Machine Learning's widespread application owes to its ability to develop accurate and
scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning …

Semi-supervised machine learning can be used for obtaining subsets of unlabeled or
partially labeled dataset based on the applicable metrics of dissimilarity. At later stage, the …

Abstract In Machine Learning, the datasets used to build models are one of the main factors
limiting what these models can achieve and how good their predictive performance is …

In this paper, we propose DiFF-RF, an ensemble approach composed of random partitioning
binary trees to detect point-wise and collective (as well as contextual) anomalies. Thanks to …

Abstract Network Intrusion Detection Systems (NIDSs) are increasingly crucial due to the
expansion of computer networks. Detection techniques based on machine learning have …

Abstract A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)
requires a large amount of labeled up-to-date training data to effectively detect intrusions …

Identification of abnormal behaviors affecting public safety (eg, shoplifting, robbery, and
stealing) is essential for preventing human casualties and property damage. Many studies …

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm.
Early and accurate detection is crucial for effective treatment. However, single-lead …

IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration
and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting …

The emergence of the Mirai malware facilitated a DDoS attack vector to surge to almost 1
Tbps in 2016, instigated by less than 150,000 infected IoT devices. With the infection of five …