Rapid developments in network technologies and the amount and scope of data transferred
on networks are increasing day by day. Depending on this situation, the density and …

In the current technological environment, different entities engage in intricate cyber security
approaches in order to counter damages and disruptions in web-based systems. The design …

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some
of the complex threats that have put the online community at risk. The increase in the …

Over the last years, several works have proposed highly accurate Android malware
detection techniques. Surprisingly, modern malware apps can still pave their way to official …

Outlier detection is an essential research field in data mining, especially in the areas of
network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier …

Machine learning has become increasingly popular in academic and industrial communities
and has been widely implemented in various online applications due to its powerful ability to …

Intrusion detection systems (IDSs) are employed at various levels in the network to either
detect or prevent an intrusion that could cause irrecoverable data damage in IoT …

The last decade has seen the increasing application of machine learning to various tasks,
including network anomaly detection. But anomaly detection methods based on a single …

This paper proposes a new model for onboard physical fault detection on autonomous
unmanned aerial vehicles (UAV) through machine learning (ML) techniques. The proposal …

Network-based intrusion detection is a widely explored topic in the literature. Yet, despite the
promising reported results, designed schemes are rarely used in production environments …