Biblio

Though currently a “hot topic”, over the past fifteen years [1][2], there has been significant work on the use of machine learning to design large scale computer-communication networks, motivated by the complexity of the systems that are being considered and the unpredictability of their workloads. A topic of great concern has been security [3] and novel techniques for detecting network attacks have been developed based on Machine Learning [8]. However the main challenge with Machine Learning methods in networks has concerned their compatibility with the Internet Protocol and with legacy systems, and a major step forward has come from the establishment of Software Defined Networks (SDN) [4] which delegate network routing to specific SDN routers [4]. SDN has become an industry standard for concentrating network management and routing decisions within specific SDN routers that download the selected paths periodically to network routers, which operate otherwise under the IP protocol. In this paper we describe our work on real-time control of Security and Privacy [7], Energy Consumption and QoS [6] of packet networks using Machine Learning based on the Cognitive Packet Network [9] principles and their application to the H2020 SerIoT Project [5].

The paper introduces a smart system developed with sensors that is useful for internal and external security. The system is useful for people living in houses, apartments, high officials, bank, and offices. The system is developed in two phases one for internal security like home another is external security like open areas, streets. The system is consist of a mobile application, capacitive sensing, smart routing these valuable features to ensure safety of life and wealth. This security system is wireless sensor based which is an effective alternative of cctv cameras and other available security systems. Efficiency of this system is developed after going through practical studies and prototyping. The end result explains the feasibility rate, positive impact factor, reliability of the system. More research is possible in future based on this system this research explains that.