Biblio
User engagement is recognized as an important component of the user experience, but relatively little is known about the effect of engagement on the learning outcomes of such interactions. This experimental user study examines the relationship between user engagement (UE) and comprehension in varied academic reading environments. Forty-one university students interacted with one of two sets of texts presented in 4 conditions in the context of preparing for a class assignment. Employing the User Engagement Scale (UES), we found evidence of a relationship between students' comprehension of the texts and their degree of engagement with them. However, this association was confined to one of the UES subscales and was not consistent across levels of engagement. An examination of additional variables found little evidence that system and content characteristics influenced engagement; however, we noted that all students' reported increased knowledge, but topical interest for non-engaged students declined. Results contribute to existing literature by adding further evidence that the relationship between engagement and comprehension is complex and mediated.
Funded under the European Union's Horizon 2020 research and innovation programme, SAFEcrypto will provide a new generation of practical, robust and physically secure post-quantum cryptographic solutions that ensure long-term security for future ICT systems, services and applications. The project will focus on the remarkably versatile field of Lattice-based cryptography as the source of computational hardness, and will deliver optimised public key security primitives for digital signatures and authentication, as well identity based encryption (IBE) and attribute based encryption (ABE). This will involve algorithmic and design optimisations, and implementations of lattice-based cryptographic schemes addressing cost, energy consumption, performance and physical robustness. As the National Institute of Standards and Technology (NIST) prepares for the transition to a post-quantum cryptographic suite B, urging organisations that build systems and infrastructures that require long-term security to consider this transition in architectural designs; the SAFEcrypto project will provide Proof-of-concept demonstrators of schemes for three practical real-world case studies with long-term security requirements, in the application areas of satellite communications, network security and cloud. The goal is to affirm Lattice-based cryptography as an effective replacement for traditional number-theoretic public-key cryptography, by demonstrating that it can address the needs of resource-constrained embedded applications, such as mobile and battery-operated devices, and of real-time high performance applications for cloud and network management infrastructures.
Establishing trust relationships between network participants by having them prove their operating system's integrity via a Trusted Platform Module (TPM) provides interesting approaches for securing local networks at a higher level. In the introduced approach on OSI layer 2, attacks carried out by already authenticated and participating nodes (insider threats) can be detected and prevented. Forbidden activities and manipulations in hard- and software, such as executing unknown binaries, loading additional kernel modules or even inserting unauthorized USB devices, are detected and result in an autonomous reaction of each network participant. The provided trust establishment and authentication protocol operates independently from upper protocol layers and is optimized for resource constrained machines. Well known concepts of backbone architectures can maintain the chain of trust between different kinds of network types. Each endpoint, forwarding and processing unit monitors the internal network independently and reports misbehaviours autonomously to a central instance in or outside of the trusted network.
In the area of the Internet of Things, cloud-based camera surveillance systems are ubiquitously available for industrial and private environments. However, the sensitive nature of the surveillance use case imposes high requirements on privacy/confidentiality, authenticity, and availability of such systems. In this work, we investigate how currently available mass-market camera systems comply with these requirements. Considering two attacker models, we test the cameras for weaknesses and analyze for their implications. We reverse-engineered the security implementation and discovered several vulnerabilities in every tested system. These weaknesses impair the users' privacy and, as a consequence, may also damage the camera system manufacturer's reputation. We demonstrate how an attacker can exploit these vulnerabilities to blackmail users and companies by denial-of-service attacks, injecting forged video streams, and by eavesdropping private video data - even without physical access to the device. Our analysis shows that current systems lack in practice the necessary care when implementing security for IoT devices.
To ensure reliable and predictable service in the electrical grid it is important to gauge the level of trust present within critical components and substations. Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between substations using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between substations.
Static code analysis is a convenient technique to support the development of software. Without prior test setup, information about a later runtime behavior can be inferred and errors in the code can be found before using a regular compiler. Solutions to apply static code analysis to PLC software following the IEC 61131-3 already exist, but using these separate tools usually creates a gap in the development process. In this paper we introduce an architecture to use static analysis directly in a development environment and give instant feedback to the developer while he is still editing the PLC software.
Using heterogeneous clouds has been considered to improve performance of big-data analytics for healthcare platforms. However, the problem of the delay when transferring big-data over the network needs to be addressed. The purpose of this paper is to analyze and compare existing cloud computing environments (PaaS, IaaS) in order to implement middleware services. Understanding the differences and similarities between cloud technologies will help in the interconnection of healthcare platforms. The paper provides a general overview of the techniques and interfaces for cloud computing middleware services, and proposes a cloud architecture for healthcare. Cloud middleware enables heterogeneous devices to act as data sources and to integrate data from other healthcare platforms, but specific APIs need to be developed. Furthermore, security and management problems need to be addressed, given the heterogeneous nature of the communication and computing environment. The present paper fills a gap in the electronic healthcare register literature by providing an overview of cloud computing middleware services and standardized interfaces for the integration with medical devices.
The growing volume of data and its increasing complexity require even more efficient and faster information retrieval techniques. Approximate nearest neighbor search algorithms based on hashing were proposed to query high-dimensional datasets due to its high retrieval speed and low storage cost. Recent studies promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for a heavy training process to achieve accurate query results and the critical dependency on data-parameters. In this work we execute exhaustive experiments in order to compare recent methods that are able to produces a better representation of the data space with a less computational cost for a better accuracy by computing the best data-parameter values for optimal sub-space projection exploring the correlations among CNN feature attributes using fractal theory. We give an overview of these different techniques and present our comparative experiments for data representation and retrieval performance.
Lo et al. (2011) proposed an efficient key assignment scheme for access control in a large leaf class hierarchy where the alternations in leaf classes are more frequent than in non-leaf classes in the hierarchy. Their scheme is based on the public-key cryptosystem and hash function where operations like modular exponentiations are very much costly compared to symmetric-key encryptions and decryptions, and hash computations. Their scheme performs better than the previously proposed schemes. However, in this paper, we show that Lo et al.’s scheme fails to preserve the forward security property where a security class can also derive the secret keys of its successor classes ’s even after deleting the security class from the hierarchy. We aim to propose a new key management scheme for dynamic access control in a large leaf class hierarchy, which makes use of symmetric-key cryptosystem and one-way hash function. We show that our scheme requires significantly less storage and computational overheads as compared to Lo et al.’s scheme and other related schemes. Through the informal and formal security analysis, we further show that our scheme is secure against all possible attacks including the forward security. In addition, our scheme supports efficiently dynamic access control problems compared to Lo et al.’s scheme and other related schemes. Thus, higher security along with low storage and computational costs make our scheme more suitable for practical applications compared to other schemes.
Technological advances in wearable and implanted medical devices are enabling wireless body area networks to alter the current landscape of medical and healthcare applications. These systems have the potential to significantly improve real time patient monitoring, provide accurate diagnosis and deliver faster treatment. In spite of their growth, securing the sensitive medical and patient data relayed in these networks to protect patients' privacy and safety still remains an open challenge. The resource constraints of wireless medical sensors limit the adoption of traditional security measures in this domain. In this work, we propose a distributed mobile agent based intrusion detection system to secure these networks. Specifically, our autonomous mobile agents use machine learning algorithms to perform local and network level anomaly detection to detect various security attacks targeted on healthcare systems. Simulation results show that our system performs efficiently with high detection accuracy and low energy consumption.
In this study, we are using a multi-party recording as a template for building a parametric speech synthesiser which is able to express different levels of attentiveness in backchannel tokens. This allowed us to investigate i) whether it is possible to express the same perceived level of attentiveness in synthesised than in natural backchannels; ii) whether it is possible to increase and decrease the perceived level of attentiveness of backchannels beyond the range observed in the original corpus.
This publication presents some techniques for insider threats and cryptographic protocols in secure processes. Those processes are dedicated to the information management of strategic data splitting. Strategic data splitting is dedicated to enterprise management processes as well as methods of securely storing and managing this type of data. Because usually strategic data are not enough secure and resistant for unauthorized leakage, we propose a new protocol that allows to protect data in different management structures. The presented data splitting techniques will concern cryptographic information splitting algorithms, as well as data sharing algorithms making use of cognitive data analysis techniques. The insider threats techniques will concern data reconstruction methods and cognitive data analysis techniques. Systems for the semantic analysis and secure information management will be used to conceal strategic information about the condition of the enterprise. Using the new approach, which is based on cognitive systems allow to guarantee the secure features and make the management processes more efficient.
Cybersecurity assurance plays an important role in managing trust in smart grid communication systems. In this paper, cybersecurity assurance controls for smart grid communication networks and devices are delineated from the more technical functional controls to provide insights on recent innovative risk-based approaches to cybersecurity assurance in smart grid systems. The cybersecurity assurance control baselining presented in this paper is based on requirements and guidelines of the new family of IEC 62443 standards on network and systems security of industrial automation and control systems. The paper illustrates how key cybersecurity control baselining and tailoring concepts of the U.S. NIST SP 800-53 can be adopted in smart grid security architecture. The paper outlines the application of IEC 62443 standards-based security zoning and assignment of security levels to the zones in smart grid system architectures. To manage trust in the smart grid system architecture, cybersecurity assurance base lining concepts are applied per security impact levels. Selection and justification of security assurance controls presented in the paper is utilizing the approach common in Security Technical Implementation Guides (STIGs) of the U.S. Defense Information Systems Agency. As shown in the paper, enhanced granularity for managing trust both on the overall system and subsystem levels of smart grid systems can be achieved by implementation of the instructions of the CNSSI 1253 of the U.S. Committee of National Security Systems on security categorization and control selection for national security systems.
There are many everyday situations in which users need to enter their user identification (user ID), such as logging in to computer systems and entering secure offices. In such situations, contactless passive IC cards are convenient because users can input their user ID simply by passing the card over a reader. However, these cards cannot be used for successive interactions. To address this issue, we propose AccelTag, a contactless IC card equipped with an acceleration sensor and a liquid crystal display (LCD). AccelTag utilizes high-function RFID technology so that the acceleration sensor and the LCD can also be driven by a wireless power supply. With its built-in acceleration sensor, AccelTag can acquire its direction and movement when it is waved over the reader. We demonstrate several applications using AccelTag, such as displaying several types of information in the card depending on the user's requirements.
There are over 1 billion websites today, and most of them are designed using content management systems. Cybersecurity is one of the most discussed topics when it comes to a web application and protecting the confidentiality, integrity of data has become paramount. SQLi is one of the most commonly used techniques that hackers use to exploit a security vulnerability in a web application. In this paper, we compared SQLi vulnerabilities found on the three most commonly used content management systems using a vulnerability scanner called Nikto, then SQLMAP for penetration testing. This was carried on default WordPress, Drupal and Joomla website pages installed on a LAMP server (Iocalhost). Results showed that each of the content management systems was not susceptible to SQLi attacks but gave warnings about other vulnerabilities that could be exploited. Also, we suggested practices that could be implemented to prevent SQL injections.
The need to keep an attacker oblivious of an attack mitigation effort is a very important component of a defense against denial of services (DoS) and distributed denial of services (DDoS) attacks because it helps to dissuade attackers from changing their attack patterns. Conceptually, DDoS mitigation can be achieved by two components. The first is a decoy server that provides a service function or receives attack traffic as a substitute for a legitimate server. The second is a decoy network that restricts attack traffic to the peripheries of a network, or which reroutes attack traffic to decoy servers. In this paper, we propose the use of a two-stage map table extension Locator/ID Separation Protocol (LISP) to realize a decoy network. We also describe and demonstrate how LISP can be used to implement an oblivious DDoS mitigation mechanism by adding a simple extension on the LISP MapServer. Together with decoy servers, this method can terminate DDoS traffic on the ingress end of an LISP-enabled network. We verified the effectiveness of our proposed mechanism through simulated DDoS attacks on a simple network topology. Our evaluation results indicate that the mechanism could be activated within a few seconds, and that the attack traffic can be terminated without incurring overhead on the MapServer.
Recent events have brought to light the increasingly intertwined nature of modern infrastructures. As a result much effort is being put towards protecting these vital infrastructures without which modern society suffers dire consequences. These infrastructures, due to their intricate nature, behave in complex ways. Improving their resilience and understanding their behavior requires a collaborative effort between the private sector that operates these infrastructures and the government sector that regulates them. This collaboration in the form of information sharing requires a new type of information network whose goal is in two parts to enable infrastructure operators share status information among interdependent infrastructure nodes and also allow for the sharing of vital information concerning threats and other contingencies in the form of alerts. A communication model that meets these requirements while maintaining flexibility and scalability is presented in this paper.
In Smart Grids (SGs), data aggregation process is essential in terms of limiting packet size, data transmission amount and data storage requirements. This paper presents a novel Domingo-Ferrer additive privacy based Secure Data Aggregation (SDA) scheme for Fog Computing based SGs (FCSG). The proposed protocol achieves end-to-end confidentiality while ensuring low communication and storage overhead. Data aggregation is performed at fog layer to reduce the amount of data to be processed and stored at cloud servers. As a result, the proposed protocol achieves better response time and less computational overhead compared to existing solutions. Moreover, due to hierarchical architecture of FCSG and additive homomorphic encryption consumer privacy is protected from third parties. Theoretical analysis evaluates the effects of packet size and number of packets on transmission overhead and the amount of data stored in cloud server. In parallel with the theoretical analysis, our performance evaluation results show that there is a significant improvement in terms of data transmission and storage efficiency. Moreover, security analysis proves that the proposed scheme successfully ensures the privacy of collected data.
Internet security issues require authorship identification for all kinds of internet contents; however, authorship identification for microblog users is much harder than other documents because microblog texts are too short. Moreover, when the number of candidates becomes large, i.e., big data, it will take long time to identify. Our proposed method solves these problems. The experimental results show that our method successfully identifies the authorship with 53.2% of precision out of 10,000 microblog users in the almost half execution time of previous method.