No abstract

Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

The wireless communication systems has gone from different generations from SISO systems to MIMO systems. Bandwidth is one important constraint in wireless communication. In wireless communication, high data transmission rates are essential for the services like tripple play i.e. data, voice and video. At user end the capacity determines the quality of the communication systems. This paper aims to compare the different RF wireless communication systems like SISO, MISO, SIMO and MIMO systems on the capacity basis and explaining the concept as today, the wireless communication has evolved from 2G, 3G to 4G and the companies are fighting to create networks with more and more capacity so that data rates can be increased and customers can be benefitted more. The ultimate goal of wireless communication systems is to create a global personal and multimedia communication without any capacity issues.

Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.

The internet of things is one of the most innovative technology that promises to improve and optimize daily life based on sensors and smart objects. Many security issues and challenges in IoT are probed by researchers to achieve secured communication using variety of IoT devices. Basic principles of security i.e. confidentiality, integrity and availability are considered as the significant challenges for IoT. This paper gives an overview of IoT components and layers used for secured communication in IoT Ecosystem. The work also focuses on the vulnerabilities inherited in IoT ecosystem as well as the attacks which are specially targeting IoT network and three basic principles of security. Based on our study of different threats in IoT Ecosystem a threat model is recommended and classifications of threats have been done based on different real life scenarios. Further added, paper also recommends solutions and research directions with respect to security in IoT Ecosystem.