Exact Conditional and Unconditional Cram\`er-Rao Bounds for Near Field Localization

This paper considers the Cram\`er-Rao lower Bound (CRB) for the source localization problem in the near field. More specifically, we use the exact expression of the delay parameter for the CRB derivation and show how this exact CRB can be significantly different from the one given in the literature and based on an approximate time delay expression (usually considered in the Fresnel region). This CRB derivation is then generalized by considering the exact expression of the received power profile (i.e., variable gain case) which, to our best knowledge, has been ignored in the literature. Finally, we exploit the CRB expression to introduce the new concept of Near Field Localization (NFL) region for a target localization performance associated to the application at hand. We illustrate the usefulness of the proposed CRB derivation and its developments as well as the NFL region concept through numerical simulations in different scenarios

Etat de renouvellement spatial et application sur un reseau cylindrique

CNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

A statistical detection mechanism for node misbehaviours in wireless mesh networks

International audienc

Improving Web Application Firewalls to detect advanced SQL injection attacks

International audienceInjections flaws which include SQL injection are the most prevalent security threats affecting Web applications[1]. To mitigate these attacks, Web Application Firewalls (WAFs) apply security rules in order to both inspect HTTP data streams and detect malicious HTTP transactions. Nevertheless, attackers can bypass WAF's rules by using sophisticated SQL injection techniques. In this paper, we introduce a novel approach to dissect the HTTP traffic and inspect complex SQL injection attacks. Our model is a hybrid Injection Prevention System (HIPS) which uses both a machine learning classifier and a pattern matching inspection engine based on reduced sets of security rules. Our Web Application Firewall architecture aims to optimize detection performances by using a prediction module that excludes legitimate requests from the inspection process

Hybrid Approach to Detect SQLi Attacks and Evasion Techniques

International audience—Injections flaws which include SQL injection are the most prevalent security threats affecting Web applications[1]. To mitigate these attacks, Web Application Firewalls (WAFs) apply security rules in order to both inspect HTTP data streams and detect malicious HTTP transactions. Nevertheless, attackers can bypass WAF's rules by using sophisticated SQL injection techniques. In this paper, we introduce a novel approach to dissect the HTTP traffic and inspect complex SQL injection attacks. Our model is a hybrid Injection Prevention System (HIPS) which uses both a machine learning classifier and a pattern matching inspection engine based on reduced sets of security rules

Bayesian-based model for a reputation system in vehicular networks

International audienc

Exact Cramer Rao Bound for near field source localization

International audienc

Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks

International audienc