Privacy enhancing technologies (PETs) for connected vehicles in smart cities

This is an accepted manuscript of an article published by Wiley in Transactions on Emerging Telecommunications Technologies, available online: https://doi.org/10.1002/ett.4173 The accepted version of the publication may differ from the final published version.Many Experts believe that the Internet of Things (IoT) is a new revolution in technology that has brought many benefits for our organizations, businesses, and industries. However, information security and privacy protection are important challenges particularly for smart vehicles in smart cities that have attracted the attention of experts in this domain. Privacy Enhancing Technologies (PETs) endeavor to mitigate the risk of privacy invasions, but the literature lacks a thorough review of the approaches and techniques that support individuals' privacy in the connection between smart vehicles and smart cities. This gap has stimulated us to conduct this research with the main goal of reviewing recent privacy-enhancing technologies, approaches, taxonomy, challenges, and solutions on the application of PETs for smart vehicles in smart cities. The significant aspect of this study originates from the inclusion of data-oriented and process-oriented privacy protection. This research also identifies limitations of existing PETs, complementary technologies, and potential research directions.Published onlin

Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features

A recent research direction for the localization of anatomical landmarks with learning-based methods is to explore ways to enrich the trained models with context information. Lately, the addition of context features in regression-based approaches has been tried in the literature. In this work, a method is presented for the addition of context features in a regression setting where the locations of many vertebral landmarks are regressed all at once. As this method relies on the knowledge of the centers of the vertebral bodies (VBs), an automatic, endplate-based approach for the localization of the VB centers is also presented. The proposed methods are evaluated on a dataset of 28 lumbar-focused CT images. The VB localization method detects all of the lumbar VBs of the testing set with a mean localization error of 3.2 mm. The multi-landmark localization method is tested on the task of localizing the tips of all the inferior articular processes of the lumbar vertebrae, in addition to their VB centers. The proposed method detects these landmarks with a mean localization error of 3.0 mm

Location-enhanced authentication using the IoT because you cannot be in two places at once

User location can act as an additional factor of authentication in scenarios where physical presence is required, such as when making in-person purchases or unlocking a vehicle. This paper proposes a novel approach for estimating user location and modeling user movement using the Internet of Things (IoT). Our goal is to utilize its scale and diversity to estimate location more robustly, than solutions based on smartphones alone, and stop adversaries from using compromised user credentials (e.g., stolen keys, passwords, etc.), when sufficient evidence physically locates them elsewhere. To locate users, we leverage the increasing number of IoT devices carried and used by them and the smart environments that observe these devices. We also exploit the ability of many IoT devices to "sense" the user. To demonstrate our approach, we build a system, called Icelus. Our experiments with it show that it exhibits a smaller false-rejection rate than smartphone-based location-based authentication (LBA) and it rejects attackers with few errors (i.e., false acceptances). \ua9 2016 ACM

Location-enhanced authentication using the IoT because you cannot be in two places at once

User location can act as an additional factor of authentication in scenarios where physical presence is required, such as when making in-person purchases or unlocking a vehicle. This paper proposes a novel approach for estimating user location and modeling user movement using the Internet of Things (IoT). Our goal is to utilize its scale and diversity to estimate location more robustly, than solutions based on smartphones alone, and stop adversaries from using compromised user credentials (e.g., stolen keys, passwords, etc.), when sufficient evidence physically locates them elsewhere. To locate users, we leverage the increasing number of IoT devices carried and used by them and the smart environments that observe these devices. We also exploit the ability of many IoT devices to "sense" the user. To demonstrate our approach, we build a system, called Icelus. Our experiments with it show that it exhibits a smaller false-rejection rate than smartphone-based location-based authentication (LBA) and it rejects attackers with few errors (i.e., false acceptances). © 2016 ACM

A survey of keylogger and screenlogger attacks in the banking sector and countermeasures to them

Keyloggers and screenloggers are one of the active growing threats to user's confidentiality as they can run in user-space, easily be distributed and upload information to remote servers. They use a wide number of different techniques and may be implemented in many ways. Keyloggers and screenloggers are very largely diverted from their primary and legitimate function to be exploited for malicious purposes compro- mising the privacy of users, and bank customers notably. This paper presents a survey of keylogger and screenlogger attacks to increase the understanding and awareness of their threat by covering basic concepts related to bank information systems and explaining their functioning, as it presents and discusses an extensive set of plausible countermeasures

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

I Sensed It Was You: Authenticating Mobile Users with Sensor-enhanced Keystroke Dynamics

Mobile devices have become an important part of our everyday life, harvesting more and more confidential user information. Their portable nature and the great exposure to security attacks, however, call out for stronger authentication mechanisms than simple password-based identification. Biometric authentication techniques have shown potential in this context. Unfortunately, prior approaches are either excessively prone to forgery or have too low accuracy to foster widespread adoption. In this paper, we propose sensor-enhanced keystroke dynamics, a new biometric mechanism to authenticate users typing on mobile devices. The key idea is to characterize the typing behavior of the user via unique sensor features and rely on standard machine learning techniques to perform user authentication. To demonstrate the effectiveness of our approach, we implemented an Android prototype system termed Unagi. Our implementation supports several feature extraction and detection algorithms for evaluation and comparison purposes. Experimental results demonstrate that sensor-enhanced keystroke dynamics can improve the accuracy of recent gestured-based authentication mechanisms (i.e., EER>0.5%) by one order of magnitude, and the accuracy of traditional keystroke dynamics (i.e., EER>7%) by two orders of magnitude

I Sensed It Was You: Authenticating Mobile Users with Sensor-enhanced Keystroke Dynamics

Mobile devices have become an important part of our everyday life, harvesting more and more confidential user information. Their portable nature and the great exposure to security attacks, however, call out for stronger authentication mechanisms than simple password-based identification. Biometric authentication techniques have shown potential in this context. Unfortunately, prior approaches are either excessively prone to forgery or have too low accuracy to foster widespread adoption. In this paper, we propose sensor-enhanced keystroke dynamics, a new biometric mechanism to authenticate users typing on mobile devices. The key idea is to characterize the typing behavior of the user via unique sensor features and rely on standard machine learning techniques to perform user authentication. To demonstrate the effectiveness of our approach, we implemented an Android prototype system termed Unagi. Our implementation supports several feature extraction and detection algorithms for evaluation and comparison purposes. Experimental results demonstrate that sensor-enhanced keystroke dynamics can improve the accuracy of recent gestured-based authentication mechanisms (i.e., EER>0.5%) by one order of magnitude, and the accuracy of traditional keystroke dynamics (i.e., EER>7%) by two orders of magnitude