Biblio

Modern learning systems, say a tutoring platform, has many characteristics like digital data presentation with interactivity, mobility, which provides information about the study-content as per the learners understanding levels, intelligent learners behavior, etc. A sophisticated ubiquitous learner system maintains security and monitors the mischievous behavior of the learner, and authenticates and authorizes every learner, which is quintessential. Some of the existing security schemes aim only at single entry-point authentication, which may not suit to ubiquitous tutor platform. We propose a secured authentication scheme which is based on the information utility of the learner. Whenever a learner moves into a tutor platform, which has ubiquitous learner system technology, the system at first-begins with learners' identity authentication, and then it initiates trust evaluation after the successful authentication of the learner. Periodic credential verification of the learner will be carried out, which intensifies the authentication scheme of the system proposed. BAN logic has been used to prove the authentication in this system. The proposed authentication scheme has been simulated and analyzed for the indoor tutor platform environment.

Today, authentication and non-repudiation of actions are essential requirements for almost all mobile services. In this respect, various common identity systems (such as Facebook Login, Google Sign-In, Apple ID and many other) based on OpenID Connect protocol have been introduced that support easier password management for users, and reduce potential risks by securing the service provider and the user. With the widespread use of the Internet, smartphones can offer many services with rich content. The use of common identity systems on mobile devices with a high security level is becoming a more important requirement. At this point, MNOs (Mobile Network Operators) have a significant potential and capability for providing common identity services. The existing solutions based on Mobile Connect standard provide generally low level of assurance. Accordingly, there is an urgent need for a common identity system that provide higher level of assurance and security for service providers. This study presents a multi-factor authentication mechanism called TrustedID system that is based on Mobile Connect and OpenID Connect standards, and ensures higher level of assurance. The proposed system aims to use three identity factors of the user in order to access sensitive mobile services on the smartphone. The proposed authentication system will support improvement of new value-added services and also support the development of mobile ecosystem.

Modern control flow attacks circumvent existing defense mechanisms to transfer the program control to attacker chosen malicious code in the program, leaving application vulnerable to attack. Advanced attacks such as Return-Oriented Programming (ROP) attack and its variants, transfer program execution to gadgets (code-snippet that ends with return instruction). The code space to generate gadgets is large and attacks using these gadgets are Turing-complete. One big challenge to harden the program against ROP attack is to confine gadget selection to a limited locations, thus leaving the attacker to search entire code space according to payload criteria. In this paper, we present a novel approach to label the nodes of the Control-Flow Graph (CFG) of a program such that labels of the nodes on a valid control flow edge satisfy a Hamming distance property. The newly encoded CFG enables detection of illegal control flow transitions during the runtime in the processor pipeline. Experimentally, we have demonstrated that the proposed Control Flow Integrity (CFI) implementation is effective against control-flow hijacking and the technique can reduce the search space of the ROP gadgets upto 99.28%. We have also validated our technique on seven applications from MiBench and the proposed labeling mechanism incurs no instruction count overhead while, on average, it increases instruction width to a maximum of 12.13%.

RSA is a popular asymmetric key algorithm with two keys scheme, a public key for encryption and private key for decryption. RSA has weaknesses in encryption and decryption of data, including slow in the process of encryption and decryption because it uses a lot of number generation. The reason is RSA algorithm can work well and is resistant to attacks such as brute force and statistical attacks. in this paper, it aims to strengthen the scheme by combining RSA with the One Time Pad algorithm so that it will bring up a new design to be used to enhance security on two-factor authentication. Contribution in this paper is to find a new scheme algorithm for an enhanced scheme of RSA. One Time Pad and RSA can combine as well.

Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients' data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers.

With the advancement of communication technology, the interoperability of the power grid operation has improved significantly, but due to its dependence on the communication system, it is extremely vulnerable to network attacks. Among them, the false data injection attack utilizes the loophole of bad data detection in the system and attacks the state estimation system, resulting in frequent occurrence of abnormal data in the system, which brings great harm to the power grid. In view of the fact that false data injection attacks are easy to avoid traditional bad data detection methods, this paper analyzes the different situations of false data injection attacks based on the characteristics of the power grid. Firstly, it proposes to apply the distortion index method to false data injection attack detection. Experiments prove that the detection results are good and can be complementary to traditional detection methods. Then, combined with the traditional normalized residual method, this paper proposes the improved distortion index method based on the distortion index, which is good at detecting abnormal data. The use of improved distortion index method to detect false data injection attacks can make up for the defect of the lack of universality of traditional detection methods, and meet the requirements of anomaly detection efficiency. Finally, based on the MATLAB power simulation test system, experimental simulation is carried out to verify the effectiveness and universality of the proposed method for false data injection attack detection.

Unilateral spatial neglect (USN) is a higher cognitive dysfunction that can occur after a stroke. It is defined as an impairment in finding, reporting, reacting to, and directing stimuli opposite the damaged side of the brain. We have proposed a system to identify neglected regions in USN patients in three dimensions using three-dimensional virtual reality. The objectives of this study are twofold: first, to propose a system for numerically identifying the neglected regions using an object detection task in a virtual space, and second, to compare the neglected regions during object detection when the patient's neck is immobilized (‘fixed-neck’ condition) versus when the neck can be freely moved to search (‘free-neck’ condition). We performed the test using an immersive virtual reality system, once with the patient's neck fixed and once with the patient's neck free to move. Comparing the results of the study in two patients, we found that the neglected areas were similar in the fixed-neck condition. However, in the free-neck condition, one patient's neglect improved while the other patient’s neglect worsened. These results suggest that exploratory ability affects the symptoms of USN and is crucial for clinical evaluation of USN patients.

This paper investigates the effects of real time visual biofeedback for improving sports performance using a large scale immersive mixed reality system in which users are able to play a simulated game of curling. The users slide custom curling stones across the floor onto a projected target whose size is dictated by the user’s stress-related physiological measure; heart rate (HR). The higher HR the player has, the smaller the target will be, and vice-versa. In the experiment participants were asked to compete in three different conditions: baseline, with and without the proposed biofeedback. The results show that when providing a visual representation of the player’s HR or "choking" in competition, it helped the player understand their condition and improve competition performance (P-value of 0.0391).

Software} maintainers employ reverse-engineered sequence diagrams to visually understand software behavior, especially when software documentation is absent or outdated. Much research has studied the adoption of reverse-engineered sequence diagrams to visualize program interactions. However, due to the forward-engineering nature of sequence diagrams, visualizing more complex programming scenarios can be challenging. In particular, sequence diagrams represent method invocations as unidirectional arrows. However, in practice, source code may contain compound method invocations that share values/objects implicitly. For example, method invocations can be nested, e.g., fun (foo ()), or chained, e.g., fun (). foo (). The standard notation of sequence diagrams does not have enough expressive power to precisely represent compound scenarios of method invocations. Understanding the flow of information between method invocations simplifies debugging, inspection, and exception handling operations for software maintainers. Despite the research invested to address the limitations of UML sequence diagrams, previous approaches fail to visualize compound scenarios of method invocations. In this paper, we propose sequence diagram extensions to enhance the visualization of (i) three widely used types of compound method invocations in practice (i.e., nested, chained, and recursive) and (ii) lifelines of objects returned from method invocations. We aim through our extensions to increase the level of abstraction and expressiveness of method invocation code. We develop a tool to reverse engineer compound method invocations and generate the corresponding extended sequence diagrams. We evaluate how our proposed extensions can improve the understandability of program interactions using a controlled experiment. We find that program interactions are significantly more comprehensible when visualized using our extensions.

The state estimation technology is utilized to estimate the grid state based on the data of the meter and grid topology structure. The false data injection attack (FDIA) is an information attack method to disturb the security of the power system based on the meter measurement. Current FDIA detection researches pay attention on detecting its presence. The location information of FDIA is also important for power system security. In this paper, locating the FDIA of the meter is regarded as a multi-label classification problem. Each label represents the state of the corresponding meter. The ensemble model, the multi-label decision tree algorithm, is utilized as the classifier to detect the exact location of the FDIA. This method does not need the information of the power topology and statistical knowledge assumption. The numerical experiments based on the IEEE-14 bus system validates the performance of the proposed method.

Supervisory Control and Data Acquisition (SCADA) systems have been a frequent target of cyberattacks in Industrial Control Systems (ICS). As such systems are a frequent target of highly motivated attackers, researchers often resort to intrusion detection through machine learning techniques to detect new kinds of threats. However, current research initiatives, in general, pursue higher detection accuracies, neglecting the detection of new kind of threats and their proposal detection scope. This paper proposes a novel, reliable host-based intrusion detection for SCADA systems through the Operating System (OS) diversity. Our proposal evaluates, at the OS level, the SCADA communication over time and, opportunistically, detects, and chooses the most appropriate OS to be used in intrusion detection for reliability purposes. Experiments, performed through a variety of SCADA OSs front-end, shows that OS diversity provides higher intrusion detection scope, improving detection accuracy by up to 8 new attack categories. Besides, our proposal can opportunistically detect the most reliable OS that should be used for the current environment behavior, improving by up to 8%, on average, the system accuracy when compared to a single OS approach, in the best case.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants’ decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants’ trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Internet of Things (IoT) systems are becoming widely used, which makes them to be a high-value target for both hackers and crackers. From gaining access to sensitive information to using them as bots for complex attacks, the variety of advantages after exploiting different security vulnerabilities makes the security of IoT devices to be one of the most challenging desideratum for cyber security experts. In this paper, we will propose a new IoT system, designed to ensure five data principles: confidentiality, integrity, availability, authentication and authorization. The innovative aspects are both the usage of a web-based communication and a custom dynamic data request structure.

As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach.

In this paper, a time-driven performance-aware mathematical model for trust in the robot is proposed for a Human-Robot Collaboration (HRC) framework. The proposed trust model is based on both the human operator and the robot performances. The human operator’s performance is modeled based on both the physical and cognitive performances, while the robot performance is modeled over its unpredictable, predictable, dependable, and faithful operation regions. The model is validated via different simulation scenarios. The simulation results show that the trust in the robot in the HRC framework is governed by robot performance and human operator’s performance and can be improved by enhancing the robot performance.

Digital connectivity is fundamental to the health care system to deliver safe and effective care. However, insecure connectivity could be a major threat to patient safety and privacy (e.g., in August 2017, FDA recalled 465,000 pacemakers because of discovering security flaws). Although connecting a patient's pacemaker to the Internet has many advantages for monitoring the patient, this connectivity opens a new door for cyber-attackers to steal the patient data or even control the pacemaker or damage it. Therefore, patients are forced to choose between connectivity and security. This paper presents a framework for secure and private communications between wearable medical devices and patient monitoring systems. The primary objective of this research is twofold, first to identify and analyze the communication vulnerabilities, second, to develop a framework for combating unauthorized access to data through the compromising of computer security. Specifically, hiding targets from cyber-attackers could prevent our system from future cyber-attacks. This is the most effective way to stop cyber-attacks in their first step.

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-DEOBS, a JavaScript deobfuscation tool that we have built. The aim of SAFE-DEOBS is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script's intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-DEOBS through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst's toolset.

Cybersecurity of the supervisory control and data acquisition (SCADA) system, which is the key component of the cyber-physical systems (CPS), is facing big challenges and will affect the reliability of the smart grid. System reliability can be influenced by various cyber threats. In this paper, the reliability of the electric power system considering different cybersecurity issues in the SCADA system is analyzed by using Semi-Markov Process (SMP) and mean time-to-compromise (MTTC). External and insider attacks against the SCADA system are investigated with the SMP models and the results are compared. The system reliability is evaluated by reliability indexes including loss of load probability (LOLP) and expected energy not supplied (EENS) through Monte Carlo Simulations (MCS). The lurking threats of the cyberattacks are also analyzed in the study. Case studies were conducted on the IEEE Reliability Test System (RTS-96). The results show that with the increase of the MTTCs of the cyberattacks, the LOLP values decrease. When insider attacks are considered, both the LOLP and EENS values dramatically increase owing to the decreased MTTCs. The results provide insights into the establishment of the electric power system reliability enhancement strategies.

Throughout the life cycle of any technical project, the enterprise needs to assess the risks associated with its development, commissioning, operation and decommissioning. This article defines the task of researching risks in relation to the operation of a data storage subsystem in the cloud infrastructure of a geographically distributed company and the tools that are required for this. Analysts point out that, compared to 2018, in 2019 there were 3.5 times more cases of confidential information leaks from storages on unprotected (freely accessible due to incorrect configuration) servers in cloud services. The total number of compromised personal data and payment information records increased 5.4 times compared to 2018 and amounted to more than 8.35 billion records. Moreover, the share of leaks of payment information has decreased, but the percentage of leaks of personal data has grown and accounts for almost 90% of all leaks from cloud storage. On average, each unsecured service identified resulted in 33.7 million personal data records being leaked. Leaks are mainly related to misconfiguration of services and stored resources, as well as human factors. These impacts can be minimized by improving the skills of cloud storage administrators and regularly auditing storage. Despite its seeming insecurity, the cloud is a reliable way of storing data. At the same time, leaks are still occurring. According to Kaspersky Lab, every tenth (11%) data leak from the cloud became possible due to the actions of the provider, while a third of all cyber incidents in the cloud (31% in Russia and 33% in the world) were due to gullibility company employees caught up in social engineering techniques. Minimizing the risks associated with the storage of personal data is one of the main tasks when operating a company's cloud infrastructure.

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.

Current paradigms for client-server authentication often rely on username/password schemes. Studies show such schemes are increasingly vulnerable to heuristic and brute-force attacks. This is either due to poor practices by users such as insecure weak passwords, or insecure systems by server operators. A recurring problem in any system which retains information is insecure management policies for sensitive information, such as logins and passwords, by both hosts and users. Increased processing power on the horizon also threatens the security of many popular hashing algorithms. Furthermore, increasing reliance on applications that exchange sensitive information has resulted in increased urgency. This is demonstrated by a large number of mobile applications being deemed insecure by Open Web Application Security Project (OWASP) standards. This paper proposes a secure alternative technique of authentication that retains the current ecosystem, while minimizes attack vectors without inflating responsibilities on users or server operators. Our proposed authentication scheme uses layered encryption techniques alongside a two-part verification process. In addition, it provides dynamic protection for preventing against common cyber-attacks such as replay and man-in-the-middle attacks. Results show that our proposed authentication mechanism outperform other schemes in terms of deployability and resilience to cyber-attacks, without inflating transaction's speed.

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home.

Despite advances regarding autonomous functionality for robots, teleoperation remains a means for performing delicate tasks in safety critical contexts like explosive ordnance disposal (EOD) and ambiguous environments. Immersive stereoscopic displays have been proposed and developed in this regard, but bring about their own specific problems, e.g., simulator sickness. This work builds upon standardized test environments to yield reproducible comparisons between different robotic platforms. The focus was placed on testing three optronic systems of differing degrees of immersion: (1) A laptop display showing multiple monoscopic camera views, (2) an off-the-shelf virtual reality headset coupled with a pantilt-based stereoscopic camera, and (3) a so-called Telepresence Unit, providing fast pan, tilt, yaw rotation, stereoscopic view, and spatial audio. Stereoscopic systems yielded significant faster task completion only for the maneuvering task. As expected, they also induced Simulator Sickness among other results. However, the amount of Simulator Sickness varied between both stereoscopic systems. Collected data suggests that a higher degree of immersion combined with careful system design can reduce the to-be-expected increase of Simulator Sickness compared to the monoscopic camera baseline while making the interface subjectively more effective for certain tasks.

Cyber-Physical Systems (CPS) often rely on network boundary defence as a primary means of access control; therefore, the compromise of one device threatens the security of all devices within the boundary. Resource and real-time constraints, tight hardware/software coupling, and decades-long service lifetimes complicate efforts for more robust, host-based access control mechanisms. Distributed capability systems provide opportunities for restoring access control to resource-owning devices; however, such a protection model requires a capability-based architecture for CPS devices as well as task compartmentalisation to be effective.This paper demonstrates hardware enforcement of network bearer tokens using an efficient translation between CHERI (Capability Hardware Enhanced RISC Instructions) architectural capabilities and Macaroon network tokens. While this method appears to generalise to any network-based access control problem, we specifically consider CPS, as our method is well-suited for controlling resources in the physical domain. We demonstrate the method in a distributed robotics application and in a hierarchical industrial control application, and discuss our plans to evaluate and extend the method.

Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes.