Network attacks pose a significant challenge for smart grid networks, mainly due to the
existence of several multi-directional communication devices coupling consumers to the …

The incorporation of IoT in the world has had tremendous popularity in the field of
Technology. This great innovation has enabled seamless transformation in business and …

This paper presents several novel algorithms for real-time cyberattack detection using the
Auto-Associative Deep Random Neural Network. Some of these algorithms require offline …

Data mining algorithms have essential methods and rules that can contribute in detecting
and preventing various types of network attacks. These methods are utilized with the …

Botnets have become increasingly common and progressively dangerous to both business
and domestic networks alike. Due to the Covid-19 pandemic, a large quantity of the …

The poor security and larger number of IoT devices are highly possible to be snatched and
results in Distributed Denial of Service attack. The IoT attacks corrupt the availability of …

In this paper, we apply a fusion machine learning method to construct an automatic intrusion
detection system. Concretely, we employ the orthogonal variance decomposition technique …

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-
processing phase with imbalanced data could bring the negative effect on prediction result …

In recent years, IoT devices have often been the target of Mirai Botnet attacks. This paper
develops an intrusion detection method based on Auto-Associated Dense Random Neural …

This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which
enables a fully online Machine Learning (ML) based Intrusion Detection System (IDS) that …