Biblio

This work establishes a game-theoretic framework to study cross-layer coordinated attacks on cyber-physical systems (CPSs). The attacker can interfere with the physical process and launch jamming attacks on the communication channels simultaneously. At the same time, the defender can dodge the jamming by dispensing with observations. The generic framework captures a wide variety of classic attack models on CPSs. Leveraging dynamic programming techniques, we fully characterize the Subgame Perfect Equilibrium (SPE) control strategies. We also derive the SPE observation and jamming strategies and provide efficient computational methods to compute them. The results demonstrate that the physical and cyber attacks are coordinated and depend on each other.On the one hand, the control strategies are linear in the state estimate, and the estimate error caused by jamming attacks will induce performance degradation. On the other hand, the interactions between the attacker and the defender in the physical layer significantly impact the observation and jamming strategies. Numerical examples illustrate the inter-actions between the defender and the attacker through their observation and jamming strategies.

New services and products are increasingly becoming integral parts of our daily lives rising our technological dependence, as well as our exposure to risks from cyber. Critical sectors such as transport are progressively depending on digital technologies to run their core operations and develop novel solutions to exploit the economic strengths of the European Union. However, despite the fact that the continuously increasing number of visitors, entering the European Union through land-border crossing points or seaports, brings tremendous economic benefits, novel border control solutions, such as mobile devices for passenger identification for land and sea border control, are essential to accurately identify passengers ``on the fly'' while ensuring their comfort. However, the highly confidential personal data managed by these devices makes them an attractive target for cyberattacks. Therefore, novel secure and usable user authentication mechanisms are required to increase the level of security of this kind of devices without interrupting border control activities. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Besides that, a novel risk-based adaptive user authentication mechanism is proposed for mobile passenger identification devices used by border control officers at land and sea borders.

Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different “attack scenarios”, then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which “detectors” best fit for each “attack scenario”. Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters.

The increasingly intensive use of software systems in diverse sectors, especially in business, government, healthcare, and critical infrastructures, makes it essential to deliver code that is secure. In this work, we present two sets of experiments aiming at helping developers to improve software security from the early development stages. The first experiment is focused on using software metrics to build prediction models to distinguish vulnerable from non-vulnerable code. The second experiment studies the hypothesis of developing a consensus-based decision-making approach on top of several machine learning-based prediction models, trained using software metrics data to categorize code units with respect to their security. Such categories suggest a priority (ranking) of software code units based on the potential existence of security vulnerabilities. Results show that software metrics do not constitute sufficient evidence of security issues and cannot effectively be used to build a prediction model to distinguish vulnerable from non-vulnerable code. However, with a consensus-based decision-making approach, it is possible to classify code units from a security perspective, which allows developers to decide (considering the criticality of the system under development and the available resources) which parts of the software should be the focal point for the detection and removal of security vulnerabilities.

This paper studies the use of Supervisory Control Theory to design and implement post-fault restoration schemes in a HVDC grid. Our study focuses on the synthesis of discrete controllers and on the management of variable control rules during the execution of the protection strategy. The resulting supervisory control system can be proven "free of deadlocks" in the sense that designated tasks are always completed.

This paper builds an Augmented Reality Sandbox (AR Sandbox) system based on augmented reality technology, and performs a 3D reconstruction for the sandbox terrain using the depth sensor Microsoft Kinect in the AR Sandbox, as an entry point to pave the way for later development of related metaverse applications, such as the metaverse architecting and visual interactive modeling. The innovation of this paper is that for the AR Sandbox scene, a 3D reconstruction method based on depth sensor is proposed, which can automatically cut off the edge of the sandbox table in Kinect field of view, and accurately and completely reconstruct the sandbox terrain in Matlab.

This paper introduces a scheme for achieving trustworthy computing on SoCs that use an outsourced AXI interconnect for on-chip communication. This is achieved through component guarding, data tagging, event verification, and consequently responding dynamically to an attack. Experimental results confirm the ability of the proposed scheme to detect HT attacks and respond to them at run-time. The proposed scheme extends the state-of-art in trustworthy computing on untrustworthy components by focusing on the issue of an untrusted on-chip interconnect for the first time, and by developing a scheme that is independent of untrusted third-party IP.

Telecommunications and data are pervasive in almost each aspect of our every-day life and new concerns progressively arise as a result of stakes related to privacy and data protection [1]. Indeed, systems development becomes data-centric leading to an ecosystem where a variety of players intervene (citizens, industry, regulators) and where the policies regarding data usage and utilization are far from consensual. The new General Data Protection Regulation (GDPR) enacted by the European Commission in 2018 has introduced new provisions including principles for lawfulness, fairness, transparency, etc. thus endorsing data subjects with new rights in regards to their personal data. In this context, a growing need for approaches that conceptualize and help engineers to integrate GDPR and privacy provisions at design time becomes paramount. This paper presents a comprehensive approach to support different phases of the design process with special attention to the integration of privacy and data protection principles. Among others, it is a generic model-based approach that can be specialized according to the specifics of different application domains.

Lane-free traffic has been recently proposed for connected automated vehicles (CAV). As incremental changes of the road width in lane-free traffic lead to corresponding incremental changes of the traffic flow capacity, the concept of internal boundary control can be used to optimize infrastructure utilization. Internal boundary control leads to flexible sharing of the total road width and capacity among the two traffic directions (of a highway or an arterial) in real-time, in response to the prevailing traffic conditions. A feedback-based Linear-Quadratic regulator with Integral action (LQI regulator) is appropriately developed in this paper to efficiently address this problem. Simulation investigations, involving a realistic highway stretch, demonstrate that the proposed simple LQI regulator is robust and very efficient.

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.

The successful realization of the climate goals agreed upon in the European Union's COP21 commitments makes a fundamental change of the European energy system necessary. In particular, for a reduction of greenhouse gas emissions over 80%, the use of renewable energies must be increased not only in the electricity sector but also across all energy sectors, such as heat and mobility. Furthermore, a progressive integration of renewable energies increases the risk of congestions in the transmission grid and makes network expansion necessary. An efficient planning for future energy systems must comprise the coupling of energy sectors as well as interdependencies of generation and transmission grid infrastructure. However, in traditional energy system planning, these aspects are considered as decoupled. Therefore, the project PlaMES develops an approach for integrated planning of multi-energy systems on a European scale. This paper aims at analyzing the model requirements and describing the modeling approach.

This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..

"Moving fast, and breaking things", instead of "being safe and secure", is the credo of the IT industry. However, if we look at the wide societal impact of IT security incidents in the past years, it seems like it is no longer sustainable. Just like in the case of Equifax, people simply forget updates, just like in the case of Maersk, companies do not use sufficient network segmentation. Security certification does not seem to help with this issue. After all, Equifax was IS027001 compliant.In this paper, we take a look at how we handle and (do not) learn from security incidents in IT security. We do this by comparing IT security incidents to early and later aviation safety. We find interesting parallels to early aviation safety, and outline the governance levers that could make the world of IT more secure, which were already successful in making flying the most secure way of transportation.

This paper presents BackFlow, a compiler-based toolchain that enforces indirect backward edge control flow integrity for low-end ARM Cortex-M microprocessors. BackFlow is implemented within the Clang/LLVM compiler and supports the ARM instruction set and its subset Thumb. The control flow integrity generated by the compiler relies on a bitmap, where each set bit indicates a valid pointer destination. The efficiency of the framework is benchmarked using an STM32 NUCLEO F446RE microcontroller. The obtained results show that the control flow integrity solution incurs an execution time overhead ranging from 1.5 to 4.5%.

The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.

Recommended systems are very popular nowadays. They are used online to help a user get the desired product quickly. Recommended Systems are found on almost every website, especially big companies such as Facebook, eBay, Amazon, NetFlix, and others. In specific cases, these systems help the user find a book, movie, article, product of his or her preference, and are also used on social networks to meet friends who share similar interests in different fields. These companies use referral systems because they bring amazing benefits in a very fast time. To generate more accurate recommendations, recommended systems are based on the user's personal information, eg: different ratings, history observation, personal profiles, etc. Use of these systems is very necessary but the way this information is received, and the privacy of this information is almost constantly ignored. Many users are unaware of how their information is received and how it is used. This paper will discuss how recommended systems work in different online companies and how safe they are to use without compromising their privacy. Given the widespread use of these systems, an important issue has arisen regarding user privacy and security. Collecting personal information from recommended systems increases the risk of unwanted exposure to that information. As a result of this paper, the reader will be aware of the functioning of Recommended systems, the way they receive and use their information, and will also discuss privacy protection techniques against Recommended systems.

Deep learning is a highly effective machine learning technique for large-scale problems. The optimization of nonconvex functions in deep learning literature is typically restricted to the class of first-order algorithms. These methods rely on gradient information because of the computational complexity associated with the second derivative Hessian matrix inversion and the memory storage required in large scale data problems. The reward for using second derivative information is that the methods can result in improved convergence properties for problems typically found in a non-convex setting such as saddle points and local minima. In this paper we introduce TRMinATR - an algorithm based on the limited memory BFGS quasi-Newton method using trust region - as an alternative to gradient descent methods. TRMinATR bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate Hessian approximations. We provide empirical results on the classification task of the MNIST dataset and show robust convergence with preferred generalization characteristics.

Wear and tear are essential in establishing the market value of an asset. From shutter counters on DSLRs to odometers inside cars, specific counters, that encode the degree of wear, exist on most products. But malicious modification of the information that they report was always a concern. Our work explores a solution to this problem by using the blockchain technology, a layered encoding of product attributes and identity-based cryptography. Merging such technologies is essential since blockchains facilitate the construction of a distributed database that is resilient to adversarial modifications, while identity-based signatures set room for a more convenient way to check the correctness of the reported values based on the name of the product and pseudonym of the owner alone. Nonetheless, we reinforce security by using ownership cards deployed around NFC tokens. Since odometer fraud is still a major practical concern, we discuss a practical scenario centered on vehicles, but the framework can be easily extended to many other assets.

Due to safety concerns and legislation implemented by various governments, the maritime sector adopted Automatic Identification System (AIS). Whilst governments and state agencies have an increasing reliance on AIS data, the underlying technology can be found to be fundamentally insecure. This study identifies and describes a number of potential attack vectors and suggests conceptual countermeasures to mitigate such attacks. With interception by Navy and Coast Guard as well as marine navigation and obstacle avoidance, the vulnerabilities within AIS call into question the multiple deployed overlapping AIS networks, and what the future holds for the protocol.

Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. transactions. This paper aims to benchmark multiple machine learning methods such as k-nearest neighbor (KNN), random forest and support vector machines (SVM), while the deep learning methods such as autoencoders, convolutional neural networks (CNN), restricted boltzmann machine (RBM) and deep belief networks (DBN). The datasets which will be used are the European (EU) Australian and German dataset. The Area Under the ROC Curve (AUC), Matthews Correlation Coefficient (MCC) and Cost of failure are the 3-evaluation metrics that would be used.

In order to be more environmentally friendly, a lot of parts and aspects of life become electrified to reduce the usage of fossil fuels. This can be seen in the increased number of electrical vehicles in everyday life. This of course only makes a positive impact on the environment, if the electricity is produced environmentally friendly and comes from renewable sources. But when the green electrical power is produced, it still needs to be transported to where it's needed, which is not necessarily near the production site. In China, one of the ways to do this transport is to use High Voltage Direct Current (HVDC) technology. This of course means, that the current has to be converted to DC before being transported to the end user. That implies that the converter stations are of great importance for the grid security. Therefore, a precise monitoring of the stations is necessary. Ideally, this could be accomplished with wireless sensor nodes with an autarkic energy supply. A role in this energy supply could be played by a thermoelectrical generator (TEG). But to assess the power generated in the specific environment, a simulation would be highly desirable, to evaluate the power gained from the temperature difference in the converter station. This paper proposes a method to simulate the generated power by combining a model for the generator with a Computational Fluid Dynamics (CFD) model converter.

Critical infrastructures of all countries unites common cyberspace. In this space, there are many threats that can disrupt the security of critical infrastructure in one country, but also cause damage in other countries. This is a reality that makes it necessary to agree on intergovernmental national views on the composition of critical infrastructures, an assessment of their security and protection. The article presents an overview of views on critical infrastructures of the United States, the European Union, the United Kingdom, and the Russian Federation, the purpose of which is to develop common positions.

This paper presents an overview of the H2020 project VESSEDIA [9] aimed at verifying the security and safety of modern connected systems also called IoT. The originality relies in using Formal Methods inherited from high-criticality applications domains to analyze the source code at different levels of intensity, to gather possible faults and weaknesses. The analysis methods are mostly exhaustive an guarantee that, after analysis, the source code of the application is error-free. This paper is structured as follows: after an introductory section 1 giving some factual data, section 2 presents the aims and the problems addressed; section 3 describes the project's use-cases and section 4 describes the proposed approach for solving these problems and the results achieved until now; finally, section 5 discusses some remaining future work.

Many techniques have been presented to protect image content confidentiality. The owner of an image encrypts it using a key and transmits the encrypted image across a network. If the recipient is authorized to access the original content of the image, he can reconstruct it losslessly. However, if during the transmission the encrypted image is noised, some parts of the image can not be deciphered. In order to localize and correct these errors, we propose an approach based on the local Shannon entropy measurement. We first analyze this measure as a function of the block-size. We provide then a full description of our blind error localization and removal process. Experimental results show that the proposed approach, based on local entropy, can be used in practice to correct noisy encrypted images, even with blocks of very small size.

The EU SoftFIRE project has built an experimentation platform for NFV and SDN experiments, tailored for testing and evaluating 5G network applications and solutions. The platform is a fully orchestrated virtualisation testbed consisting of multiple component testbeds across Europe. Users of the platform can deploy their virtualisation experiments via the platform's Middleware. This paper introduces the SoftFIRE testbed and its Middleware, and presents a set of KPI results for evaluation of experiment deployment performance.