Biblio

With the emergence of new digital trends like Internet of Things (IoT), more industry actors and technical committees pursue research in utilising such technologies as they promise a better and optimised management, improved energy efficiency and a better quality living through a wide array of value-added services. However, as sensing, actuation, communication and control become increasingly more sophisticated, such promising data-driven systems generate, process, and exchange larger amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. In turn this affirms the importance of trustworthiness in IoT and emphasises the need of a solid technical and regulatory foundation. The goal of this paper is to first introduce the concept of trustworthiness in IoT, its main pillars namely, security, privacy and data protection, and then analyse the state-of-the-art in research and standardisation for each of these subareas. Throughout the paper, we develop and refer to Unmanned Aerial Vehicles (UAVs) as a promising value-added service example of mobile IoT devices. The paper then presents a thorough gap analysis and concludes with recommendations for future work.

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

The purpose of this study was to propose a model of development of trust in social robots. Insights in interpersonal trust were adopted from social psychology and a novel model was proposed. In addition, this study aimed to investigate the relationship among trust development and self-esteem. To validate the proposed model, an experiment using a communication robot NAO was conducted and changes in categories of trust as well as self-esteem were measured. Results showed that general and category trust have been developed in the early phase. Self-esteem is also increased along the interactions with the robot.

More and more organizations and individuals start to pay attention to real-time threat intelligence to protect themselves from the complicated, organized, persistent and weaponized cyber attacks. However, most users worry about the trustworthiness of threat intelligence provided by TISPs (Threat Intelligence Sharing Platforms). The trust evaluation mechanism has become a hot topic in applications of TISPs. However, most current TISPs do not present any practical solution for trust evaluation of threat intelligence itself. In this paper, we propose a graph mining-based trust evaluation mechanism with multidimensional features for large-scale heterogeneous threat intelligence. This mechanism provides a feasible scheme and achieves the task of trust evaluation for TISP, through the integration of a trust-aware intelligence architecture model, a graph mining-based intelligence feature extraction method, and an automatic and interpretable trust evaluation algorithm. We implement this trust evaluation mechanism in a practical TISP (called GTTI), and evaluate the performance of our system on a real-world dataset from three popular cyber threat intelligence sharing platforms. Experimental results show that our mechanism can achieve 92.83% precision and 93.84% recall in trust evaluation. To the best of our knowledge, this work is the first to evaluate the trust level of heterogeneous threat intelligence automatically from the perspective of graph mining with multidimensional features including source, content, time, and feedback. Our work is beneficial to provide assistance on intelligence quality for the decision-making of human analysts, build a trust-aware threat intelligence sharing platform, and enhance the availability of heterogeneous threat intelligence to protect organizations against cyberspace attacks effectively.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

Smart cities comprise multiple critical infrastructures, two of which are the power grid and communication networks, backed by centralized data analytics and storage. To effectively model the interdependencies between these infrastructures and enable a greater understanding of how communities respond to and impact them, large amounts of varied, real-world data on residential and commercial consumer energy consumption, load patterns, and associated human behavioral impacts are required. The dissemination of such data to the research communities is, however, largely restricted because of security and privacy concerns. This paper creates an opportunity for the development and dissemination of synthetic energy consumption data which is inherently anonymous but holds similarities to the properties of real data. This paper explores a framework using mixture density network (MDN) model integrated with a multi-layered Long Short-Term Memory (LSTM) network which shows promise in this area of research. The model is trained using an initial sample recorded from residential smart meters in the state of Florida, and is used to generate fully synthetic energy consumption data. The synthesized data will be made publicly available for interested users.

In order to standardize and describe vulnerability information in detail as far as possible and realize knowledge sharing, reuse and extension at the semantic level, a vulnerability ontology is constructed based on the information security public databases such as CVE, CWE and CAPEC and industry public standards like CVSS. By analyzing the relationship between vulnerability class and weakness class, inference rules are defined to realize knowledge inference from vulnerability instance to its consequence and from one vulnerability instance to another vulnerability instance. The experimental results show that this model can analyze the causal and congeneric relationships between vulnerability instances, which is helpful to repair vulnerabilities and predict attacks.

Real-Time Ethernet has become the major communication technology for modern automation and industrial control systems. On the one hand, this trend increases the need for an automation-friendly security solution, as such networks can no longer be considered sufficiently isolated. On the other hand, it shows that, despite diverging requirements, the domain of Operational Technology (OT) can derive advantage from high-volume technology of the Information Technology (IT) domain. Based on these two sides of the same coin, we study the challenges and prospects of approaches to communication security in real-time Ethernet automation systems. In order to capitalize the expertise aggregated in decades of research and development, we put a special focus on the reuse of well-established security technology from the IT domain. We argue that enhancing such technology to become automation-friendly is likely to result in more robust and secure designs than greenfield designs. Because of its widespread deployment and the (to this date) nonexistence of a consistent security architecture, we use PROFINET as a showcase of our considerations. Security requirements for this technology are defined and different well-known solutions are examined according their suitability for PROFINET. Based on these findings, we elaborate the necessary adaptions for the deployment on PROFINET.

In Compositional data, the relative proportions of the components contain important relevant information. In such case, Euclidian distance fails to capture variation when considered within data science models and approaches such as partial least squares discriminant analysis (PLS-DA). Indeed, the Euclidean distance assumes implicitly that the data is normally distributed which is not the case of compositional vectors. Aitchison transformation has been considered as a standard in compositional data analysis. In this paper, we consider two other transformation methods, Isometric log ratio (ILR) transformation and data-based power (alpha) transformation, before feeding the data to PLS-DA algorithm for classification [1]. In order to investigate the merits of both methods, we apply them in two challenging information system security applications namely spam filtering and intrusion detection.

Several assessment techniques and methodologies exist to analyze the security of an application dynamically. However, they either are focused on a particular product or are mainly concerned about the assessment process rather than the product's security confidence. Most crucially, they tend to assess the security of a target application as a standalone artifact without assessing its host infrastructure. Such attempts can undervalue the overall security posture since the infrastructure becomes crucial when it hosts a critical application. We present an ontology-based security model that aims to provide the necessary knowledge, including network settings, application configurations, testing techniques and tools, and security metrics to evaluate the security aptitude of a critical application in the context of its hosting infrastructure. The objective is to integrate the current good practices and standards in security testing and virtualization to furnish an on-demand and test-ready virtual target infrastructure to execute the critical application and to initiate a context-aware and quantifiable security assessment process in an automated manner. Furthermore, we present a security assessment architecture to reflect on how the ontology can be integrated into a standard process.

Research into the established area of ITS is evolving into the Internet of Vehicles (IoV), itself a fast-moving research area, fuelled in part by rapid changes in computing and communication technologies. Using pseudonym certificate is a popular way to address privacy issues in IoV. Therefore, the certificate management scheme is considered as a feasible technique to manage system and maintain the lifecycle of certificate. In this paper, we propose an efficient pseudonym certificate management scheme in IoV. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks.

Model validation, though a process that's continuous and complex, establishes confidence in the soundness and usefulness of a model. Making sure that the model behaves similar to the modes of behavior seen in real systems, allows the builder of said model to assure accumulation of confidence in the model and thus validating the model. While doing this, the model builder is also required to build confidence from a target audience in the model through communicating to the bases. The basis of the system dynamics model validation, both in general and in the field of cyber security, relies on a casual loop diagram of the system being agreed upon by a group of experts. Model validation also uses formal quantitative and informal qualitative tools in addition to the validation techniques used by system dynamics. Amongst others, the usefulness of a model, in a user's eyes, is a valid standard by which we can evaluate them. To validate our system dynamics cyber security model, we used empirical structural and behavior tests. This paper describes tests of model structure and model behavior, which includes each test's purpose, the ways the tests were conducted, and empirical validation results using a proof-of-concept cyber security model.

Technical debt is an analogy introduced in 1992 by Cunningham to help explain how intentional decisions not to follow a gold standard or best practice in order to save time or effort during creation of software can later on lead to a product of lower quality in terms of product quality itself, reliability, maintainability or extensibility. Little work has been done so far that applies this analogy to cyber physical (production) systems (CP(P)S). Also there is only little work that uses this analogy for security related issues. This work aims to fill this gap: We want to find out which security related symptoms within the field of cyber physical production systems can be traced back to TD items during all phases, from requirements and design down to maintenance and operation. This work shall support experts from the field by being a first step in exploring the relationship between not following security best practices and concrete increase of costs due to TD as consequence.

As the connectivity within manufacturing processes increases in light of Industry 4.0, information security becomes a pressing issue for product suppliers, systems integrators, and asset owners. Reaching new heights in digitizing the manufacturing industry also provides more targets for cyber attacks, hence, cyber-physical production systems (CPPSs) must be adequately secured to prevent malicious acts. To achieve a sufficient level of security, proper defense mechanisms must be integrated already early on in the systems' lifecycle and not just eventually in the operation phase. Although standardization efforts exist with the objective of guiding involved stakeholders toward the establishment of a holistic industrial security concept (e.g., IEC 62443), a dedicated security development lifecycle for systems integrators is missing. This represents a major challenge for engineers who lack sufficient information security knowledge, as they may not be able to identify security-related activities that can be performed along the production systems engineering (PSE) process. In this paper, we propose a novel methodology named Security Development Lifecycle for Cyber-Physical Production Systems (SDL-CPPS) that aims to foster security by design for CPPSs, i.e., the engineering of smart production systems with security in mind. More specifically, we derive security-related activities based on (i) security standards and guidelines, and (ii) relevant literature, leading to a security-improved PSE process that can be implemented by systems integrators. Furthermore, this paper informs domain experts on how they can conduct these security-enhancing activities and provides pointers to relevant works that may fill the potential knowledge gap. Finally, we review the proposed approach by means of discussions in a workshop setting with technical managers of an Austrian-based systems integrator to identify barriers to adopting the SDL-CPPS.

The behavior of a differentially private system is governed by a parameter epsilon which sets a balance between protecting the privacy of individuals and returning accurate results. While a system owner may use a number of heuristics to select epsilon, existing techniques may be unresponsive to the needs of the users who's data is at risk. A promising alternative is to allow users to express their preferences for epsilon. In a system we call epsilon voting, users report the parameter values they want to a chooser mechanism, which aggregates them into a single value. We apply techniques from mechanism design to ask whether such a chooser mechanism can itself be truthful, private, anonymous, and also responsive to users. Without imposing restrictions on user preferences, the only feasible mechanisms belong to a class we call randomized dictatorships with phantoms. This is a restrictive class in which at most one user has any effect on the chosen epsilon. On the other hand, when users exhibit single-peaked preferences, a broader class of mechanisms - ones that generalize the median and other order statistics - becomes possible.

Modern automotive architectures are complex and often comprise of hundreds of electronic control units (ECUs). These ECUs provide diverse services including infotainment, telematics, diagnostics, advanced driving assistance, and many others. The availability of such services is mainly attained by the increasing connectivity with the external world, thus expanding the attack surface. In recent years, automotive original equipment manufacturers (OEMs) and ECU suppliers have become cautious of cyber attacks and have begun fortifying the most vulnerable systems, with hardware-based security modules that enable sandboxing, secure boot, secure software updates and end-to-end message authentication. Nevertheless, insecure legacy ECUs are still in-use in modern vehicles due to price and design complexity issues. Legacy ECUs depend on simple microcontrollers, that lack any kind of hardware-based security. This makes it essential to bridge the gap between modern and legacy ECUs through software-based security by which cyber attacks can be mitigated, thus enhancing the security of vehicles. This paper provides one more step towards highly secure vehicles by introducing a lightweight software- based security framework which provides legacy ECUs with software-based virtualization and protection features along with custom security services. We discuss the motivation for pure software-based approaches, explore the various requirements and advantages obtained, and give an initial insight of the design rationale. Furthermore, we provide a proof of concept implementation and evaluation with a demonstrative use case illustrating the importance of such framework in delivering new diagnostics security services to legacy ECUs.

As nuclear power plant Instrumentation and Control(I&C) systems have turned into digital systems, the possibility of cyber-attacks has increased. To protect the nuclear power plant from cyber-attacks, digital assets are classified and managed as critical digital assets which have safety, security and emergency preparedness functions. However, critical digital assets represent 70-80% of total digital assets, and applying and managing the same security control is inefficient. Therefore, this paper presents the criteria for identifying digital assets that are classified as vital digital assets that can directly affect the serious accidents of nuclear power plants.

Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by enabling vehicles to broadcast information-such as speed, location and heading-through inter-vehicular communications to proactively avoid collisions. However, the attacks targeting these networks might overshadow their advantages if not protected against. One powerful threat against VANETs is vehicular botnets. In our earlier work, we demonstrated several vehicular botnet attacks that can have damaging impacts on the security and privacy of VANETs. In this paper, we present SHIELDNET, the first detection mechanism against vehicular botnets. Similar to the detection approaches against Internet botnets, we target the vehicular botnet communication and use several machine learning techniques to identify vehicular bots. We show via simulation that SHIELDNET can identify 77 percent of the vehicular bots. We propose several improvements on the VANET standards and show that their existing vulnerabilities make an effective defense against vehicular botnets infeasible.

Internet of Things devices have spread to near ubiquity this decade. All around us now lies an invisible mesh of communication from devices embedded in seemingly everything. Inevitably some of that communication flying around our heads will contain data that must be protected or otherwise shielded from tampering. The responsibility to protect this sensitive information from malicious actors as it travels through the air then falls upon the standards used to communicate this data. Bluetooth Low Energy (BLE) is one of these standards, the aim of this paper is to put its security standards to test. By attempting to exploit its vulnerabilities we can see how secure this standard really is. In this paper, we present steps for analyzing the security of BLE devices using open-source hardware and software.

The notion of attribute-based encryption with outsourced decryption (OD-ABE) was proposed by Green, Hohenberger, and Waters. In OD-ABE, the ABE ciphertext is converted to a partially-decrypted ciphertext that has a shorter bit length and a faster decryption time than that of the ABE ciphertext. In particular, the transformation can be performed by a powerful third party with a public transformation key. In this paper, we propose a generic approach for constructing ABE with outsourced decryption from standard ABE, as long as the later satisfies some additional properties. Its security can be reduced to the underlying standard ABE in the selective security model by a black-box way. To avoid the drawback of selective security in practice, we further propose a modified decryption outsourcing mode so that our generic construction can be adapted to satisfying adaptive security. This partially solves the open problem of constructing an OD-ABE scheme, and its adaptive security can be reduced to the underlying ABE scheme in a black-box way. Then, we present some concrete constructions that not only encompass existing ABE outsourcing schemes of Green et al., but also result in new selectively/adaptively-secure OD-ABE schemes with more efficient transformation key generation algorithm. Finally, we use the PBC library to test the efficiency of our schemes and compare the results with some previous ones, which shows that our schemes are more efficient in terms of decryption outsourcing and transformation key generation.

This paper introduces a secured and distributed Big Data storage scheme with multiple authorizations. It divides the Big Data into small chunks and distributes them through multiple Cloud locations. The Shamir's Secret Sharing and Secure Hash Algorithm are employed to provide the security and authenticity of this work. The proposed methodology consists of two phases: the distribution and retrieving phases. The distribution phase comprises three operations of dividing, encrypting, and distribution. The retrieving phase performs collecting and verifying operations. To increase the security level, the encryption key is divided into secret shares using Shamir's Algorithm. Moreover, the Secure Hash Algorithm is used to verify the Big Data after retrieving from the Cloud. The experimental results show that the proposed design can reconstruct a distributed Big Data with good speed while conserving the security and authenticity properties.

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.

Point of Sale (POS) systems has become the technology of choice for most businesses and offering number of advantages over traditional cash registers. They manage staffs, customers, transaction, inventory, sale and labor reporting, price adjustment, as well as keeping track of cash flow, expense management, reducing human errors and more. Whether traditional on-premise POS, or Cloud-Bases POS, they help businesses to run more efficiently. However, despite all these advantages, POS systems are becoming targets of a number of cyber-attacks. Security of a POS system is a key requirement of the Payment Card Industry Data Security Standard (PCI DSS). This paper undertakes research into the PCI DSS and its accompanying standards, in an attempt to break or bypass security measures using varying degrees of vulnerability and penetration attacks in a methodological format. The resounding goal of this experimentation is to achieve a basis from which attacks can be made against a realistic networking environment from whence an intruder can bypass security measures thus exposing a vulnerability in the PCI DSS and potentially exposing confidential customer payment information.

Differential Power Analysis (DPA) attacks were shown to be effective in recovering the secret key information from a variety cryptographic systems. In response, several design methods, ranging from the cell level to the algorithmic level, have been proposed to defend against DPA attacks. Cell level solutions depend on DPA resistant cell designs which attempt to minimize power variance during transitions while minimizing area and power consumption. In this paper, we discuss how a differential circuit design style is incorporated into a COTS tool set, resulting in a fully automated synthesis system DPA resistant integrated circuits. Based on the Secure Differential Multiplexer Logic (SDMLp), this system can be used to synthesize complete cryptographic processors which provide strong defense against DPA while minimizing area and power overhead. We discuss how both combinational and sequential cells are incorporated in the cell library. We show the effectiveness of the tool chain by using it to automatically synthesize the layouts, from RT level Verilog specifications, of both the DES and AES encryption ICs in 90nm CMOS. In each case, we present experimental data to demonstrate DPA attack resistance and area, power and performance overhead and compare these with circuits synthesized in another differential logic called MDPL as well as standard CMOS synthesis results.