PROACTIVE BIOMETRIC-ENABLED FORENSIC IMPRINTING SYSTEM

Insider threats are a significant security issue. The last decade has witnessed countless instances of data loss and exposure in which leaked data have become publicly available and easily accessible. Losing or disclosing sensitive data or confidential information may cause substantial financial and reputational damage to a company. Therefore, preventing or responding to such incidents has become a challenging task. Whilst more recent research has focused explicitly on the problem of insider misuse, it has tended to concentrate on the information itself—either through its protection or approaches to detecting leakage. Although digital forensics has become a de facto standard in the investigation of criminal activities, a fundamental problem is not being able to associate a specific person with particular electronic evidence, especially when stolen credentials and the Trojan defence are two commonly cited arguments. Thus, it is apparent that there is an urgent requirement to develop a more innovative and robust technique that can more inextricably link the use of information (e.g., images and documents) to the users who access and use them. Therefore, this research project investigates the role that transparent and multimodal biometrics could play in providing this link by leveraging individuals’ biometric information for the attribution of insider misuse identification. This thesis examines the existing literature in the domain of data loss prevention, detection, and proactive digital forensics, which includes traceability techniques. The aim is to develop the current state of the art, having identified a gap in the literature, which this research has attempted to investigate and provide a possible solution. Although most of the existing methods and tools used by investigators to conduct examinations of digital crime help significantly in collecting, analysing and presenting digital evidence, essential to this process is that investigators establish a link between the notable/stolen digital object and the identity of the individual who used it; as opposed to merely using an electronic record or a log that indicates that the user interacted with the object in question (evidence). Therefore, the proposed approach in this study seeks to provide a novel technique that enables capturing individual’s biometric identifiers/signals (e.g. face or keystroke dynamics) and embedding them into the digital objects users are interacting with. This is achieved by developing two modes—a centralised or decentralised manner. The centralised approach stores the mapped information alongside digital object identifiers in a centralised storage repository; the decentralised approach seeks to overcome the need for centralised storage by embedding all the necessary information within the digital object itself. Moreover, no explicit biometric information is stored, as only the correlation that points to those locations within the imprinted object is preserved. Comprehensive experiments conducted to assess the proposed approach show that it is highly possible to establish this correlation even when the original version of the examined object has undergone significant modification. In many scenarios, such as changing or removing part of an image or document, including words and sentences, it was possible to extract and reconstruct the correlated biometric information from a modified object with a high success rate. A reconstruction of the feature vector from unmodified images was possible using the generated imprints with 100% accuracy. This was achieved easily by reversing the imprinting processes. Under a modification attack, in which the imprinted object is manipulated, at least one imprinted feature vector was successfully retrieved from an average of 97 out of 100 images, even when the modification percentage was as high as 80%. For the decentralised approach, the initial experimental results showed that it was possible to retrieve the embedded biometric signals successfully, even when the file (i.e., image) had had 75% of its original status modified. The research has proposed and validated a number of approaches to the embedding of biometric data within digital objects to enable successful user attribution of information leakage attacks.Embassy of Saudi Arabia in Londo

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Improved Performance of Secured VoIP Via Enhanced Blowfish Encryption Algorithm

Both the development and the integration of efficient network, open source technology, and Voice over Internet Protocol (VoIP) applications have been increasingly important and gained quick popularity due to new rapidly emerging IP-based network technology. Nonetheless, security and privacy concerns have emerged as issues that need to be addressed. The privacy process ensures that encryption and decryption methods protect the data from being alternate and intercept, a privacy VoIP call will contribute to private and confidential conversation purposes such as telebanking, telepsychiatry, health, safety issues and many more. Hence, this study had quantified VoIP performance and voice quality under security implementation with the technique of IPSec and the enhancement of the Blowfish encryption algorithm. In fact, the primary objective of this study is to improve the performance of Blowfish encryption algorithm. The proposed algorithm was tested with varying network topologies and a variety of audio codecs, which contributed to the impact upon VoIP network. A network testbed with seven experiments and network configurations had been set up in two labs to determine its effects on network performance. Besides, an experimental work using OPNET simulations under 54 experiments of network scenarios were compared with the network testbed for validation and verification purposes. Next, an enhanced Blowfish algorithm for VoIP services had been designed and executed throughout this research. From the stance of VoIP session and services performance, the redesign of the Blowfish algorithm displayed several significant effects that improved both the performance of VoIP network and the quality of voice. This finding indicates some available opportunities that could enhance encrypted algorithm, data privacy, and integrity; where the balance between Quality of Services (QoS) and security techniques can be applied to boost network throughput, performance, and voice quality of existing VoIP services. With that, this study had executed and contributed to a threefold aspect, which refers to the redesign of the Blowfish algorithm that could minimize computational resources. In addition, the VoIP network performance was analysed and compared in terms of end-to-end delay, jitter, packet loss, and finally, sought improvement for voice quality in VoIP services, as well as the effect of the designed enhanced Blowfish algorithm upon voice quality, which had been quantified by using a variety of voice codecs

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC