Biblio

Cognitive radio is a promising technology that intends on solving the spectrum scarcity problem by allocating free spectrum dynamically to the unlicensed Secondary Users (SUs) in order to establish coexistence between the licensed Primary User (PU) & SUs, without causing any interference to the incumbent transmission. Primary user emulation attack (PUEA) is one such major threat posed on spectrum sensing, which decreases the spectrum access probability. Detection and defense against PUEA is realized using Yardstick based Threshold Allocation technique (YTA), by assigning threshold level to the base station thereby efficiently enhancing the spectrum sensing ability in a dynamic CR network. The simulation is performed using NS2 and analysis by using X-graph. The results shows minimum interference to primary transmissions by letting SUs spontaneously predict the prospective spectrum availability and aiding in effective prevention of potential emulation attacks along with proficient improvement of throughput in a dynamic cognitive radio environment.

Augmented reality is changing the way its users see the world. Smart augmented-reality glasses, with high resolution Optical Head Mounted display, supplements views of the real-world using video, audio, or graphics projected in front of user's eye. The area of Smart Glasses and heads-up display devices is not a new one, however in the last few years, it has seen an extensive growth in various fields including education. Our work takes advantage of a student's ability to adapt to new enabling technologies to investigate improvements teaching techniques in STEM areas and enhance the effectiveness and efficiency in teaching the new course content. In this paper, we propose to focus on the application of Smart Augmented-Reality Glasses in cybersecurity education to attract and retain students in STEM. In addition, creative ways to learn cybersecurity education via Smart Glasses will be explored using a Discovery Learning approach. This mode of delivery will allow students to interact with cybersecurity theories in an innovative, interactive and effective way, enhancing their overall live experience and experimental learning. With the help of collected data and in-depth analysis of existing smart glasses, the ongoing work will lay the groundwork for developing augmented reality applications that will enhance the learning experiences of students. Ultimately, research conducted with the glasses and applications may help to identify the unique skillsets of cybersecurity analysts, learning gaps and learning solutions.

To discuss secure key generation from imperfect random numbers, we address the secure key generation length. There are several studies for its asymptotic expansion up to the order √n or log n. However, these expansions have errors of the order o(√n) or o(log n), which does not go to zero asymptotically. To resolve this problem, we derive the asymptotic expansion up to the constant order for upper and lower bounds of these optimal values. While the expansions of upper and lower bonds do not match, they clarify the ranges of these optimal values, whose errors go to zero asymptotically.

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.