A survey on intrusion detection systems for fog and cloud computing
The rapid advancement of internet technologies has dramatically increased the number of
connected devices. This has created a huge attack surface that requires the deployment of …
connected devices. This has created a huge attack surface that requires the deployment of …
Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection …
To protect the network, resources, and sensitive data, the intrusion detection system (IDS)
has become a fundamental component of organizations that prevents cybercriminal …
has become a fundamental component of organizations that prevents cybercriminal …
Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey
The advancement of communication and internet technology has brought risks to network
security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious …
security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious …
An innovative perceptual pigeon galvanized optimization (PPGO) based likelihood Naïve Bayes (LNB) classification approach for network intrusion detection system
Intrusion detection and classification have gained significant attention recently due to the
increased utilization of networks. For this purpose, there are different types of Network …
increased utilization of networks. For this purpose, there are different types of Network …
Mitigating cyber threats through integration of feature selection and stacking ensemble learning: the LGBM and random forest intrusion detection perspective
AK Mishra, S Paliwal - Cluster Computing, 2023 - Springer
The network traffic has observed astounding expansion and is set to explode in the next few
years. Security attacks are becoming more and more synchronized as attackers are involved …
years. Security attacks are becoming more and more synchronized as attackers are involved …
Deep ensemble-based efficient framework for network attack detection
Nowadays, networks play a critical role in business, education, and daily life, allowing
people to communicate via different platforms across long distances. However, such …
people to communicate via different platforms across long distances. However, such …
Using autoencoders for anomaly detection and transfer learning in IoT
CW Tien, TY Huang, PC Chen, JH Wang - Computers, 2021 - mdpi.com
With the development of Internet of Things (IoT) technologies, more and more smart devices
are connected to the Internet. Since these devices were designed for better connections with …
are connected to the Internet. Since these devices were designed for better connections with …
A Modified Gray Wolf Optimizer‐Based Negative Selection Algorithm for Network Anomaly Detection
Intrusion detection systems are crucial in fighting against various network attacks. By
monitoring the network behavior in real time, possible attack attempts can be detected and …
monitoring the network behavior in real time, possible attack attempts can be detected and …
MANomaly: Mutual adversarial networks for semi-supervised anomaly detection
L Zhang, X **e, K **ao, W Bai, K Liu, P Dong - Information Sciences, 2022 - Elsevier
In network intrusion detection, since the available attack traffic is much less than normal
traffic, detecting attacks and intrusions from these unbalanced traffic can be a problem of …
traffic, detecting attacks and intrusions from these unbalanced traffic can be a problem of …
A sampling-based stack framework for imbalanced learning in churn prediction
Churn prediction is gaining popularity in the research community as a powerful paradigm
that supports data-driven operational decisions. Datasets related to churn prediction are …
that supports data-driven operational decisions. Datasets related to churn prediction are …