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In this paper, we present a side-channel resilient moving target defense mechanism against power-/electromagnetic-based side-channel attacks. Recent countermeasures use fresh rekeying after every encryption/decryption process; this causes major overhead in synchronizing the communicating parties. In contrast to previous work, our mechanism integrates fresh rekeying and masking techniques at an interval, where these techniques are driven by the maximum number of side-channel leakage traces required toward a successful embedded attack. Hence, the mechanism tracks the effect of attacks on the number of traces, and consequently applies rekeying at suitable intervals to reduce the computational/communication overhead, while increasing the attack cost. The mechanism scalability was evaluated against an advanced attack model based on machine learning methods that reduces significantly the number of traces required for a successful attack under masking implementation.

Most existing industrial control systems (ICSs), such as building energy management systems (EMSs), were installed when potential security threats were only physical. With advances in connectivity, ICSs are now, typically, connected to communications networks and, as a result, can be accessed remotely. This extends the attack surface to include the potential for sophisticated cyber attacks, which can adversely impact ICS operation, resulting in service interruption, equipment damage, safety concerns, and associated financial implications. In this work, a novel cyber-physical security framework for ICSs is proposed, which incorporates an analytics tool for attack detection and executes a reliable estimation-based attack-resilient control policy, whenever an attack is detected. The proposed framework is adaptable to already implemented ICS and the stability and optimal performance of the controlled system under attack has been proved. The performance of the proposed framework is evaluated using a reduced order model of a real EMS site and simulated attacks. QC 20170906 CERCES

Smart cities offer a variety of services to provide citizens with efficient transport, water distribution, crime prevention, and traffic control. Such services are personalized by automatically capturing, storing, and processing personally identifiable data. The disclosure of such data to a service provider raises privacy concerns for application users. As a result, research has recognized the need for privacy aware services in smart cities. In this paper we present PrivacyZones, a privacy awareness framework which requires the service provider to share meaningful features of the data collected by their application (such as the number of users in proximity to a location). Using this information an application user will know the potential privacy risk prior to sharing their data. The framework can also recommend the actions a user can take to mitigate privacy risks, such as when, where and what data to share with a service provider. We demonstrate how PrivacyZones could work for two services. First, a Hail-A-Taxi service, with real cell phone and taxi-cab GPS data from the Chinese city, Shenzhen. Second, a Get-A-Discount service with census-income data where inferred income level determines the type of discounts offered to users as they explore a city.

Current approaches to building Cyber Physical systems including Energy Management Systems (EMS) design often rely on large monolithic models to represent the EMS. These models are typically abstract and simple to aid with performance. However, they can lack fidelity and detail, particularly with regards to physical aspects. This makes it challenging to extract valuable and accurate data from these models, such as sustainability metrics. Our approach to this problem is to employ a Cyber-Physical Systems Model-Based Design methodology that enables the use of various specialised multi-disciplinary models through Functional Mock-up Interface (FMI) and a common integration platform based on SysML. We apply the approach to a heating, ventilation and air conditioning case study and show how the resulting data can be used to evaluate a design. In addition, we discuss how the approach supports the inclusion of dedicated models for the generation of precise sustainability metrics.

We describe an approach to the model-based engineering of cyber-physical systems that permits the coupling of diverse discrete-event and continuous-time models and their simulators. A case study in the building automation domain demonstrates how such co-models and co-simulation can promote early cooperation between disciplines within a systems engineering process before the expensive commitment is made to integration in physical prototypes. We identify areas for future advances in foundations, methods and tools to realise the potential of a co-modelling approach within established systems engineering processes.