Local modification of GaAs nanowires induced by laser heating

GaAs nanowires were heated locally under ambient air conditions by a focused laser beam which led to oxidation and formation of crystalline arsenic on the nanowire surface. Atomic force microscopy, photoluminescence and Raman spectroscopy experiments were performed on the same single GaAs nanowires in order to correlate their structural and optical properties. We show that the local changes of the nanowires act as a barrier for thermal transport which is of interest for thermoelectric applications

Continuous and transparent multimodal authentication: reviewing the state of the art

Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner

Anomaly Detection for Mobile Device Comfort

Part 2: Full PapersInternational audienceAs part of the Device Comfort paradigm, we envision a mobile device which, armed with the information made available by its sensors, is able to recognize whether it is being used by its owner or whether its owner is using the mobile device in an “unusual” manner. To this end, we conjecture that the use of a mobile device follows diurnal patterns and introduce a method for the detection of such anomalies in the use of a mobile device. We evaluate the accuracy of our method with two publicly available data sets and show its feasibility on two mobile devices

Misuse detection in a simulated IaaS environment

Cloud computing is an emerging technology paradigm by offering elastic computing resources for individuals and organisations with low cost. However, security is still the most sensitive issue in cloud computing services as the service remains accessible to anyone after initial simple authentication login for significant periods. This has led to increase vulnerability to potential attacks and sensitive customer information being misused. To be able to detect this misuse, an additional intelligent security measures are arguably required. Tracking user’s activity by building user behaviour profiles is one technique that has been successfully applied in a variety of applications such as telecommunication misuse and credit card fraud. This paper presents an investigation into applying behavioural profiling in a simulated IaaS-based infrastructure for the purposes of misuse detection by verifying the active user continuously and transparently. In order to examine the feasibility of this approach within cloud infrastructure services, a private dataset was collected containing real interactions of 60 users over a three-week period (totalling 1,048,195 log entries). A series of experiments were conducted using supervised machine learning algorithms to examine the ability of detecting abnormal usage. The best experimental result of 0.32% Equal Error Rate is encouraging and indicates the ability of identifying misuse within cloud computing services via the behavioural profiling technique