, Y Yang - Sensors, 2019 - mdpi.com
Intrusion detection systems play an important role in preventing security threats and
protecting networks from attacks. However, with the emergence of unknown attacks and …
protecting networks from attacks. However, with the emergence of unknown attacks and …
A hybrid intrusion detection system based on sparse autoencoder and deep neural network
KN Rao, KV Rao, PR PVGD - Computer Communications, 2021 - Elsevier
A large number of attacks are launched daily in the era of the internet and with a large
number of users. Nowadays, effective detection of numerous attacks using the Intrusion …
number of users. Nowadays, effective detection of numerous attacks using the Intrusion …
IMIDS: An intelligent intrusion detection system against cyber threats in IoT
The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily
lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for …
lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for …
Network intrusion detection based on supervised adversarial variational auto-encoder with regularization
Y Yang, K Zheng, B Wu, Y Yang, X Wang - IEEE access, 2020 - ieeexplore.ieee.org
To explore the advantages of adversarial learning and deep learning, we propose a novel
network intrusion detection model called SAVAER-DNN, which can not only detect known …
network intrusion detection model called SAVAER-DNN, which can not only detect known …
Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks
Y Yang, K Zheng, C Wu, X Niu, Y Yang - Applied Sciences, 2019 - mdpi.com
Featured Application The model proposed in this paper can be deployed to the enterprise
gateway, dynamically monitor network activities, and connect with the firewall to protect the …
gateway, dynamically monitor network activities, and connect with the firewall to protect the …
An ensemble of prediction and learning mechanism for improving accuracy of anomaly detection in network intrusion environments
The connectivity of our surrounding objects to the internet plays a tremendous role in our
daily lives. Many network applications have been developed in every domain of life …
daily lives. Many network applications have been developed in every domain of life …