Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading

A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Protection of an intrusion detection engine with watermarking in ad hoc networks

Mobile ad hoc networks have received great attention in recent years, mainly due to the evolution of wireless networking and mobile computing hardware. Nevertheless, many inherent vulnerabilities exist in mobile ad hoc networks and their applications that affect the security of wireless transactions. As intrusion prevention mechanisms, such as encryption and authentication, are not sufficient we need a second line of defense, Intrusion Detection. In this pa-per we present an intrusion detection engine based on neural networks and a protection method based on watermarking techniques. In particular, we exploit information visualization and machine learning techniques in order to achieve intrusion detection and we authenticate the maps produced by the application of the intelligent techniques using a novel combined watermarking embedding method. The performance of the proposed model is evaluated under different traffic conditions, mobility patterns and visualization metrics

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