Biblio

{Mobile devices are promising to apply two-factor authentication in order to improve system security and enhance user privacy-preserving. Existing solutions usually have certain limits of requiring some form of user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearables to extract individual characteristics of PPG signals. In order to realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts, which allows verifying users without intentionally staying still during the process of authentication. In addition, to deal with non-cancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials, which enables to defense against man-in-the-middle attacks and replay attacks. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors in wearables. We build a prototype of PPGPass and conduct the system with comprehensive experiments involving multiple participants. PPGPass can achieve an average F1 score of 95.3%, which confirms its high effectiveness, security, and usability}.

The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.

Child security in the school campus is most important in building a good society. In and around the world the children are abused and killed also in sometimes by the people those who are not in good attitude in the school campus. To track and resolve such issues an enhanced security feature system is required. Hence in this paper an enhanced version of security system for children is proposed by using `Wearable Sensors'. In this proposed method two wearable sensors nodes such as `Staff Node' and `Student Node' are paired by using `Bluetooth' communication technology and Smart Watch technology is also used to communicate the Security Center or Processing Node for tracking them about their location and whether the two nodes are moved away from the classroom. If the child node is not moving for a long period then it may be notified by the center and they will inform the security officers near to the place. This proposed method may satisfy the need of school management about the staff movements with students and the behavior of students to avoid unexpected issues.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

In this paper, the problem of network connectivity is studied for an adversarial Internet of Battlefield Things (IoBT) system in which an attacker aims at disrupting the connectivity of the network by choosing to compromise one of the IoBT nodes at each time epoch. To counter such attacks, an IoBT defender attempts to reestablish the IoBT connectivity by either deploying new IoBT nodes or by changing the roles of existing nodes. This problem is formulated as a dynamic multistage Stackelberg connectivity game that extends classical connectivity games and that explicitly takes into account the characteristics and requirements of the IoBT network. In particular, the defender's payoff captures the IoBT latency as well as the sum of weights of disconnected nodes at each stage of the game. Due to the dependence of the attacker's and defender's actions at each stage of the game on the network state, the feedback Stackelberg solution [feedback Stackelberg equilibrium (FSE)] is used to solve the IoBT connectivity game. Then, sufficient conditions under which the IoBT system will remain connected, when the FSE solution is used, are determined analytically. Numerical results show that the expected number of disconnected sensors, when the FSE solution is used, decreases up to 46% compared to a baseline scenario in which a Stackelberg game with no feedback is used, and up to 43% compared to a baseline equal probability policy.

Wireless wearable embedded devices dominate the Internet of Things (IoT) due to their ability to provide useful information about the body and its local environment. The constrained resources of low power processors, however, pose a significant challenge to run-time error logging and hence, product reliability. Error logs classify error type and often system state following the occurrence of an error. Traditional error logging algorithms attempt to balance storage and accuracy by selectively overwriting past log entries. Since a specific combination of firmware faults may result in system instability, preserving all error occurrences becomes increasingly beneficial as IOT systems become more complex. In this paper, a novel hash-based error logging algorithm is presented which has both constant insertion time and constant memory while also exhibiting no false negatives and an acceptable false positive error rate. Both theoretical analysis and simulations are used to compare the performance of the hash-based and traditional approaches.

The design of low power chip for IoT applications is very challenge, especially for self-powered wireless sensors. Achieving ultra low power requires both system level optimization and circuit level innovation. This paper presents a continuous-in-time and discrete-in-amplitude (CTDA) system architecture that facilitates adaptive data rate sampling and clockless implementation for a wireless sensor SoC.