Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

Compensating inaccurate annotations to train 3D facial landmark localisation models

In this paper we investigate the impact of inconsistency in manual annotations when they are used to train automatic models for 3D facial landmark localization. We start by showing that it is possible to objectively measure the consistency of annotations in a database, provided that it contains replicates (i.e. repeated scans from the same person). Applying such measure to the widely used FRGC database we find that manual annotations currently available are suboptimal and can strongly impair the accuracy of automatic models learnt therefrom. To address this issue, we present a simple algorithm to automatically correct a set of annotations and show that it can help to significantly improve the accuracy of the models in terms of landmark localization errors. This improvement is observed even when errors are measured with respect to the original (not corrected) annotations. However, we also show that if errors are computed against an alternative set of manual annotations with higher consistency, the accuracy of the models constructed using the corrections from the presented algorithm tends to converge to the one achieved by building the models on the alternative,more consistent set

Distributed Information Retrieval using Keyword Auctions

This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions

Implicit Sensor-based Authentication of Smartphone Users with Smartwatch

Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial log-in mechanism, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data, whether in the cloud or in the smartphone. But attackers who have gained access to a logged-in smartphone have no incentive to re-authenticate, so this must be done in an automatic, non-bypassable way. Hence, this paper proposes a novel authentication system, iAuth, for implicit, continuous authentication of the end-user based on his or her behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We design a system that gives accurate authentication using machine learning and sensor data from multiple mobile devices. Our system can achieve 92.1% authentication accuracy with negligible system overhead and less than 2% battery consumption.Comment: Published in Hardware and Architectural Support for Security and Privacy (HASP), 201

Estimating Spectroscopic Redshifts by Using k Nearest Neighbors Regression I. Description of Method and Analysis

Context: In astronomy, new approaches to process and analyze the exponentially increasing amount of data are inevitable. While classical approaches (e.g. template fitting) are fine for objects of well-known classes, alternative techniques have to be developed to determine those that do not fit. Therefore a classification scheme should be based on individual properties instead of fitting to a global model and therefore loose valuable information. An important issue when dealing with large data sets is the outlier detection which at the moment is often treated problem-orientated. Aims: In this paper we present a method to statistically estimate the redshift z based on a similarity approach. This allows us to determine redshifts in spectra in emission as well as in absorption without using any predefined model. Additionally we show how an estimate of the redshift based on single features is possible. As a consequence we are e.g. able to filter objects which show multiple redshift components. We propose to apply this general method to all similar problems in order to identify objects where traditional approaches fail. Methods: The redshift estimation is performed by comparing predefined regions in the spectra and applying a k nearest neighbor regression model for every predefined emission and absorption region, individually. Results: We estimated a redshift for more than 50% of the analyzed 16,000 spectra of our reference and test sample. The redshift estimate yields a precision for every individually tested feature that is comparable with the overall precision of the redshifts of SDSS. In 14 spectra we find a significant shift between emission and absorption or emission and emission lines. The results show already the immense power of this simple machine learning approach for investigating huge databases such as the SDSS.Comment: accepted for publication in A&