No abstract

Wireless sensor networks have been researched extensively over the past few years. They were first used by the military for surveillance purposes and have since expanded into industrial and civilian uses such as weather, pollution, traffic control, and healthcare. One aspect of wireless sensor networks on which research has been conducted is the security of wireless sensor networks. These networks are vulnerable to hackers who might go into the network with the intent of rendering it useless. An example of this would be an enemy commandeering a drone and getting it to attack friendly forces. In this paper, we review the security of wireless sensor networks. Areas that are covered include: architectures and routing protocols; security issues that include context and design as well as confidentiality, integrity, and authenticity; algorithms; and performance issues for wireless sensor network design. Performance of the Self-Originating Wireless Sensor Network (SOWSN), Practical Algorithm for Data Security (PADS), and mechanisms for in-network processing were investigated in further detail with SOWSN having the best performance as a result of it being based on realistic scenarios.

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.

Biometric security is a rapidly growing field concerning the use of unique identifiers in the human body for access control and access management. This survey paper will outline the history of biometric security, detail some of the common techniques used for identification, and discuss the reliability and fail rates of these systems. During the course of our research we found that biometric security systems are poised to become the standard method of identification and authorization.

Although security plays a major role in the design of software systems, security requirements and policies are usually added to an already existing system, not created in conjunction with the product. As a result, there are often numerous problems with the overall design. In this paper, we discuss the relationship between software engineering, security engineering, and policy engineering and present a security policy life-cycle; an engineering methodology to policy development in high assurance computer systems. The model provides system security managers with a procedural engineering process to develop security policies. We also present an executable Prolog-based model as a formal specification and knowledge representation method using a theorem prover to verify system correctness with respect to security policies in their life-cycle stages.