Embedded systems serving as IoT nodes are often vulnerable to malicious and unknown runtime software that could compromise the system, steal sensitive data, and cause undesirable system behaviour. Commercially available embedded systems used in automation, medical equipment, and automotive industries, are especially exposed to this vulnerability since they lack the resources to incorporate conventional safety features and are challenging to mitigate through conventional approaches. We propose a novel system design coined as APPARENT which can identify program characteristics by monitoring and counting the maximum possible low-level hardware events from Hardware Performance Counters (HPCs) that occur during the program's execution and analyse the correlation among the counts of various monitored events. To further utilise these captured events as features we propose a self-supervised machine learning algorithm that combines a Graph Attention Network GAT and a Generative Topographic Mapping GTM to detect unusual program behaviour as anomalies to enhance the system security. Our proposed methodology takes advantage of attributes like program counter, cycles per instruction, and physical and virtual timers at various exception levels of the embedded processor to identify abnormal activity. APPARENT identifies unknown program behaviours not present in the training phase with an accuracy of over 98.46% on Autobench EEMBC benchmarks.

Nowadays, cyber-attacks have become a common and persistent issue affecting various human activities in modern societies. Due to the continuously evolving landscape of cyber-attacks and the growing concerns around “black box” models, there has been a strong demand for novel explainable and interpretable intrusion detection systems with online learning abilities. In this paper, a novel soft prototype-based autonomous fuzzy inference system (SPAFIS) is proposed for network intrusion detection . SPAFIS learns from network traffic data streams online on a chunk-by-chunk basis and autonomously identifies a set of meaningful, human-interpretable soft prototypes to build an IF-THEN fuzzy rule base for classification. Thanks to the utilization of soft prototypes, SPAFIS can precisely capture the underlying data structure and local patterns, and perform internal reasoning and decision-making in a human-interpretable manner based on the ensemble properties and mutual distances of data. To maintain a healthy and compact knowledge base , a pruning scheme is further introduced to SPAFIS, allowing itself to periodically examine the learned solution and remove redundant soft prototypes from its knowledge base. Numerical examples on public network intrusion detection datasets demonstrated the efficacy of the proposed SPAFIS in both offline and online application scenarios, outperforming the state-of-the-art alternatives.

Negative Road Anomalies (Potholes, cracks, and other road anomalies) have long posed a risk for drivers driving on the road. In this paper, we apply deep learning techniques to implement a YOLO-based (You Only Look Once) network in order to detect and identify potholes in real-time providing a fast and accurate detection and sufficient time for proper safe navigation and avoidance of potholes. This system can be used in conjunction with any existing system and can be mounted to moving platforms such as autonomous vehicles. Our results show that the system is able to reach real-time processing (29.34 frames per second) with a high level of accuracy (mAP of 82.05%) and detection accuracy of 89.75% when mounted onto an Electric-Powered Wheelchair (EPW).

People with movement disabilities may find powered wheelchair driving a challenging task due to their comorbidities. Certain visually impaired persons with mobility disabilities are not prescribed a powered wheelchair because of their sight condition. However, powered wheelchairs are essential to the majority of these disabled users for commuting and social interaction. It is vital for their independence and wellbeing. In this paper, we propose to use a semantic segmentation (SS) system based on deep learning algorithms to provide environmental cues and information to visually impaired wheelchair users to aid with the navigation process. The system classifies the objects of the indoor environment and presents the annotated output on a display customised to the user's condition. The user can select a target object, for which the system can display the estimated distance from the current position of the wheelchair. The system runs in real-time, using a depth camera installed on the wheelchair, and it displays the scene in front of the wheelchair with every pixel annotated with distinguishable colour to represent the different components of the environment along with the distance to the target object. Our system has been designed, implemented and deployed on a real powered wheelchair for practical evaluation. The proposed system helped the users to estimate more accurately the distance to the target objects with a relative error of 19.8% and 18.4% for the conditions of a) semi-neglect and b) short-sightedness, respectively, compared to errors of 47.8% and 5.6% without the SS system. In our experiments, healthy participants were put in simulated conditions representing the above visual impairments using instruments commonly used in medical research for this purpose. Finally, our system helps to visualise, on the display, hidden areas of the environment and blind spots that visually impaired users would not be able to see without it.

Semantic segmentation ‘SS’ evaluation metrics are great tools to assess systems’ performance in terms of pixels’ accuracy and the alignment of segments. SS systems represent the core element of many human-system interaction applications. Thus, improving the comprehensiveness of SS evaluation metrics can accurately reflect the performance of human-system inter-action. Standard metrics ignore pixels’ confidence scores which can carry useful information. Pixels’ scores represent the level of confidence of the system for assigning class labels to image pixels. However, it has not been utilised by any evaluating metric for semantic segmentation systems. We propose to incorporate pixels’ confidence scores with existing metrics to gain better insights into systems’ behaviours. Results show the usefulness of the introduced approach to utilise the pixels’ scores in the evaluation process. Besides, using pixels’ scores thresholding can help to enhance the system performance on a specific task or objects of a particular size.

Synthetic, or artificial data is used in security applications such as protection of sensitive information, prediction of rare events, and training neural networks. Risk and trust are assessed specifically for a given kind of synthetic data and particular application. In this paper, we consider a more complicated scenario, - biometric-enabled cognitive cognitive biometric-enabled identity management, in which multiple kinds of synthetic data are used in addition to authentic data. For example, authentic biometric traits can be used to train the intelligent tools to identify humans, while synthetic, algorithmically generated data can be used to expand the training set or to model extreme situations. This paper is dedicated to understanding the potential impact of synthetic data on the cognitive checkpoint performance, and risk and trust prediction.

As the adoption of digital technologies expands, it becomes vital to build trust and confidence in the integrity of such technology. The SPIRIT project investigates the proof of concept of employing novel secure and privacy-ensuring techniques in services set-up in the Internet of Things (IoT) environment, aiming to increase the trust of users in IoTbased systems. The proposed system integrates three highly novel technology concepts developed by the consortium partners. Specifically, a technology, ermed ICMetrics, for deriving encryption keys directly from the operating characteristics of digital devices; secondly, a technology based on a contentbased signature of user data in order to ensure the integrity of sentdata upon arrival; a third technology, termed semantic firewall, which is able to allow or deny the transmission of data derived from an IoT device according to the information contained within the data and the information gathered about the requester.

The calculation of binary iris codes from feature values (e.g., the result of Gabor transform) is a key step in iris recognition systems. Traditional binarization method based on the sign of feature values has achieved very promising performance. However, currently, little research focuses on a deeper insight into this binarization method to produce iris codes. In this paper, we illustrate the iris code calculation from the perspective of optimization. We demonstrate that the traditional iris code is the solution of an optimization problem, which minimizes the distance between the feature values and the iris codes. Furthermore, we show that more effective iris codes can be obtained by adding terms to the objective function of this optimization problem. We investigate two additional objective terms. The first objective term exploits the spatial relationships of the bits in different positions of an iris code. The second objective term mitigates the influence of less reliable bits in iris codes. The two objective terms can be applied to the optimization problem individually, or in a combined scheme. We conduct experiments on four benchmark datasets with varying image quality. The experimental results demonstrate that the iris code produced by solving the optimization problem with the two additional objective terms achieves a generally improved performance in comparison to the traditional iris code calculated by binarizing feature values based on their signs.

Iris recognition systems working in less constrained environments with the subject at-a-distance and on-the-move suffer from the noise and degradations in the iris captures. These noise and degradations significantly deteriorate iris recognition performance. In this paper, we propose a novel signal-level information fusion method to mitigate the influence of noise and degradations for less constrained iris recognition systems. The proposed method is based on low rank approximation (LRA). Given multiple noisy captures of the same eye, we assume that: 1) the potential noiseless images lie in a low rank subspace and 2) the noise is spatially sparse. Based on these assumptions, we seek an LRA of noisy captures to separate the noiseless images and noise for information fusion. Specifically, we propose a sparse-error low rank matrix factorization model to perform LRA, decomposing the noisy captures into a low rank component and a sparse error component. The low rank component estimates the potential noiseless images, while the error component models the noise. Then, the low rank and error components are utilized to perform signal-level fusion separately, producing two individually fused images. Finally, we combine the two fused images at the code level to produce one iris code as the final fusion result. Experiments on benchmark data sets demonstrate that the proposed signal-level fusion method is able to achieve a generally improved iris recognition performance in less constrained environment, in comparison with the existing iris recognition algorithms, especially for the iris captures with heavy noise and low quality.

A potential threat to embedded systems is the execution of unknown or malicious software capable of triggering harmful system behavior, aimed at theft of sensitive data or causing damage to the system. Commercial off-the-shelf embedded devices, such as embedded medical equipment, are more vulnerable as these type of products cannot be amended conventionally or have limited resources to implement protection mechanisms. In this paper, we present a self-organizing map (SOM)-based approach to enhance embedded system security by detecting abnormal program behavior. The proposed method extracts features derived from processor's program counter and cycles per instruction, and then utilises the features to identify abnormal behavior using the SOM. Results achieved in our experiment show that the proposed method can identify unknown program behaviors not included in the training set with over 98.4% accuracy.

In this paper, we propose a novel algorithm for colour iris segmentation. The algorithm may be divided into the following components: coarse iris localization, limbic boundary segmentation, pupillary boundary segmentation, eyelids fitting, reflection and shadow removal. The key contribution of the proposed algorithm is that we demonstrate the power of sparsity induced by ℓ1-norm in overcoming the noise and degradations in colour iris images. We show that limbic and pupillary boundary, as well as eyelids, can be fitted robustly by solving ℓ1-norm regression problems. The experimental analysis shows the robustness of the proposed algorithm; comparison with state-of-the-art methods achieves an improved performance.

Purpose Biometric authentication, which requires storage of biometric templates and/or encryption keys, raises a matter of serious concern, since the compromise of templates or keys necessarily compromises the information secured by those keys. To address such concerns, efforts based on dynamic key generation directly from the biometrics have recently emerged. However, previous methods often have quite unacceptable authentication performance and/or small key spaces and therefore are not viable in practice. The purpose of this paper is to propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys. Design/methodology/approach This proposition is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise. Findings The evaluation demonstrates that the proposed method for dynamic key generation can offer both good reliability and security in practice, and outperforms other related methods. Originality/value In this paper, the authors propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys. This is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise.

In this paper, we develop a fingerprint matching method which operates by first introducing an integrated measure, which combines two different matching criteria based on heterogeneous features. We then devise a genetically guided algorithm to optimise the integrated measure for simultaneous fingerprint alignment and verification. The proposed method is evaluated through experiments conducted on two public domain collections of fingerprint images and compared with related work, with very encouraging results.

With the continuing rise in the complexity of embedded systems, there is an emerging need for a higher level modelling environment that facilitates efficient handling of this complexity. The aim here is to produce such a high level environment using Model Driven Development (MDD) techniques that maps a high level abstract description of an electronic embedded system into its low level implementation details. The Unified Modelling Language (UML) is a high level graphical based language that is broad enough in scope to model embedded systems hardware circuits. The authors have developed a framework for deriving Very High Speed Integrated Circuits Hardware Description Language (VHDL) code from UML state diagrams and defined a set of rules that enable automated generation of synthesisable VHDL code from UML specifications using MDD techniques. By adopting the techniques and tools described in this paper the design and implementation of complex state-based systems is greatly simplified.

Biometric authentication is increasingly gaining popularity in a wide range of applications. However, the storage of the biometric templates and/or encryption keys that are necessary for such applications is a matter of serious concern, as the compromise of templates or keys necessarily compromises the information secured by those keys. In this paper, we propose a novel method, which requires storage of neither biometric templates nor encryption keys, by directly generating the keys from statistical features of biometric data. An outline of the process is as follows: given biometric samples, a set of statistical features is first extracted from each sample. On each feature subset or single feature, we model the intra and interuser variation by clustering the data into natural clusters using a fuzzy genetic clustering algorithm. Based on the modelling results, we subsequently quantify the consistency of each feature subset or single feature for each user. By selecting the most consistent feature subsets and/or single features for each user individually, we generate the key reliably without compromising its relative security. The proposed method is evaluated on handwritten signature data and compared with related methods, and the results are very promising.

The problem of the incorporation of pattern features with unusual distributions is well known within pattern recognition systems even if not easily addressed. The problem is more acute when features are derived from characteristics of given integrated electronic circuits. The current paper introduces novel efficient techniques for normalising sets of features which are highly multi-modal in nature, so as to allow them to be incorporated within a single encryption key generation system based primarily on measured hardware characteristics. The utility of the proposed system lies in the observation that the need for data sent to and from remote network nodes to be secure and verified is substantial. Security can be improved by using encryption techniques based on keys, which are based on unique properties of the individual nodes within the network. This will serve both to minimize the need for key storage and sharing as well as to validate the initiator node of a message.

Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching between two minutiae sets. Within the MFMA, we first introduce an efficient matching operation to produce an initial population of local alignment configurations by examining local features of minutiae. Then, we devise a hybrid evolutionary procedure by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal or near optimal global alignment. Finally, we define a reliable matching function for fitness computation. The proposed algorithm was evaluated by means of a series of experiments conducted on the FVC2002 database and compared with previous work. Experimental results confirm that the MFMA is an effective and practical matching algorithm for fingerprint verification. The algorithm is faster and more accurate than a traditional genetic-algorithm-based method. It is also more accurate than a number of other methods implemented for comparison, though our method generally requires more computational time in performing fingerprint matching.

A novel strategy for the representation and manipulationof distributed documents, potentially complex andheterogeneous, is presented in this paper. The documentunder the proposed model is represented in a hierarchicalstructure. Associated metadata' describes the flexiblehierarchy with the scope of dynamically restructuring thetree at runtime. All useful functionals can also be includedwithin the hierarchy to minimize reliance on externalprograms in manipulating sensitive data. Thisgives the proposed model two key properties: generality(capable of representing any document format includingfuture innovations) and autonomy (non-reliance on externalprograms). The model also allows incorporation ofadditional features for security and access control. Biometricperson authentication measures are introduced. Abrief example illustrates the key ideas.

This paper investigates the performance of multi-neural systems, focusing on the benefits that can be gained when integrating different types of neural experts (hybrid multi-neural system). An empirical evaluation shows that the integration of different types of neural networks leads to an improvement in performance in a practical classification task for a range of combination methods.

The set of fuzzy connectives can be seen as an important combination tool, such as in combining the antecedent sets of the rules, in multi-criteria decision making and in combining the outputs of neural classifiers in a multi-neural system. This papers investigates the performance of some fuzzy combination schemes applied to a multi hybrid neural system which is composed of neural and fuzzy neural networks. An empirical evaluation in a handwritten numeral recognition task is used to investigate the performance of the presented fuzzy methods with some existing combination methods.

A novel strategy for the representation and manipulation of multimedia documents is introduced. The model allows such documents to be treated as autonomous entities capable of representing and modifying their own internal structures. The model is sufficiently flexible as to allow any given arbitrary document format and any programming language to be incorporated within its implementation.

This paper presents a mechanism to vary the vigilance parameter in the RePART fuzzy neural network. This mechanism helps to smooth out the problem of category proliferation which affects ARTMAP-based networks. Empirical experiments show that the use of variable vigilance improves the performance of the RePART model while, at the same time, requiring a less complex structure.

A novel multiple-expert framework for recognition of handwritten characters is presented. The proposed framework is composed of multiple classifiers (experts) put together in such a manner as to enhance the recognition capability of the combined network compared to the best performing individual expert participating in the framework. Each of these experts has been derived from a novel neural structure in which the weight values are derived from Clifford algebra. A Clifford algebra is a mathematical paradigm capable of capturing the interdimensional dependencies found in multidimensional data. It offers a technique for concise data storage and processing by representing dependencies between the component dimensions of the data which is otherwise difficult to encode and hence is often employed in analyzing multidimensional data. Results achieved by the proposed multiple-expert framework demonstrates significant improvement over alternative techniques.

It is well known that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple neural classifiers. This paper analyses the performance of some combination schemes applied to a multi-hybrid neural system which is composed of neural and fuzzy neural networks. Essentially, the combination methods employ different ways to extract valuable information from the output of the experts through the use of confidence (weights) measures of the ensemble members to each class. An empirical evaluation in a handwritten numeral recognition task is used to investigate the performance of the presented methods in comparison with some existing combination methods.

This paper introduces a novel networking strategy for RAM-based neurons which significantly improves the training and recognition performance of such networks whilst maintaining the generalization capabilities achieved in previous network configurations. A number of different architectures are introduced, each using the same underlying principles. Initially, features which are common to all architectures are described, illustrating the basis of the underlying paradigm. Three architectures are then introduced illustrating different techniques of employing the paradigm to meet differing performance specifications. The architectures are described in terms of the structure of the neurons they employ. Details of the various training and recognition algorithms employed by the architectures are supplied in order to present a complete description of the operation of this class of artificial neural network.