Biblio
Security has become the vital component of today's technology. People wish to safeguard their valuable items in bank lockers. With growing technology most of the banks have replaced the manual lockers by digital lockers. Even though there are numerous biometric approaches, these are not robust. In this work we propose a new approach for personal biometric identification based on features extracted from ECG.
This research provides security and safety extensions to a blockchain based solution whose target is e-health. The Advanced Blockchain platform is extended with intelligent monitoring for security and machine learning for detecting patient treatment medication safety issues. For the reasons of stringent HIPAA, HITECH, EU-GDPR and other regional regulations dictating security, safety and privacy requirements, the e-Health blockchains have to cover mandatory disclosure of violations or enforcements of policies during transaction flows involving healthcare. Our service solution further provides the benefits of resolving the abnormal flows of a medical treatment process, providing accountability of the service providers, enabling a trust health information environment for institutions to handle medication safely, giving patients a better safety guarantee, and enabling the authorities to supervise the security and safety of e-Health blockchains. The capabilities can be generalized to support a uniform smart solution across industry in a variety of blockchain applications.
Improved safety, high mobility and environmental concerns in transportation systems across the world and the corresponding developments in information and communication technologies continue to drive attention towards Intelligent Transportation Systems (ITS). This is evident in advanced driver-assistance systems such as lane departure warning, adaptive cruise control and collision avoidance. However, in connected and autonomous vehicles, the efficient functionality of these applications depends largely on the ability of a vehicle to accurately predict it operating parameters such as location and speed. The ability to predict the immediate future/next location (or speed) of a vehicle or its ability to predict neighbors help in guaranteeing integrity, availability and accountability, thus boosting safety and resiliency of the Vehicular Network for Mobile Cyber Physical Systems (VCPS). In this paper, we proposed a secure movement-prediction for connected vehicles by using Kalman filter. Specifically, Kalman filter predicts the locations and speeds of individual vehicles with reference to already observed and known information such posted legal speed limit, geographic/road location, direction etc. The aim is to achieve resilience through the predicted and exchanged information between connected moving vehicles in an adaptive manner. By being able to predict their future locations, the following vehicle is able to adjust its position more accurately to avoid collision and to ensure optimal information exchange among vehicles.
With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.
Advancements in the AI field unfold tremendous opportunities for society. Simultaneously, it becomes increasingly important to address emerging ramifications. Thereby, the focus is often set on ethical and safe design forestalling unintentional failures. However, cybersecurity-oriented approaches to AI safety additionally consider instantiations of intentional malice – including unethical malevolent AI design. Recently, an analogous emphasis on malicious actors has been expressed regarding security and safety for virtual reality (VR). In this vein, while the intersection of AI and VR (AIVR) offers a wide array of beneficial cross-fertilization possibilities, it is responsible to anticipate future malicious AIVR design from the onset on given the potential socio-psycho-technological impacts. For a simplified illustration, this paper analyzes the conceivable use case of Generative AI (here deepfake techniques) utilized for disinformation in immersive journalism. In our view, defenses against such future AIVR safety risks related to falsehood in immersive settings should be transdisciplinarily conceived from an immersive co-creation stance. As a first step, we motivate a cybersecurity-oriented procedure to generate defenses via immersive design fictions. Overall, there may be no panacea but updatable transdisciplinary tools including AIVR itself could be used to incrementally defend against malicious actors in AIVR.
New technologies, such as augmented reality (AR) are used to enhance human capabilities and extend human functioning; nevertheless they may cause distraction and incorrect human functioning. Systems including socio entities (such as human) and technical entities (such as augmented reality) are called socio-technical systems. In order to do risk assessment in such systems, considering new dependability threats caused by augmented reality is essential, for example failure of an extended human function is a new type of dependability threat introduced to the system because of new technologies. In particular, it is required to identify these new dependability threats and extend modeling and analyzing techniques to be able to uncover their potential impacts. This research aims at providing a framework for risk assessment in AR-equipped socio-technical systems by identifying AR-extended human failures and AR-caused faults leading to human failures. Our work also extends modeling elements in an existing metamodel for modeling socio-technical systems, to enable AR-relevant dependability threats modeling. This extended metamodel is expected to be used for extending analysis techniques to analyze AR-equipped socio-technical systems.
Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.
Verifying complex Cyber-Physical Systems (CPS) is increasingly important given the push to deploy safety-critical autonomous features. Unfortunately, traditional verification methods do not scale to the complexity of these systems and do not provide systematic methods to protect verified properties when not all the components can be verified. To address these challenges, this paper proposes a real-time mixed-trust computing framework that combines verification and protection. The framework introduces a new task model, where an application task can have both an untrusted and a trusted part. The untrusted part allows complex computations supported by a full OS with a realtime scheduler running in a VM hosted by a trusted hypervisor. The trusted part is executed by another scheduler within the hypervisor and is thus protected from the untrusted part. If the untrusted part fails to finish by a specific time, the trusted part is activated to preserve safety (e.g., prevent a crash) including its timing guarantees. This framework is the first allowing the use of untrusted components for CPS critical functions while preserving logical and timing guarantees, even in the presence of malicious attackers. We present the framework design and implementation along with the schedulability analysis and the coordination protocol between the trusted and untrusted parts. We also present our Raspberry Pi 3 implementation along with experiments showing the behavior of the system under failures of untrusted components, and a drone application to demonstrate its practicality.
Trust is an important topic in medical human-robot interaction, since patients may be more fragile than other groups of people. This paper investigates the issue of users' trust when interacting with a rehabilitation robot. In the study, we investigate participants' heart rate and perception of safety in a scenario when their arm is led by the rehabilitation robot in two types of exercises at three different velocities. The participants' heart rate are measured during each exercise and the participants are asked how safe they feel after each exercise. The results showed that velocity and type of exercise has no significant influence on the participants' heart rate, but they do have significant influence on how safe they feel. We found that increasing velocity and longer exercises negatively influence participants' perception of safety.
Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).
Recently, the novel networking technology Software-Defined Networking(SDN) and Service Function Chaining(SFC) are rapidly growing, and security issues are also emerging for SDN and SFC. However, the research about security and safety on a novel networking environment is still unsatisfactory, and the vulnerabilities have been revealed continuously. Among these security issues, this paper addresses the ARP Poisoning attack to exploit SFC vulnerability, and proposes a method to defend the attack. The proposed method recognizes the repetitive ARP reply which is a feature of ARP Poisoning attack, and detects ARP Poisoning attack. The proposed method overcomes the limitations of the existing detection methods. The proposed method also detects the presence of an attack more accurately.
Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.
Vehicular Adhoc Network (VANET), a specialized form of MANET in which safety is the major concern as critical information related to driver's safety and assistance need to be disseminated between the vehicle nodes. The security of the nodes can be increased, if the network availability is increased. The availability of the network is decreased, if there is Denial of Service Attacks (DoS) in the network. In this paper, a packet detection algorithm for the prevention of DoS attacks is proposed. This algorithm will be able to detect the multiple malicious nodes in the network which are sending irrelevant packets to jam the network and that will eventually stop the network to send the safety messages. The proposed algorithm was simulated in NS-2 and the quantitative values of packet delivery ratio, packet loss ratio, network throughput proves that the proposed algorithm enhance the security of the network by detecting the DoS attack well in time.
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.