1,721,208 research outputs found
Proceedings of the 2010 International Conference on Emerging Security Technologies (EST 2010) - Robots and Security (ROBOSEC 2010) - Learning and Adaptive Behavior in Robotic Systems (LAB-RS 2010)
No full textInternational Workshop: ModEasy ’07: Model Driven Design for Automotive Safety Embedded Systems
No full textMulti-factor Authentication using Accelerometers for the Internet-of-Things
Full text linkEmbedded and mobile devices forming part of the Internet-of-Things (IoT) need new authentication technologies and techniques. This requirement is due to the increase in effort and time attackers will use to compromise a device, often remote, based on the possibility of a significant monetary return. This paper proposes exploiting a device’s accelerometers in-built functionality to implement multi-factor authentication. An experimental embedded system designed to emulate a typical mobile device is used to implement the ideas and investigated as proof-of-concept
Strategies for template-free direct biometric encryption using voice based features
Full text linkCurrent biometric systems references information stored on templates and it possess major drawbacks in their inability, if lost or stolen, to be revoked and re-issued as would be the case with passwords. Thus, once a biometric source has been compromised, the owner of the biometric, as well as the data protected by the biometric, is compromised for life. This research investigates the potential of the voice modality for employment in a template-!ree biometric system. which requires storage of neither hiometric templates nor encryption keys. It 'rl'orks by directly encrypting the biometric data provided by the human voice and therefore eliminates the need for storing templates used for data validation, and thus increasing the security of the system. The research also introduces feature concatenation as a novel method of combining the binary information from the feature sets and also introduces five new revocation strategies based on the template-free method The promising results allow us to conclude that voice may potentially form the basis for a practical template-free biometric system
Encryption key generation inCloud Environments
No full textProtecting Cloud services located within the Cloud Computing centre easily would be a significant advantage in the current Cloud computing market. However, the existing encryption system all process a notable weakness that the private key must be stored locally, so could be accessed and used to break the encryption. To solve this problem, a novel technology has been investigated that recompose the private key by using the properties and behaviours extracted from a Cloud server during execution. This thesis will investigate the feasibility of this approach by analysing simple online programs which would typically form the basis or components of larger systems and thereby indicate, by the ability to distinguish such simple systems, which larger real world practical systems may also be distinguished. The private key does not need to store in the system, which this paper has proved such a system is feasible to be applied in the current encryption system
Investigation of Multimodal Template-Free Biometric Techniques and Associated Exception Handling
Full text linkThe Biometric systems are commonly used as a fundamental tool by both government and private sector organizations to allow restricted access to sensitive areas, to identify the criminals by the police and to authenticate the identification of individuals requesting to access to certain personal and confidential services. The applications of these identification tools have created issues of security and privacy relating to personal, commercial and government identities. Over the last decade, reports of increasing insecurity to the personal data of users in the public and commercial domain applications has prompted the development of more robust and sound measures to protect the personal data of users from being stolen and spoofing. The present study aimed to introduce the scheme for integrating direct and indirect biometric key generation schemes with the application of Shamir‘s secret sharing algorithm in order to address the two disadvantages: revocability of the biometric key and the exception handling of biometric modality. This study used two different approaches for key generation using Shamir‘s secret sharing scheme: template based approach for indirect key generation and template-free. The findings of this study demonstrated that the encryption key generated by the proposed system was not required to be stored in the database which prevented the attack on the privacy of the data of the individuals from the hackers. Interestingly, the proposed system was also able to generate multiple encryption keys with varying lengths. Furthermore, the results of this study also offered the flexibility of providing the multiple keys for different applications for each user. The results from this study, consequently, showed the considerable potential and prospect of the proposed scheme to generate encryption keys directly and indirectly from the biometric samples, which could enhance its success in biometric security field
An enhanced AODV protocol for external communication in self-driving vehicles
Full text linkThe increasing number of autonomous and semi-autonomous vehicles on the road leads to an increasing need for external vehicle communication, in particular through emerging vehicular ad hoc networks also known as VANETs. This technology has the ability to facilitate intelligent transportation applications, comfort and other required services for self-driving vehicles. However, suitable routing protocols need to be utilised in order to provide stable routing and enable high performance for this external communication in autonomous vehicles. Ad hoc on Demand Distance Vector routing (AODV) is to date rarely used in mobile ad hoc network but offers great potential as a reactive routing protocol. However, the AODV protocol is affected by poor performance, when directly employed in VANETs. In this paper, two improvements are presented to the route selection and route discovery of AODV to improve its performance in forms of packet delivery rate and communication link stability for VANETs. Thus, we obtain new vehicle V-AODV that suits the specific requirements of autonomous vehicles communications. Simulation results demonstrate that V-AODV can enhance the route stability, reduce overhead and improve communication performance between vehicles
Exploring Novel Device Authentication Techniques for General Computing Devices
Full text linkSecure device authentication is one of the top challenges worldwide from a security and privacy point of view. For the provisioning of security services, cryptographic methods have traditionally relied on keys stored in the devices. These keys are vulnerable to attack since they are seldom protected.
This thesis investigates the feasibility to enhance device security. The recommended framework makes use of novel Integrated Circuit Metrics (ICMetrics) technology, which leverages measurable features and properties of a device. Low level device features are used to build an identity for the device through the use of the ICMetrics. This technology specialises in deriving strong device identity to prevent all forms of skimming and malware attacks.
Firstly, the research contribution is to examine the suitability of employing various low level behavioural characteristics or features derived from wearable and general computing devices. The novelty offered by this research enables the utilization of dynamic features instead of solely relying on static features. Additionally, the feature characteristics need not remain absolutely constant but are free to vary within deduced parameters, thus allowing the software to operate in several states and on a variety of platforms. To increase the complexity of the generated ICMetrics, the extracted feature values are subjected to statistical and mathematical analysis. Another fundamental problem solved by ICMetrics is the generation of stable and unique digital identities from features that are unstable. Potential features that might be used for device identification were the initial point of focus, which was followed by a study of the feature extraction strategy and multimodal properties. The modular dataset made it easier to assess how reliable the device identification was. The security system is analysed and tested during this phase in order to measure its efficacy. In other words, it is tested using a dataset that was captured directly from the computing devices. The accuracy rate and confusion matrix, are calculated in this phase. The investigation showed that the suggested model outperformed all other model for identifying devices. The accuracy results obtained for the second and third feature sets of the proposed model are 91.5%, 92%, and 80.3% respectively.
The thesis also investigates the effectiveness of employing measured hardware features mapped into the frequency domain for device identification. Discrete Wavelet Transform (DWT) coefficients are used as differentiating features in the approach. In this thesis, the proposed model of multivariate Gaussian distribution is used to describe the analysis process and its mathematical application. Hardware characteristics were investigated. Wavelet-based features were leveraged. The analysis and comparison of classifiers revealed that they behave differently on the same dataset. Overall, wavelet features outperform raw features, and Sym2 and DB2 are the two wavelets that perform the best.
Finally, because the sample data was stored on the device, an efficient technique for data security had to be implemented. A decision was taken to employ the homomorphic encryption (HE) algorithm. The method fulfils the requirements for data protection
Improving less constrained iris recognition
Full text linkThe iris has been one of the most reliable biometric traits for automatic human authentication due to its highly stable and distinctive patterns. Traditional iris recognition algorithms have achieved remarkable performance in strictly constrained environments, with the subject standing still and with the iris captured at a close distance. This enables the wide deployment of iris recognition systems in applications such as border control and access control. However, in less constrained environments with the subject at-a-distance and on-the-move, the iris recognition performance is significantly deteriorated, since such environments induce noise and degradations in iris captures. This restricts the applicability and practicality of iris recognition technology for some real-world applications with more open capturing conditions, such as surveillance, forensic and mobile device security applications. Therefore, robust algorithms for less constrained iris recognition are desirable for the wider deployment of iris recognition systems.
This thesis focuses on improving less constrained iris recognition. Five methods are proposed to improve the performance of different stages in less constrained iris recognition. First, a robust iris segmentation algorithm is developed using l1-norm regression and model selection. This algorithm formulates iris segmentation as robust l1-norm regression problems. To further enhance the robustness, multiple segmentation results are produced by applying l1-norm regression to different models, and a model selection technique is used to select the most reliable result. Second, an iris liveness detection method using regional features is investigated. This method seeks not only low level features, but also high level feature distributions for more accurate and robust iris liveness detection. Third, a signal-level information fusion algorithm is presented to mitigate the noise in less constrained iris captures. With multiple noisy iris captures, this algorithm proposes a sparse-error low rank matrix factorization model to separate noiseless iris structures and noise. The noiseless structures are preserved and emphasised during the fusion process, while the noise is suppressed, in order to obtain more reliable signals for recognition. Fourth, a method to generate optimal iris codes is proposed. This method considers iris code generation from the perspective of optimization. It formulates traditional iris code generation method as an optimization problem; an additional objective term modelling the spatial correlations in iris codes is applied to this optimization problem to produce more effective iris codes. Fifth, an iris weight map method is studied for robust iris matching. This method considers both intra-class bit stability and inter-class bit discriminability in iris codes. It emphasises highly stable and discriminative bits for iris matching, enhancing the robustness of iris matching.
Comprehensive experimental analysis are performed on benchmark datasets for each of the above methods. The results indicate that the presented methods are effective for less constrained iris recognition, generally improving state-of-the-art performance
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