Formal Analysis of ISO/IEC 9798-2 Authentication Standard using AVISPA

International audienceUse of formal methods is considered as a useful and efficient technique for the validation of security properties of the protocols. In this paper, we analyze the protocols of ISO/IEC 9798-2 entity authentication standard using a state-of-the-art tool for automated analysis named AVISPA. Our analysis of the standard using AVISPA's OFMC and CL-AtSe back-ends shows that the two party protocols are secure against the specified security properties while the back-ends are able to find attacks against unilateral and mutual authentication protocols involving a trusted third party

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

Doping and Transfer of High Mobility Graphene Bilayers for Room Temperature Mid-Wave Infrared Photodetectors

High-performance graphene-HgCdTe detector technology has been developed combining the best properties of both materials for mid-wave infrared (MWIR) detection and imaging. The graphene functions as a high mobility channel that whisks away carriers before they can recombine, further contributing to detection performance. Comprehensive modeling on the HgCdTe, graphene, and the HgCdTe-graphene interface has aided the design and development of this MWIR detector technology. Chemical doping of the bilayer graphene lattice has enabled p-type doping levels in graphene for high mobility implementation in high-performance MWIR HgCdTe detectors. Characterization techniques, including SIMS and XPS, confirm high boron doping concentrations. A spin-on doping (SOD) procedure is outlined that has provided a means of doping layers of graphene on native substrates, while subsequently allowing integration of the doped graphene layers with HgCdTe for final implementation in the MWIR photodetection devices. Successful integration of graphene into HgCdTe photodetectors can thus provide higher MWIR detector efficiency and performance compared to HgCdTe-only detectors. New earth observation measurement capabilities are further enabled by the room temperature operational capability of the graphene-enhanced HgCdTe detectors and arrays to benefit and advance space and terrestrial applications

An Improved Hwang-Lee-Tang Remote User Authentication Scheme

Abstract: In this paper, we present a secure and efficient remote authentication scheme by improving Hwang-Lee-Tang’s scheme. The security of our scheme is based on the onewayness and collision-resistance properties of the hash functions being used. The proposed scheme is able to withstand all commonly known attacks against remote au-thentication schemes. In addition, the scheme does not store a password table on the server, provides mutual authentication between the user and the server, does not re-veal user’s password to the server, allows the user to freely choose a password of her choice, and allows the user to change her password by running a simple protocol with the server.

Iris recognition performance enhancement using weighted majority voting

Biometric authentication is a convenient and increasingly reli-able way to prove one’s identity. Iris scanning in particular is among the most accurate biometric authentication technologies currently available. However, despite their extremely high accuracy under ideal imaging conditions, existing iris recognition methods degrade when the iris images are noisy or the enrollment and verification imaging conditions are substantially different. To address this issue and enable iris recognition on less-than-ideal images, we introduce a weighted majority voting technique applicable to any biometric authentication system using bitwise comparison of enrollment-time and verification-time biometric templates. In a series of experiments with the CASIA iris database, we find that the method outperforms existing majority voting and reliable bit selection techniques. Our method is a simple and efficient means to improve upon the accu-racy of existing iris recognition systems. Index Terms — Biometrics, iris recognition, weighted majority voting 1

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

Vehicle Remote Health Monitoring and Prognostic Maintenance System

In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to diagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions. However with the great development in automotive industry, it looks feasible today to analyze sensor’s data along with machine learning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of vehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in faulty condition (when any failure in specific system has occurred) and in normal condition. The data is transmitted to the server which analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, K Nearest Neighbor, and Random Forest. These patterns are later used to detect future failures in other vehicles which show the similar behavior. The approach is produced with the end goal of expanding vehicle up-time and was demonstrated on 70 vehicles of Toyota Corolla type. Accuracy comparison of all classifiers is performed on the basis of Receiver Operating Characteristics (ROC) curves

Driver Fatigue Detection Using Viola Jones and Principal Component Analysis

In this paper, we have proposed a low-cost solution for driver fatigue detection based on micro-sleep patterns. Contrary to conventional methods, we acquired images by placing a camera on the extreme left side of the driver and proposed two algorithms that facilitate accurate face and eye detections, even when the driver is not facing the camera or driver’s eyes are closed. The classification to find whether eye is closed or open is done on the right eye only using SVM and Adaboost. Based on eye states, micro-sleep patterns are determined and an alarm is triggered to warn the driver, when needed. In our dataset, we considered multiple subjects from both genders, having different appearances and under different lightning conditions. The proposed scheme gives 99.9% and 98.7% accurate results for face and eye detection, respectively. For all the subjects, the average accuracy of SVM and Adaboost is 96.5% and 95.4%, respectively