Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

Securing the Participation of Safety-Critical SCADA Systems in the Industrial Internet of Things

In the past, industrial control systems were ‘air gapped’ and isolated from more conventional networks. They used specialist protocols, such as Modbus, that are very different from TCP/IP. Individual devices used proprietary operating systems rather than the more familiar Linux or Windows. However, things are changing. There is a move for greater connectivity – for instance so that higher-level enterprise management systems can exchange information that helps optimise production processes. At the same time, industrial systems have been influenced by concepts from the Internet of Things; where the information derived from sensors and actuators in domestic and industrial components can be addressed through network interfaces. This paper identifies a range of cyber security and safety concerns that arise from these developments. The closing sections introduce potential solutions and identify areas for future research

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms

Guest Editorial Special Issue on: Big Data Analytics in Intelligent Systems

The amount of information that is being created, every day, is quickly growing. As such, it is now more common than ever to deal with extremely large datasets. As systems develop and become more intelligent and adaptive, analysing their behaviour is a challenge. The heterogeneity, volume and speed of data generation are increasing rapidly. This is further exacerbated by the use of wireless networks, sensors, smartphones and the Internet. Such systems are capable of generating a phenomenal amount of information and the need to analyse their behaviour, to detect security anomalies or predict future demands for example, is becoming harder. Furthermore, securing such systems is a challenge. As threats evolve, so should security measures develop and adopt increasingly intelligent security techniques. Adaptive systems must be employed and existing methods built upon to provide well-structured defence in depth. Despite the clear need to develop effective protection methods, the task is a difficult one, as there are significant weaknesses in the existing security currently in place. Consequently, this special issue of the Journal of Computer Sciences and Applications discusses big data analytics in intelligent systems. The specific topics of discussion include the Internet of Things, Web Services, Cloud Computing, Security and Interconnected Systems

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability