Related papers: A Tsetlin Machine-driven Intrusion Detection Syste…
The rapid expansion of Internet of Things (IoT) systems across various domains such as industry, smart cities, healthcare, manufacturing, and government services has led to a significant increase in security risks, threatening data…
The integration of Internet of Things (IoT) technology in various domains has led to operational advancements, but it has also introduced new vulnerabilities to cybersecurity threats, as evidenced by recent widespread cyberattacks on IoT…
In the context of cybersecurity of modern communications networks, Intrusion Detection Systems (IDS) have been continuously improved, many of them incorporating machine learning (ML) techniques to identify threats. Although there are…
Embedded devices are specialised devices designed for one or only a few purposes. They are often part of a larger system, through wired or wireless connection. Those embedded devices that are connected to other computers or embedded systems…
The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond…
Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities. Several previous machine learning…
Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning…
The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditional IDS models often overlook the temporal characteristics…
The rapid growth in Internet of Things (IoT) technology has become an integral part of today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging IoT to improve communication and connectivity via…
The uses of Machine Learning (ML) in detection of network attacks have been effective when designed and evaluated in a single organisation. However, it has been very challenging to design an ML-based detection system by utilising…
Software-defined network (SDN) is a new approach that allows network control to become directly programmable, and the underlying infrastructure can be abstracted from applications and network services. Control plane). When it comes to…
Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large…
Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack…
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our…
A Network Intrusion Detection System (NIDS) is an important tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as potential…
The evolution of the traditional power grid into the "smart grid" has resulted in a fundamental shift in energy management, which allows the integration of renewable energy sources with modern communication technology. However, this…
Over the past few decades, Industrial Control Systems (ICSs) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems…
The developing field of enhanced diagnostic techniques in the diagnosis of infectious diseases, constitutes a crucial domain in modern healthcare. By utilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and incorporating…
A Network Intrusion Detection System (NIDS) is a tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as solutions to detect…
As the number of cyber-attacks is increasing, cybersecurity is evolving to a key concern for any business. Artificial Intelligence (AI) and Machine Learning (ML) (in particular Deep Learning - DL) can be leveraged as key enabling…