An intelligent multimodal biometric authentication model for personalised healthcare services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16

Adaptive categorization of complex system fault patterns

Due to large amount of information and the inherent intricacy, diagnosis in complex systems is a difficult task. This can be somehow simplified by taking a per-step towards categorizing the system conditions and faults. In this paper, the development and implementation of an approach that establishes class membership conditions, using a labelled training set, is described. More specifically, the use of negative recognition for classification and diagnosis of complex system faults are discussed. The adaptive recognition to achieve the classification is based on discovery of pattern features that make them distinct from objects belonging to different classes. Most of the existing approaches to fault diagnosis, particularly for large or complex systems, depend on heuristic rules. The approach proposed in this work does not resort to any heuristic rules, which makes it more suitable for diagnosis of faults in dynamic and complex systems. For evaluation purposes, using the data provided by the protection simulator of a large power system, its fault diagnosis is carried out. The results of those simulations are also reported. They clearly reveal that even for complex systems, the proposed approach, based on making use of the distinctive features of encountered fault patterns, is capable of fault classification with minimal supervision

Fuzzy detection of network intrusions and integrated network management

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Handling network management information complexity through soft computing

The increasing complexity of modern networks requires radical changes in network management approaches. One of the prerequisites for successful management of these complex systems is the ability to handle large amounts of information. The information may contain incoherent, missing, or unreliable data that need to be filtered and processed. In this respect, artificial intelligence techniques offer many appealing solutions that merit to be considered. In particular, the power of soft computing in handling uncertainties makes it an appropriate choice for taking up a significant role in management of networked systems. This paper focuses on this topic, highlighting in a conceptual manner the role of soft computing in identifying or improving solutions to network management problems. To demonstrate the effectiveness of soft computing in improved management of networks, some specific areas of functional importance are also discussed

Adaptive categorization in complex systems

A fast and reliable method for categorization of patterns that may be encountered in complex systems is described. Most pattern recognition and classification approaches are founded on discovering the connections and similarities between the members of each class. In this work, a different view of classification is presented. The classification is based on identification of distinctive features of patterns. It will be shown that the members of different classes have different values for some or all of such features. The paper will also show that by making use of the distinctive features and their corresponding values, classification of all patterns, even for complex systems, can be accomplished. The classification process does not rely on any heuristic rules. In this process, patterns are grouped together in such a way that their distinctive features can be explored. Such features are then used for identification purposes

Employing artificial immunology and approximate reasoning models for enhanced network intrusion detection

With the massive connectivity provided by modern computer networks, more and more systems are subject to attack by intruders. The creativity of attackers, the complexities of host computers, along with the increasing prevalence of distributed systems and insecure networks such as the Internet have contributed to the difficulty in effectively identifying and counteracting security breaches. As such, while it is critical to have the mechanisms that are capable of preventing security violations, complete prevention of security breaches does not appear to be practical. Intrusion detection can be regarded as an alternative, or as a compromise to this situation. Several techniques for detecting intrusions are already well developed. But given their shortcomings, other approaches are being proposed and studied by many researchers. This paper discusses the shortcomings of some of the more traditional approaches used in intrusion detection systems. It argues that some of the techniques that are based on the traditional views of computer security are not likely to fully succeed. An alternative view that may provide better security systems is based on adopting the design principles from the natural immune systems, which in essence solve similar types of problems in living organisms. Furthermore, in any of these methodologies, the need for exploiting the tolerance for imprecision and uncertainty to achieve robustness and low solution costs is evident. This work reports on the study of the implications and advantages of using artificial immunology concepts for handling intrusion detection through approximate reasoning and approximate matching

Soft computing in network intrusion detection

The explosive growth of computer networking has brought about limitless possibilities and opportunities along with increased risks of malicious intrusions. It is therefore, critical to have the security mechanisms that are able to prevent security violations. However, complete prevention of security breaches does not appear to be practical. Intrusion detection can be regarded as an alternative, or as a compromise to this situation. Several techniques for detecting intrusions have been studied by many researchers. This paper discusses why intrusion detection systems are needed and presents the main techniques that these systems utilize. In any of these techniques, the need for exploiting the tolerance for imprecision and uncertainty to achieve robustness and low solution costs is evident. It can be noted that, this is in fact, the guiding principle of soft computing and more particularly soft computing. This work reports the preliminary steps taken in the study of the implications and advantages of using fuzzy logic for handling intrusion detection through approximate reasoning and approximate matching

Opportunities in ICT education

While cost saving is at the forefront of the reasons for offshoring to low wage countries, the moves relevant to ICT are also motivated by difficulty in finding the right talents inside the country. One of the root causes for such a difficulty is related to the drastic fall in the number of students in field like computer engineering and ICT. To combat that, there have been serious changes in national education policies, and the way universities and other training institutions conduct their business to inspire young students to choose ICT for their studies. Although as a consequence of those, in some parts of the world, the number of students enrolling in these fields have stabilized or even increased, given the number of years it takes to educate a graduate, the number of graduates has been dropping at alarming rates. Furthermore, the ICT skills shortages for experienced professionals, in most industrialized countries can be expected to get worse, before they eventually get better. There is also a strong case for retraining many people who already have tertiary education, whether in the workforce or not, to overcome to ominous ICT skills dilemma. This paper reports on the examination of these problems. It also reports on the advantages of taking a more broad-spectrum view, requiring a combination of many existing solutions along with novel approaches and realistic analysis of the acceptance of the current global ICT services and education environments to overcome these problems

Fuzzy modelling to enhance cooperative management

Efficient management of any complex system, such as modern enterprise networks, depends on understanding the roles of its constituents and their interactions with one another. As such, awareness modelling and levels can play significant roles in improving the management efficiency. The awareness levels in human beings and managers have a fuzzy nature with linguistic variables extensively used in their definitions and communications. This paper explores the notion of fuzzy logic and soft computing to improve awareness modelling to achieve more effective cooperative management. This is further demonstrated through its application to an illustrative example