Biblio

In this paper, we study a wireless powered cellular network (WPCN) supported with network coding capability. In particular, we consider a network consisting of k cellular users (CUs) served by a hybrid access point (HAP) that takes over energy transfer to the users on top of information transmission over both the uplink (UL) and downlink (DL). Each CU has k+1 states representing its communication behavior, and collectively are referred to as the user demand profile. Opportunistically, when the CUs have information to be exchanged through the HAP, it broadcasts this information in coded format to the exchanging pairs, resulting in saving time slots over the DL. These saved slots are then utilized by the HAP to prolong the network lifetime and enhance the network throughput. We quantify, analytically, the performance gain of our network-coded WPCN over the conventional one, that does not employ network coding, in terms of network lifetime and throughput. We consider the two extreme cases of using all the saved slots either for energy boosting or throughput enhancement. In addition, a lifetime/throughput optimization is carried out by the HAP for balancing the saved slots assignment in an optimized fashion, where the problem is formulated as a mixed-integer linear programming optimization problem. Numerical results exhibit the network performance gains from the lifetime and throughput perspectives, for a uniform user demand profile across all CUs. Moreover, the effect of biasing the user demand profile of some CUs in the network reveals considerable improvement in the network performance gains.

To improve the security and reliability of the system in case of sensor failure, a fault diagnosis algorithm based on neural network is proposed to locate the fault quickly and reconstruct the control system in this paper. Firstly, the typical airborne sensors are introduced and their common failure modes are analyzed. Then, a new method of complex feature extraction using wavelet packet is put forward to extract the fault characteristics of UAV sensors. Finally, the observer method based on BP neural network is adopted to train and acquire data offline, and to detect and process single or multiple sensor faults online. Matlab simulation results show that the algorithm has good diagnostic accuracy and strong generalization ability, which also has certain practicability in engineering.

Industrial Control Systems (ICS) are complex systems made up of many components with different tasks. For a safe and secure operation, each device needs to carry out its tasks correctly. To monitor a system and ensure the correct behavior of systems, anomaly detection is used.Models of expected behavior often rely only on cyber or physical features for anomaly detection. We propose an anomaly detection system that combines both types of features to create a dynamic fingerprint of an ICS. We present how a cyber-physical anomaly detection using sound on the physical layer can be designed, and which challenges need to be overcome for a successful implementation. We perform an initial evaluation for identifying actions of a 3D printer.

Cyber-physical systems, which are ubiquitous in modern critical infrastructure, oftentimes rely on sending actuation commands and sensor measurements over a network, subjecting this information to potential man-in-the-middle attacks. These attacks can take the form of denial of service attacks or integrity attacks. Previous approaches at ensuring the resiliency of the overall control system against these types of attacks have leveraged functional redundancy in the system, including resilient estimation and reconfigurable control. However, these approaches are only able to ensure resiliency up to a particular subset of the actuator commands and sensor measurements being compromised. In contrast, we introduce a resiliency mechanism in this paper that can ensure safety for the overall system when all the actuator commands and sensor measurements are compromised. In addition, this approach does not require the implementation of any detection algorithm. We leverage communication redundancy in the number of pathways across the network to guarantee safety when up to a certain percentage of those pathways are compromised. The conditions under which safety is guaranteed are presented along with the resiliency mechanism itself, and our results are illustrated via simulation.

This paper presents an observer-based security control scheme for a Cyber-Physical System (CPS). In the considered system, the feedback channel of the CPS may suffer from Denial-of-Service (DoS). To begin with, a time-delayed switching CPS model is constructed according to two different attack situations. And then, based on the switching model, an observer-based controller is designed in the cyber-layer, Meanwhile, the stability of the closed-loop system is analyzed based on H$ınfty$ stability of switching systems in view of Average Dwell Time (ADT). At last, the performance of the proposed security control scheme is illustrated by an numerical example in Simulation.

Traditionally, Industrial Control Systems (ICS) have been operated as air-gapped networks, without a necessity to connect directly to the Internet. With the introduction of the Internet of Things (IoT) paradigm, along with the cloud computing shift in traditional IT environments, ICS systems went through an adaptation period in the recent years, as the Industrial Internet of Things (IIoT) became popular. ICS systems, also called Cyber-Physical-Systems (CPS), operate on physical devices (i.e., actuators, sensors) at the lowest layer. An anomaly that effect this layer, could potentially result in physical damage. Due to the new attack surfaces that came about with IIoT movement, precise, accurate, and prompt intrusion/anomaly detection is becoming even more crucial in ICS. This paper proposes a novel method for real-time intrusion/anomaly detection based on a cyber-physical system network traffic. To evaluate the proposed anomaly detection method's efficiency, we run our implementation against a network trace taken from a Secure Water Treatment Testbed (SWAT) of iTrust Laboratory at Singapore.

The aim of this study is to determine the current challenges related to security and trust issues in digital supply chains. The development of information and communication technologies (ICT) has improved the efficiency of supply chains, while creating new vulnerabilities and increasing the likelihood of security threats. Previous studies lack the physical security aspect, so the emphasis is on the security of cyber-physical systems. In order to achieve the goal of the study, traditional and digital supply chains, their security risks and main differences were examined. A security framework for cyber-physical risks in digital supply chains was developed.

The Internet is evolving everywhere and expanding its entity globally. The IoT(Internet of things) is a new and interesting concept introduced in this world of internet. Generally it is interconnected computing device which can be embedded in our daily routine objects through which we can send and receive data. It is beyond connecting computers and laptops only although it can connect billion of devices. It can be described as reliable method of communication that also make use of other technologies like wireless sensor, QR code etc. IoT (Internet of Things) is making everything smart with use of technology like smart homes, smart cities, smart watches. In this chapter, we will study the security algorithms in IoT (Internet of Things) which can be achieved with encryption process. In the world of IoT, data is more vulnerable to threats. So as to protect data integrity, data confidentiality, we have Light weight Encryption Algorithms like symmetric key cryptography and public key cryptography for secure IoT (Internet of Things) named as Secure IoT. Because it is not convenient to use full encryption algorithms that require large memory size, large program code and larger execution time. Light weight algorithms meet all resource constraints of small memory size, less execution time and efficiency. The algorithms can be measured in terms of key size, no of blocks and algorithm structure, chip size and energy consumption. Light Weight Techniques provides security to smart object networks and also provides efficiency. In Symmetric Key Cryptography, two parties can have identical keys but has some practical difficulty. Public Key Cryptography uses both private and public key which are related to each other. Public key is known to everyone while private key is kept secret. Public Key cryptography method is based on mathematical problems. So, to implement this method, one should have a great expertise.

The internal laboratory directed research and development (LDRD) project Infrastructure eXpression (IX) at the Idaho National Laboratory (INL), is based on codifying infrastructure to support automatic applicability to emerging cyber issues, enabling automated cyber responses, codifying attack surfaces, and analysis of cyber impacts to our nation's most critical infrastructure. IX uses the Structured Threat Information eXpression (STIX) open international standard version 2.1 which supports STIX Cyber Observable (SCO) to codify infrastructure characteristics and exposures. Using these codified infrastructures, STIX Relationship Objects (SRO) connect to STIX Domain Objects (SDO) used for modeling cyber threat used to create attack surfaces integrated with specific infrastructure. This IX model creates a shareable, actionable and implementable attack surface that is updateable with emerging threat or infrastructure modifications. Enrichment of cyber threat information includes attack patterns, indicators, courses of action, malware and threat actors. Codifying infrastructure in IX enables creation of software and hardware bill of materials (SBoM/HBoM) information, analysis of emerging cyber vulnerabilities including supply chain threat to infrastructure.

In this paper, a multiple switches open-fault diagnostic method using ANNs (Artificial Neural Networks) for three-phase PWM (Pulse Width Modulation) converters is proposed. When an open-fault occurs on switches in the converter, the stator currents can include dc and harmonic components. Since these abnormal currents cannot be easily cut off by protection circuits, secondary faults can occur in peripherals. Therefore, a method of diagnosing the open-fault is required. For open-faults for single switch and double switches, there are 21 types of fault modes depending on faulty switches. In this paper, these fault modes are localized by using the dc component and THD (Total Harmonics Distortion) in fault currents. For obtaining the dc component and THD in the currents, an ADALINE (Adaptive Linear Neuron) is used. For localizing fault modes, two ANNs are used in series; the 21 fault modes are categorized into six sectors by the first ANN of using the dc components, and then the second ANN localizes fault modes by using both the dc and THDs of the d-q axes current in each sector. Simulations and experiments confirm the validity of the proposed method.

Direct-access attacks were initially considered as un-realistic threats in cyber security because the attacker can more easily mount other non-computerized attacks like cutting a brake line. In recent years, some research into direct-access attacks have been conducted especially in the automotive field, for example, research on an attack method that makes the ECU stop functioning via the CAN bus. The problem with existing risk quantification methods is that direct-access attacks seem not to be recognized as serious threats. To solve this problem, we propose a new risk quantification method by applying vulnerability evaluation criteria and by setting metrics. We also confirm that direct-access attacks not recognized by conventional methods can be evaluated appropriately, using the case study of an automotive system as an example of a cyber-physical system.

The traditional data storage method often adopts centralized architecture, which is prone to trust and security problems. This paper proposes a trusted data storage and access control scheme combining blockchain and attribute-based encryption, which allow cyber-physical system (CPS) nodes to realize the fine-grained access control strategy. At the same time, this paper combines the blockchain technology with distributed storage, and only store the access control policy and the data access address on the blockchain, which solves the storage bottleneck of blockchain system. Furthermore, this paper proposes a novel multi-authority attributed-based identification method, which realizes distributed attribute key generation and simplifies the pairwise authentication process of multi-authority. It can not only address the key escrow problem of one single authority, but also reduce the problem of high communication overhead and heavy burden of multi-authority. The analyzed results show that the proposed scheme has better comprehensive performance in trusted data storage and access control for power cyber-physical system.

Cyber-physical systems (CPS) and IoT systems are nowadays commonly designed as self-adaptive, endowing them with the ability to dynamically reconFigure to reflect their changing environment. This adaptation concerns also the security, as one of the most important properties of these systems. Though the state of the art on adaptivity in terms of security related to these systems can often deal well with fully anticipated situations in the environment, it becomes a challenge to deal with situations that are not or only partially anticipated. This uncertainty is however omnipresent in these systems due to humans in the loop, open-endedness and only partial understanding of the processes happening in the environment. In this paper, we partially address this challenge by featuring an approach for tackling access control in face of partially unanticipated situations. We base our solution on special kind of aspects that build on existing access control system and create a second level of adaptation that addresses the partially unanticipated situations by modifying access control rules. The approach is based on our previous work where we have analyzed and classified uncertainty in security and trust in such systems and have outlined the idea of access-control related situational patterns. The aspects that we present in this paper serve as means for application-specific specialization of the situational patterns. We showcase our approach on a simplified but real-life example in the domain of Industry 4.0 that comes from one of our industrial projects.

To ensure a ship’s fully operational status in a wide spectrum of missions, as passenger transportation, international trade, and military activities, numerous interdependent systems are essential. Despite the potential critical consequences of misunderstanding or ignoring those interdependencies, there are very few documented approaches to enable their identification, representation, analysis, and use. From the cybersecurity point of view, if an anomaly occurs on one of the interdependent systems, it could eventually impact the whole ship, jeopardizing its mission success. This paper presents a proposal to identify the main dependencies of layers within and between generic ship’s functional blocks. An analysis of one of these layers, the platform systems, is developed to examine a naval cyber-physical system (CPS), the water management for passenger use, and its associated dependencies, from an intrinsic perspective. This analysis generates a three layers graph, on which dependencies are represented as oriented edges. Each abstraction level of the graph represents the physical, digital, and system variables of the examined CPS. The obtained result confirms the interest of graphs for dependencies representation and analysis. It is an operational depiction of the different systems interdependencies, on which can rely a cybersecurity evaluation, like anomaly detection and propagation assessment.

This paper reports electroplated CoPt permanent magnets samples yielding thicknesses up to 100 μm, deposition rates up to 35 μm/h, coercivities up to 1000 kA/m (1.25 T), remanences up to 0.8 T, and energy products up to 77 kJ/m3. The impact of electroplating bath temperature and glycine additives are systematically studied. Compared to prior work, these microfabricated magnets not only exhibit up to 10X increase in thickness without sacrificing magnetic performance, but also improve the areal magnetic energy density by 2X. Using a thick removeable SU-8 mold, these high-performing thick-film magnets are intended for magnetic microactuators, magnetic field sensors, energy conversion devices, and more.

The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.

Cloud architecture has become a valuable solution for different applications, such as big data analytics, due to its high degree of availability, scalability and strategic value. However, there still remain challenges in managing cloud architecture, in areas such as cloud security. In this paper, we exploit software-defined networking (SDN) and blockchain technologies to secure cloud management platforms from a networking perspective. We develop a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. Furthermore, our proposed networking infrastructure also enables the autonomous bandwidth provisioning to enhance the availability of cloud architecture. In the simulation section, we evaluate the performance of our proposed blockchain-powered SDN-enabled networking infrastructure by considering different scenarios.

The Internet of Things (IoT) has been one of the biggest revelations of the last decade. These cyber-physical systems seamlessly integrate and improve the activities in our daily lives. Hence, creating a wide application for it in several domains, such as smart buildings and cities. However, the integration of IoT also comes with privacy challenges. The privacy challenges result from the ability of these devices to pervasively collect personal data about individuals through sensors in ways that could be unknown to them. A number of research efforts have evaluated privacy policy awareness and enforcement as key components for addressing these privacy challenges. This paper provides a framework for understanding contextualized privacy policy within the IoT domain. This will enable IoT privacy researchers to better understand IoT privacy policies and their modeling.

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.

Power systems automation and communication standards are crucial for the transition of the conventional power system towards a smart grid. The IEC 61850 standard is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation devices. IEC 61850 serves as the foundation for open communication and data exchange for digital substations of the smart grid. However, IEC 61850 has cyber security vulnerabilities that can be exploited with a man-in-the-middle attack. Such coordinated cyber attacks against the protection system in digital substations can disconnect generation and transmission lines, causing cascading failures. In this paper, we demonstrate a cyber attack involving the Generic Object-Oriented Substation Event (GOOSE) protocol of IEC 61850. This is achieved by exploiting the cyber security vulnerabilities in the protocol and injecting spoofed GOOSE data frames into the substation communication network at the bay level. The cyber attack leads to tripping of multiple protective relays in the power grid, eventually resulting in a blackout. The attack model and impact on system dynamics are verified experimentally through hardware-in-the-loop simulations using commercial relays and Real-Time Digital Simulator (RTDS).

In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

While Explanatory AI (XAI) is attracting increasing interest from academic research, most AI-based solutions still rely on black box methods. This is unsuitable for certain domains, such as smart homes, where transparency is key to gaining user trust and solution adoption. Moreover, smart homes are challenging environments for XAI, as they are decentralized systems that undergo runtime changes. We aim to develop an XAI solution for addressing problems that an autonomic management system either could not resolve or resolved in a surprising manner. This implies situations where the current state of affairs is not what the user expected, hence requiring an explanation. The objective is to solve the apparent conflict between expectation and observation through understandable logical steps, thus generating an argumentative dialogue. While focusing on the smart home domain, our approach is intended to be generic and transferable to other cyber-physical systems offering similar challenges. This position paper focuses on proposing a decentralized algorithm, called D-CAN, and its corresponding generic decentralized architecture. This approach is particularly suited for SISSY systems, as it enables XAI functions to be extended and updated when devices join and leave the managed system dynamically. We illustrate our proposal via several representative case studies from the smart home domain.

In this paper, we propose a decision support process that is designed to help network and security operators in understanding the complexity of a current security situation and decision making concerning ongoing cyber-attacks and threats. The process focuses on enterprise missions and uses a graph-based mission decomposition model that captures the missions, underlying hosts and services in the network, and functional and security requirements between them. Knowing the vulnerabilities and attacker's position in the network, the process employs logical attack graphs and Bayesian network to infer the probability of the disruption of the confidentiality, integrity, and availability of the missions. Based on the probabilities of disruptions, the process suggests the most resilient mission configuration that would withstand the current security situation.

The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.

Recently, some innovations have been applied to the linear permanent magnet generator (LPMG). They are including the introduction of high-remanence rare-earth magnets and the use of different magnet configurations. However, these actions also affect the flow and distribution of the magnetic flux. Under the load condition, the load current will also generate reverse flux. The flux resultant then affects the coil parameters; the significant one is the coil inductance. Since it is influential to the output voltage and output power profiles, the impact study of the permanent magnet settings under load condition is essential. Hence this paper presents the inductance profile study of the LMPG with different magnet configurations. After presenting the initial designs, several magnet settings including the material and configuration were varied. Finite element magnetic simulation and analytical calculations were then performed to obtain the inductance profile of the LPMG. The results show that the inductance value varies with change in load current and magnet position. The different magnet materials (SmCo 30 and N35) do not significantly affect the inductance. Meanwhile, different magnet configuration (radial, axial, halbach) results in different inductance trends.