Benjamin Ben

Membre doctorant.e

École Doctorale Cultures et Sociétés (ED529), Université Paris-Est (lien)

Rattachement : Université Paris Est Marne-la-Vallée

Établissement : Université de Paris Est Marne-la-Vallée

 

Directeur de thèse: Amos DAVID

Thèse en cours

  • Système d'observatoire pour l'aide à la décision: étude des apports du concept d'objets connectés

Thèmes de recherche développés

  • A compléter

Points forts des activités de recherche

  • A compléter

Autres activités

  • A compléter

Publications

ACL : Articles dans des revues internationales ou nationales avec comité de lecture (1)

  • 2014 — The Internet is rapidly growing in number of users, traffic levels and topological complexities. A vital component to understand the requirements, its capabilities and extension of the network is its traffic analysis. In literature, conventional traffic model does not capture today’s burstiness in Internet traffic with the assumption that inter-arrival time is independent and identically distributed. The Internet forwarding scheme has given provision for correlated inter-arrival times which are rampant when streams of packets arrive to a destination at the same time. Modern researchers are contemplating of modulating Poisson process with another distribution in form of Compound Poisson Process, Markov Modulated Poisson Process or use of Pareto model to tract actual traffic characteristics. Pareto is assumed in literature to have facility to cater for burstiness. This paper uses goodness of fit test to detect the exponential nature of the inter arrival times of Pareto generated network packets. A network simulator was used to generate statistics of arrival times of traffics were traced, inter-arrival times calculated, ranked and subjected to goodness of fit test. The analysed result fell into acceptable region which implies that Pareto can stand in place of conventional exponential distribution in modeling inter-arrival times of a bursty network.

AA : Autres articles (1)

  • 2013 — The most dreaded web application attack called Cross Site Scripting (XSS) attacks are still on the increase despite the research efforts being made. Usually, hackers upload XSS vectors into any vulnerable web site and wait for innocent victims who visit these sites. These victims are then attacked and exploited by the hacker’s XSS vectors. Several existing techniques require technical adjustments on client side browsers and server side environment variables, while other techniques try to nullify the effects of XSS on users viewing dynamic contents. Mitigating XSS from server side can guarantee a better result than any other technique because users are not required to make any configurations on their browsers and no XSS vector will find its way to the client side. In this research, a framework was developed, which is based on pattern matching using regular expressions. This framework will detect any occurrence of XSS vectors within the data collected from users and nullify them before passing it over to the web application for further processing. This implies that the web application may not store or process any XSS vectors. This framework was implemented using a PHP object oriented prototype model that can be easily integrated into existing web application. Evaluation of the framework was done using a web based PHP social network application and the results of our experiment shows that the proposed system is highly efficient in mitigating XSS attacks while maintaining a negligible runtime overhead on the web server. The purpose of this research is to design a simple XSS attack Filter framework that can be easily integrated into an existing web application which gives this research the potentials of generally reducing the rate of occurrences of XSS attacks on web applications. — Lien vers article en ligne