Biblio

There has been a significant rise in the use of wireless sensor networks (WSNs) in the past few years. It is evident that WSNs operate in unlicensed spectrum bands [1]. But due to the increasing usage in unlicensed spectrum band this band is getting overcrowded. The recent development of cognitive radio technology [2, 3] has made possible the utilization of licensed spectrum band in an opportunistic manner. This paper studies an introduction to Cognitive Radio Technology, Cognitive Radio Wireless Sensor Networks, its Advantages & Challenges, Cognitive Radio Technology Applications and a comparative analysis of node clustering techniques in CWSN.

Being a part of today’s technical world, we are connected through a vast network. More we are addicted to these modernization techniques we need security. There must be reliability in a network security system so that it is capable of doing perfect monitoring of the whole network of an organization so that any unauthorized users or intruders wouldn’t be able to halt our security breaches. Firewalls are there for securing our internal network from unauthorized outsiders but still some time possibility of attacks is there as according to a survey 60% of attacks were internal to the network. So, the internal system needs the same higher level of security just like external. So, understanding the value of security measures with accuracy, efficiency, and speed we got to focus on implementing and comparing an improved intrusion detection system. A comprehensive literature review has been done and found that some feature selection techniques with standard scaling combined with Machine Learning Techniques can give better results over normal existing ML Techniques. In this survey paper with the help of the Uni-variate Feature selection method, the selection of 14 essential features out of 41 is performed which are used in comparative analysis. We implemented and compared both binary class classification and multi-class classification-based Intrusion Detection Systems (IDS) for two Supervised Machine Learning Techniques Support Vector Machine and Classification and Regression Techniques.