Anomaly detection is a critical issue across several academic fields and real-world
applications. Artificial neural networks have been proposed to detect anomalies from …

Ever since the internet became part of the everyday lives of humans providing network
security has been considered of utmost importance. Over the years lot of time and energy …

An intrusion detection system (IDS) is a system that monitors network traffic for malicious
activity and generates alerts. In anomaly-based detection, machine learning (ML) algorithms …

Existing machine-learning research aims to improve the predictive capability of datasets
using various feature selection and classification models. In the intrusion detection, data …

In the recent years, drones have been extensively used in variety of fields and given the
essential nature of drone swarm services, such as network traffic monitoring and search and …

The biggest challenge for cyber security lies in the detection of anomalies in networks.
Machine learning techniques participate in the automation of the detection process. In this …

In this paper, the support vector machine (SVM) classification technique to classify normal
and attack traffic in the Spark distributed environment has been introduced and evaluated. In …

Intrusion detection systems play a pivotal role in safeguarding networks by analyzing
network data to identify potential intrusions. The effectiveness of these systems relies on …

Faced with a complex network environment, network security issues are getting more and
more serious. Cyber attacks will not only leak user privacy, but also cause huge economic …