Wireless Emitter Location Estimation Based on Linear and Nonlinear Algorithms using TDOA Technique

Low-power devices such as cell phones, and wireless routers are commonly used to control Improvised Explosive Devices (IEDs) and as the communication nodes for the sake of command and control. Quickly locating the source of these signals is ambitious, specifically in a metropolitan environment where buildings and towers may cause intervention. This presents a geolocation system that compounds the attributes of several proven geolocation and error mitigation methods to locate an emitter of interest in an urban environment. The proposed geolocation system uses a Time Difference of Arrival (TDOA) approach to estimate the position of the emitter of interest. Using multiple sensors at known locations, TDOA estimates are achieved by the cross-correlation of the signal received at all the sensors. A Weighted Least Squares (WLS) solution, Linear least Square (LLS) method and maximum likelihood (ML) estimation is used to estimate the emitter's location. If the variance of this location estimate is too high, a sensor is detected and identified as possessing a Non-Line of Sight (NLOS) path from the emitter. This poorly located sensor is then removed from the geolocation system and a new position estimate is computed with the remaining sensor TDOA information. The performance of the TDOA system is determined through modeling and simulations. Test results confirm the feasibility of identifying a NLOS sensor, thereby improving the geolocation system's accurateness in a metropolitan environment

Detection and Localization of Multiple Spoofing using GADE and IDOL in WSN

Abstract Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. Spatial information, a physical property associated with each node, that is hard to falsify, and not reliant on cryptography is used, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. Thus received signal strength (RSS) is inherited from wireless nodes to detect the spoofing attacks. Cluster-based mechanisms are developed to determine the number of attackers. In addition, an integrated detection and localization system is developed that can localize the positions of multiple attackers. Thus this detection and localization results provide strong evidence in detecting multiple adversaries

Proof of witness presence: Blockchain consensus for augmented democracy in smart cities

Smart Cities evolve into complex and pervasive urban environments with a citizens’ mandate to meet sustainable development goals. Repositioning democratic values of citizens’ choices in these complex ecosystems has turned out to be imperative in an era of social media filter bubbles, fake news and opportunities for manipulating electoral results with such means. This paper introduces a new paradigm of augmented democracy that promises actively engaging citizens in a more informed decision-making augmented into public urban space. The proposed concept is inspired by a digital revive of the Ancient Agora of Athens, an arena of public discourse, a Polis where citizens assemble to actively deliberate and collectively decide about public matters. The core contribution of the proposed paradigm is the concept of proving witness presence: making decision-making subject of providing secure evidence and testifying for choices made in the physical space. This paper shows how the challenge of proving witness presence can be tackled with blockchain consensus to empower citizens’ trust and overcome security vulnerabilities of GPS localization. Moreover, a novel platform for collective decision-making and crowd-sensing in urban space is introduced: Smart Agora. It is shown how real-time collective measurements over citizens’ choices can be made in a fully decentralized and privacy-preserving way. Witness presence is tested by deploying a decentralized system for crowd-sensing the sustainable use of transport means. Furthermore, witness presence of cycling risk is validated using official accident data from public authorities, which are compared against wisdom of the crowd. The paramount role of dynamic consensus, self-governance and ethically aligned artificial intelligence in the augmented democracy paradigm is outlined

Proceedings of the Third Edition of the Annual Conference on Wireless On-demand Network Systems and Services (WONS 2006)

Ce fichier regroupe en un seul documents l'ensemble des articles accéptés pour la conférences WONS2006/http://citi.insa-lyon.fr/wons2006/index.htmlThis year, 56 papers were submitted. From the Open Call submissions we accepted 16 papers as full papers (up to 12 pages) and 8 papers as short papers (up to 6 pages). All the accepted papers will be presented orally in the Workshop sessions. More precisely, the selected papers have been organized in 7 session: Channel access and scheduling, Energy-aware Protocols, QoS in Mobile Ad-Hoc networks, Multihop Performance Issues, Wireless Internet, Applications and finally Security Issues. The papers (and authors) come from all parts of the world, confirming the international stature of this Workshop. The majority of the contributions are from Europe (France, Germany, Greece, Italy, Netherlands, Norway, Switzerland, UK). However, a significant number is from Australia, Brazil, Canada, Iran, Korea and USA. The proceedings also include two invited papers. We take this opportunity to thank all the authors who submitted their papers to WONS 2006. You helped make this event again a success

Developing reliable anomaly detection system for critical hosts: a proactive defense paradigm

Current host-based anomaly detection systems have limited accuracy and incur high processing costs. This is due to the need for processing massive audit data of the critical host(s) while detecting complex zero-day attacks which can leave minor, stealthy and dispersed artefacts. In this research study, this observation is validated using existing datasets and state-of-the-art algorithms related to the construction of the features of a host's audit data, such as the popular semantic-based extraction and decision engines, including Support Vector Machines, Extreme Learning Machines and Hidden Markov Models. There is a challenging trade-off between achieving accuracy with a minimum processing cost and processing massive amounts of audit data that can include complex attacks. Also, there is a lack of a realistic experimental dataset that reflects the normal and abnormal activities of current real-world computers. This thesis investigates the development of new methodologies for host-based anomaly detection systems with the specific aims of improving accuracy at a minimum processing cost while considering challenges such as complex attacks which, in some cases, can only be visible via a quantified computing resource, for example, the execution times of programs, the processing of massive amounts of audit data, the unavailability of a realistic experimental dataset and the automatic minimization of the false positive rate while dealing with the dynamics of normal activities. This study provides three original and significant contributions to this field of research which represent a marked advance in its body of knowledge. The first major contribution is the generation and release of a realistic intrusion detection systems dataset as well as the development of a metric based on fuzzy qualitative modeling for embedding the possible quality of realism in a dataset's design process and assessing this quality in existing or future datasets. The second key contribution is constructing and evaluating the hidden host features to identify the trivial differences between the normal and abnormal artefacts of hosts' activities at a minimum processing cost. Linux-centric features include the frequencies and ranges, frequency-domain representations and Gaussian interpretations of system call identifiers with execution times while, for Windows, a count of the distinct core Dynamic Linked Library calls is identified as a hidden host feature. The final key contribution is the development of two new anomaly-based statistical decision engines for capitalizing on the potential of some of the suggested hidden features and reliably detecting anomalies. The first engine, which has a forensic module, is based on stochastic theories including Hierarchical hidden Markov models and the second is modeled using Gaussian Mixture Modeling and Correntropy. The results demonstrate that the proposed host features and engines are competent for meeting the identified challenges