X-Secure: protecting users from big bad wolves

In 2014 over 70% of people in Great Britain accessed the Internet every day. This resource is an optimal vector for malicious attackers to penetrate home computers and as such compromised pages have been increasing in both number and complexity. This paper presents X-Secure, a novel browser plug-in designed to present and raise the awareness of inexperienced users by analysing web-pages before malicious scripts are executed by the host computer. X-Secure was able to detect over 90% of the tested attacks and provides a danger level based on cumulative analysis of the source code, the URL, and the remote server, by using a set of heuristics, hence increasing the situational awareness of users browsing the internet.

Pervasive eHealth services a security and privacy risk awareness survey

The human factor is often recognised as a major aspect of cyber-security research. Risk and situational perception are identified as key factors in the decision making process, often playing a lead role in the adoption of security mechanisms. However, risk awareness and perception have been poorly investigated in the field of eHealth wearables. Whilst end-users often have limited understanding of privacy and security of wearables, assessing the perceived risks and consequences will help shape the usability of future security mechanisms. This paper present a survey of the the risks and situational awareness in eHealth services. An analysis of the lack of security and privacy measures in connected health devices is described with recommendations to circumvent critical situations.

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

The Internet of things (IoT) 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 internet packet traces, 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.