Deep SARSA-based reinforcement learning approach for anomaly network intrusion detection system
The growing evolution of cyber-attacks imposes a risk in network services. The search of
new techniques is essential to detect and classify dangerous attacks. In that regard, deep …
new techniques is essential to detect and classify dangerous attacks. In that regard, deep …
Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem
Network intrusion detection problem is an ongoing challenging research area because of a
huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks …
huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks …
Outlier dirichlet mixture mechanism: Adversarial statistical learning for anomaly detection in the fog
Current anomaly detection systems (ADSs) apply statistical and machine learning
algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced …
algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced …
Designing an efficient security framework for detecting intrusions in virtual network of cloud computing
Cloud computing has grown for various IT capabilities such as IoTs, Mobile Computing,
Smart IT, etc. However, due to the dynamic and distributed nature of cloud and …
Smart IT, etc. However, due to the dynamic and distributed nature of cloud and …
[HTML][HTML] DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things
Abstract The Industrial Internet of Things (IIoT) is a rapidly emerging technology that
increases the efficiency and productivity of industrial environments by integrating smart …
increases the efficiency and productivity of industrial environments by integrating smart …
UIDS: a unified intrusion detection system for IoT environment
Intrusion detection system (IDS) using machine learning approach is getting popularity as it
has an advantage of getting updated by itself to defend against any new type of attack …
has an advantage of getting updated by itself to defend against any new type of attack …
An improved design for a cloud intrusion detection system using hybrid features selection approach with ML classifier
The focus of cloud computing nowadays has been reshaping the digital epoch, in which
clients now face serious concerns about the security and privacy of their data hosted in the …
clients now face serious concerns about the security and privacy of their data hosted in the …
Chameleon: Optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection
In this paper, we propose an optimization approach by leveraging swarm intelligence and
ensemble methods to solve the non-deterministic feature selection problem. The proposed …
ensemble methods to solve the non-deterministic feature selection problem. The proposed …
Recursive feature elimination with cross-validation with decision tree: Feature selection method for machine learning-based intrusion detection systems
The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …
An enhanced anomaly detection in web traffic using a stack of classifier ensemble
A Web attack protection system is extremely essential in today's information age. Classifier
ensembles have been considered for anomaly-based intrusion detection in Web traffic …
ensembles have been considered for anomaly-based intrusion detection in Web traffic …