Performance of WebRTC in the context of a decentralised storage solution

Distributed Hash Table-based storage solutions provide a secure means for the storage and retrieval of data. While DHTs present interesting features, specifically around their inherently decentralised nature, in contrast to most Internet services, they also experience a significant reduction in performance when the latency increases between two peers. The challenge of latency is a particular concern for mobile users, and those using cellular connections to the Internet, which typically encounter higher network latencies than users on fixed-line wired connections. This paper proposes that the challenge can be partially mitigated by using the DHT only once, for peer discovery, to coordinate the initiation of data transfer directly between two peers using WebRTC. This raises potential for the deployment of such techniques in the near future, on account of the widespread availability of WebRTC technology in modern Internet browsers, both on desktop and mobile platforms

Performance challenges of decentralised services

Decentralised, peer-to-peer based services present a variety of security and privacy benefits for their users, and highly scalable to cater for a growing numbers of users, without extra servers being required of the service operator. This presents a significant advantage for newly emerging mobile applications (with high numbers of users, and limited funds for infrastructure), although performance is a challenge when accessing decentralised services. In this paper, we firstly show the performance of our implementation of a decentralised chunk-based storage platform is constrained by the network. We show the impact of network latency on the performance of this decentralised storage solution, and propose our solution to this, in the form of a federated, intermediary server, thus creating a hybrid decentralised service. This approach offers relatively constant performance as latency increases, due to the use of TCP connectivity, while ensuring the advantages of the decentralised service are not lost in the process

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) network is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using an IoT Data set, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks