With the exponential increase of interconnected communicating devices which make up the Internet of Things (IoT), securing the network transmission and the fifth generation (5G) systems which is the bedrock for IoT concept actualization is becoming more and more challenging. One of the major attacks which poses a great risk to data transmission is the eavesdropper (Eve) attack which occurs in both single input, single output (SISO), multiple input and multiple output (MIMO) systems. Thus, in this study, our focus is to establish a secured connection in a multiple-antenna transmission when the channel state information (CSI) of Eve is unknown to the network users. Our model comprises a secure wireless communication standard where Eve performs either optimal matched filtering (OMF) or a basic matched filtering (BMF) while the transmitting IoT node employs smart jamming strategy in order to compromise the activities of Eve. With respect to this and in attempt to realize maximum privacy, we examined the design of optimal jamming parameters. In the end, the numerical analysis of our investigation indicates that a substantial privacy advantage is achievable while utilizing only full-duplex jamming against using artificial noise from the transmitter only. However, a joint performance of both results shows a higher privacy improvement.

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

In this poster we have proposed a novel network virtualization framework for information centric data center networks that decouples the control plane and data plane, where the fully distributed data plane is controlled and managed by a well-defined and centralized control plane. Exploiting some network virtualization notion, the proposed framework provides an easily and fast deployable solution to information centric data center networks to provide several virtual networks on same physical network and efficiently handle client requests. It also deals with reliable access to virtual networks as well as mapping of virtual and physical network elements. The proposed framework utilizes the efficiency of information centric networking by providing in-network caching and faster access to requested data.