Showing papers by "Ishwar K. Sethi published in 2013"
TL;DR: The authors identify relevant security attacks in Wireless Cellular Network by conducting experiments in four different platforms such as Iphone, Android, Windows and Blackberry and give answers based on the data captured by conducting real-life scenarios.
Abstract: The importance of wireless cellular communication in our daily lives has grown considerably in the last decade. The smartphones are widely used nowadays, besides voice communication; the authors routinely use them to access the internet, conduct monetary transactions, send text messages and query a lot of useful information regarding the location of specific places of interest. The use of smartphones in their day-to-day communication raises many unresolved security and privacy issues. In this paper they identify relevant security attacks in Wireless Cellular Network. The authors conduct experiments in four different platforms such as Iphone, Android, Windows and Blackberry. The packets captured through Wireshark for approximately 24 minutes, giving them a lot of information regarding security and privacy issues involving the users. A lot of useful apps installed and used by the end-users have serious issues in terms of privacy and the information exposed. Which is the better platform comparing all four and what exactly do they expose from the user's information? What are the threats and countermeasures that the users should be aware of? The aim of the authors' paper is to give answers to the above questions based on the data captured by conducting real-life scenarios.
8 citations
04 Dec 2013
TL;DR: An extensive evaluation on the well-known benchmark datasets reveals the robustness and effectiveness of LIPID as well as its capability to handle illumination changes and texture images.
Abstract: Image representation through local descriptors is the basis of numerous computer vision applications. In the past decade, many local image descriptors such as SIFT and SURF have been proposed, yet algorithms requiring low memory and computation complexity are still preferred. Binary descriptors such as BRIEF have been suggested to satisfy this demand, showing a comparable performance but much faster computation speed. In this paper, we propose a novel local image descriptor, LIPID, which employs intensity permutation and interval division to yield an effective performance in terms of speed and recognition. Our method is inspired by LUCID, proposed by Ziegler and Christiansen [8]. An extensive evaluation on the well-known benchmark datasets reveals the robustness and effectiveness of LIPID as well as its capability to handle illumination changes and texture images.
3 citations