64 research outputs found
Reliability enhanced EV using pattern recognition techniques
The following paper will contribute to the development of novel data transmission techniques from an IVHM perspective so that Electrical Vehicles (EV) will be able to communicate semantically by directly pointing out to the worst failure/threat scenarios. This is achieved by constructing an image-based data communication in which the data that is monitored by a vast number of different sensors are collected as images; and then, the meaningful failure/threat objects are transmitted among a number of EVs. The meanings of these objects that are clarified for each EV by a set of training patterns are semantically linked from one to other EVs through the similarities that the EVs share. This is a similar approach to wellknown image compression and retrieval techniques, but the difference is that the training patterns, codebook, and codewords within the different EVs are not the same. Hence, the initial image that is compressed at the transmitter side does not exactly match the image retrieved at the receiver's side; as it concerns both EVs semantically that mainly addresses the worst risky scenarios. As an advantage, connected EVs would require less number of communication channels to talk together while also reducing data bandwidth as it only sends the similarity rates and tags of patterns instead of sending the whole initial image that is constructed from various sensors, including cameras. As a case study, this concept is applied to DC-DC converters which refer to a system that presents one of the major problems for EVs
High assurance on cyber-physical interactive systems
Cyber-Physical Systems, as distributed systems of computational elements interacting with the physical world, are highly complex systems. They can, in many instances, be considered safety critical interactive systems, as errors in interaction can have disastrous consequences (consider the case of autonomous vehicles or integrated clinical environments). High assurance is, then, an underlying requirement, also at their user interface. In this position paper we identify five challenges to be solved both in the short and in the long term, regarding the modelling of (1) distributed and (2) heterogeneous interactive systems, (3) the analysis and relation between the different abstraction layers of Cyber-Physical Systems, (4) the modelling of real time/hybrid systems, and (5) the modelling of the dynamic nature of such systems. Solutions for these challenges are not presented, but possible directions are discussed.This work was financed by National Funds through the Portuguese fundingagency, FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project: UID/EEA/50014/201
Privacy-Protecting Energy Management Unit through Model-Distribution Predictive Control
The roll-out of smart meters in electricity networks introduces risks for
consumer privacy due to increased measurement frequency and granularity.
Through various Non-Intrusive Load Monitoring techniques, consumer behavior may
be inferred from their metering data. In this paper, we propose an energy
management method that reduces energy cost and protects privacy through the
minimization of information leakage. The method is based on a Model Predictive
Controller that utilizes energy storage and local generation, and that predicts
the effects of its actions on the statistics of the actual energy consumption
of a consumer and that seen by the grid. Computationally, the method requires
solving a Mixed-Integer Quadratic Program of manageable size whenever new meter
readings are available. We simulate the controller on generated residential
load profiles with different privacy costs in a two-tier time-of-use energy
pricing environment. Results show that information leakage is effectively
reduced at the expense of increased energy cost. The results also show that
with the proposed controller the consumer load profile seen by the grid
resembles a mixture between that obtained with Non-Intrusive Load Leveling and
Lazy Stepping.Comment: Accepted for publication in IEEE Transactions on Smart Grid 2017,
special issue on Distributed Control and Efficient Optimization Methods for
Smart Gri
Critical infrastructure in the future city - developing secure and resilient cyber–physical systems
Cities face serious challenges that affect competitiveness, sustainability and their occupants’ safety & security. In response, investment is made in city infrastructure projects. Given the complexity of the systems architecture, and interactions between physical and cyber domains, this paper shows how a multi-disciplinary approach can be adopted to address the challenges. It introduces an analysis methodology for use by multi-disciplinary teams to allow the dependencies and interactions of cyber–physical systems in physical–cyber environments to be explored. The analysis methodology offers a systematic way to study the cyber– physical systems and identify safety, security or resilience issues that need to be addressed in the systems design or operation
CAPD: A Context-Aware, Policy-Driven Framework for Secure and Resilient IoBT Operations
The Internet of Battlefield Things (IoBT) will advance the operational
effectiveness of infantry units. However, this requires autonomous assets such
as sensors, drones, combat equipment, and uncrewed vehicles to collaborate,
securely share information, and be resilient to adversary attacks in contested
multi-domain operations. CAPD addresses this problem by providing a
context-aware, policy-driven framework supporting data and knowledge exchange
among autonomous entities in a battlespace. We propose an IoBT ontology that
facilitates controlled information sharing to enable semantic interoperability
between systems. Its key contributions include providing a knowledge graph with
a shared semantic schema, integration with background knowledge, efficient
mechanisms for enforcing data consistency and drawing inferences, and
supporting attribute-based access control. The sensors in the IoBT provide data
that create populated knowledge graphs based on the ontology. This paper
describes using CAPD to detect and mitigate adversary actions. CAPD enables
situational awareness using reasoning over the sensed data and SPARQL queries.
For example, adversaries can cause sensor failure or hijacking and disrupt the
tactical networks to degrade video surveillance. In such instances, CAPD uses
an ontology-based reasoner to see how alternative approaches can still support
the mission. Depending on bandwidth availability, the reasoner initiates the
creation of a reduced frame rate grayscale video by active transcoding or
transmits only still images. This ability to reason over the mission sensed
environment and attack context permits the autonomous IoBT system to exhibit
resilience in contested conditions
Resilience of the Internet of Things (IoT) from an Information Assurance (IA) Perspective
Internet infrastructure developments and the rise of the IoT Socio-Technical Systems (STS) have frequently generated more unsecure protocols to facilitate the rapid intercommunication between the plethoras of IoT devices. Whereas, current development of the IoT has been mainly focused on enabling and effectively meeting the functionality requirement of digital-enabled enterprises we have seen scant regard to their IA architecture, marginalizing system resilience with blatant afterthoughts to cyber defence. Whilst interconnected IoT devices do facilitate and expand information sharing; they further increase of risk exposure and potential loss of trust to their Socio-Technical Systems. A change in the IoT paradigm is needed to enable a security-first mind-set; if the trusted sharing of information built upon dependable resilient growth of IoT is to be established and maintained. We argue that Information Assurance is paramount to the success of IoT, specifically its resilience and dependability to continue its safe support for our digital economy
FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques
Fingerprint evidence plays an important role in a criminal investigation for
the identification of individuals. Although various techniques have been
proposed for fingerprint classification and feature extraction, automated
fingerprint identification of fingerprints is still in its earliest stage. The
performance of traditional \textit{Automatic Fingerprint Identification System}
(AFIS) depends on the presence of valid minutiae points and still requires
human expert assistance in feature extraction and identification stages. Based
on this motivation, we propose a Fingerprint Identification approach based on
Generative adversarial network and One-shot learning techniques (FIGO). Our
solution contains two components: fingerprint enhancement tier and fingerprint
identification tier. First, we propose a Pix2Pix model to transform low-quality
fingerprint images to a higher level of fingerprint images pixel by pixel
directly in the fingerprint enhancement tier. With the proposed enhancement
algorithm, the fingerprint identification model's performance is significantly
improved. Furthermore, we develop another existing solution based on Gabor
filters as a benchmark to compare with the proposed model by observing the
fingerprint device's recognition accuracy. Experimental results show that our
proposed Pix2pix model has better support than the baseline approach for
fingerprint identification. Second, we construct a fully automated fingerprint
feature extraction model using a one-shot learning approach to differentiate
each fingerprint from the others in the fingerprint identification process. Two
twin convolutional neural networks (CNNs) with shared weights and parameters
are used to obtain the feature vectors in this process. Using the proposed
method, we demonstrate that it is possible to learn necessary information from
only one training sample with high accuracy
Securing industrial control system environments: the missing piece
Cyberattacks on industrial control systems (ICSs) are no longer matters of anticipation. These systems are continually subject to malicious attacks without much resistance. Network breaches, data theft, denial of service, and command and control functions are examples of common attacks on ICSs. Despite available security solutions, safety, security, resilience, and performance require both private public sectors to step-up strategies to address increasing security concerns on ICSs. This paper reviews the ICS security risk landscape, including current security solution strategies in order to determine the gaps and limitations for effective mitigation. Notable issues point to a greater emphasis on technology security while discounting people and processes attributes. This is clearly incongruent with; emerging security risk trends, the biased security strategy of focusing more on supervisory control and data acquisition systems, and the emergence of more sector-specific solutions as against generic security solutions. Better solutions need to include approaches that follow similar patterns as the problem trend. These include security measures that are evolutionary by design in response to security risk dynamics. Solutions that recognize and include; people, process and technology security enhancement into asingle system, and addressing all three-entity vulnerabilities can provide a better solution for ICS environments
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