Related papers: Security in Process: Detecting Attacks in Industri…

Insider threats, as one type of the most challenging threats in cyberspace, usually cause significant loss to organizations. While the problem of insider threat detection has been studied for a long time in both security and data mining…

Machine learning models have made many decision support systems to be faster, more accurate, and more efficient. However, applications of machine learning in network security face a more disproportionate threat of active adversarial attacks…

We study whether specific combinations of technological advancements can signal the presence of local capabilities allowing for a given industrial production. To this end, we generate a multi-layer network using country-level patent and…

Widespread interest in the emerging area of predictive analytics is driving industries such as manufacturing to explore new approaches to the collection and management of data provided from Industrial Internet of Things (IIoT) devices.…

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods…

Process mining, as a high-level field in data mining, plays a crucial role in enhancing operational efficiency and decision-making across organizations. In this survey paper, we delve into the growing significance and ongoing trends in the…

As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for…

Information may be accessed from a distance thanks to computer networks. Wireless or wired networks are also possible. Due to recent developments in wireless infrastructure, wireless sensor networks (WSNs) were developed. Activities or…

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within…

Nowadays all industrial sectors are increasingly faced with the explosion in the amount of data. Therefore, it raises the question of the efficient use of this large amount of data. In this research work, we are concerned with process and…

Smart homes, enterprises, and cities are increasingly being equipped with a plethora of Internet of Things (IoT), ranging from smart-lights to security cameras. While IoT networks have the potential to benefit our lives, they create privacy…

In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models.…

The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened…

Todays industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the…

Along with the development of intelligent manufacturing, especially with the high connectivity of the industrial control system (ICS), the network security of ICS becomes more important. And in recent years, there has been much research on…

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical…

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used…

Threat modeling has been successfully applied to model technical threats within information systems. However, a lack of methods focusing on non-technical assets and their representation can be observed in theory and practice. Following the…

With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is pivotal to ensure that neither the model nor the data can be used…

The expansion of Internet of Things (IoT) devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the…