Biblio

Biometric methods are the most effective and accurate authentication methods. However, a significant drawback of such methods is the storage of authentication information in clear text. The article is devoted to solving this problem by means of symmetric encryption method and the method of dividing the memory space. The method of symmetric encryption ensures confidentiality during storage and transmission of biometric characteristics, the method of dividing the memory space provides an increase of information security level during processing of biometric characteristics.

Industrial control systems (ICSs) are used in various infrastructures and industrial plants for realizing their control operation and ensuring their safety. Concerns about the cybersecurity of industrial control systems have raised due to the increased number of cyber-attack incidents on critical infrastructures in the light of the advancement in the cyber activity of ICSs. Nevertheless, the operation of the industrial control systems is bind to vital aspects in life, which are safety, economy, and security. This paper presents a semi-supervised, hybrid attack detection approach for industrial control systems by combining Isolation Forest and Convolutional Neural Network (CNN) models. The proposed framework is developed using the normal operational data, and it is composed of a feature extraction model implemented using a One-Dimensional Convolutional Neural Network (1D-CNN) and an isolation forest model for the detection. The two models are trained independently such that the feature extraction model aims to extract useful features from the continuous-time signals that are then used along with the binary actuator signals to train the isolation forest-based detection model. The proposed approach is applied to a down-scaled industrial control system, which is a water treatment plant known as the Secure Water Treatment (SWaT) testbed. The performance of the proposed method is compared with the other works using the same testbed, and it shows an improvement in terms of the detection capability.

Despite significant research, the supply chain management challenges still have a long way to go with respect to solving the issues such as management of product supply information, product lifecycle, transport history, etc. Given the recent rise of blockchain technology in various industrial sectors, our work explores the issues prevalent in each stage of the supply chain and checks their candidacy for the implementation using blockchain technology. The analysis is performed in terms of the characteristics of trust and decentralization with respect to forming a generalized framework. The main contribution of this work is to create a conceptual overview of the areas where blockchain integrates with supply chain management in order to benefit further research and development.

Industrial production plants traditionally include sensors for monitoring or documenting processes, and actuators for enabling corrective actions in cases of misconfigurations, failures, or dangerous events. With the advent of the IoT, embedded controllers link these `things' to local networks that often are of low power wireless kind, and are interconnected via gateways to some cloud from the global Internet. Inter-networked sensors and actuators in the industrial IoT form a critical subsystem while frequently operating under harsh conditions. It is currently under debate how to approach inter-networking of critical industrial components in a safe and secure manner.In this paper, we analyze the potentials of ICN for providing a secure and robust networking solution for constrained controllers in industrial safety systems. We showcase hazardous gas sensing in widespread industrial environments, such as refineries, and compare with IP-based approaches such as CoAP and MQTT. Our findings indicate that the content-centric security model, as well as enhanced DoS resistance are important arguments for deploying Information Centric Networking in a safety-critical industrial IoT. Evaluation of the crypto efforts on the RIOT operating system for content security reveal its feasibility for common deployment scenarios.

New generations of industrial control systems offer higher performance, they are distributed, and it is very likely that they are internet connected in one way or another. These trends raise new challenges in the contexts of reliability and security. We propose a novel approach that tackles the complexity of industrial control systems at design time and run time. At design time our target is to ease the configuration and verification of controller configurations through model-driven engineering techniques together with the contract-based design paradigm. At run time the information from design time is reused in order to support a modular and distributed self-adaptive software system that aims to increase reliability and security. The industrial setting of the presented approach are control devices for hydropower plant units.