An Effective Intrusion Detection Scheme over Wireless Communication Environment based on Artificial Intelligence Enabled Modified Learning Strategy

Abstract

based on Artificial Intelligence Enabled Modified Learning Strategy In this present digital world, each and every document need to be in system format as well as preserved into the server end. So, that the transactions between client and server entities are handled by using wireless communication schemes. There are millions of individuals hooked up to the internet enabled network today, and each of them possesses some level of sensitive information. Nowadays, the most valuable asset is knowledge about the organization or its users. This attracts the attackers or hackers to acquire the personal or private information from the server end or else the path that is used for communication. Numerous types of hackers attempt to penetrate a private system in order to obtain access and extract all critical data that could harm the infrastructure, posing a significant threat to the firm. These kinds of attacks are called intrusions and the attackers are termed as intruders. This paper has a detailed study of the past detection scheme and proposed a novel intrusion detection scheme to support users to prevent the data from intruders. The major objective of this paper is to design a novel Intrusion Detection System with full of attack handling and identification capabilities as well as prevent the data safe in server end without give a lead to intruders to make an attempt. The motto of this work is to introduce a modified learning strategy with respect to the conventional learning scheme called Artificial Neural Network (ANN) and provides a robust support and resistance to identify the intruders over the wireless communication environment. To make the users more convenient to preserve the data over the remote server environment and make transactions in secured manner with the help of the proposed learning scheme. These objectives are clearly illustrated with graphical representations over the resulting section of this paper. This paper introduced a modified learning scheme called Artificial Intelligence Enabled Modified Learning Strategy (AIeMLS), in which it provides a support to genuine network users to carry on with their private data as well as preserve the data over the server end without any threats. The proposed logic called AIeMLS is derived from the conventional learning scheme called ANN, in which it is composed of a number of processing components that accept inputs and generate outputs according to their predetermined activation functions and it is being used to characterize complex situations and to anticipate the contents of specified model parameters based on their learning data. By modifying the last layer of the ANN and adapt the logic of Support Vector Machine (SVM) into it to customize the model into a novel design. This is helpful to identify the intrusions in an intellectual manner with maximum level of accuracy and performance ratio.The proposed modified learning approach AIeMLS attains the maximum accuracy ratio of 98.5% in an outcome, whereas the other conventional algorithms such as ANN and SVM attain 98% and 96% accuracy in results. These resulting performance and efficiency is portrayed in the resulting section using graphical outputs.This approach of identifying the intrusions over the wireless communication environment is helpful to users in real-time environment and significantly preserves the data in proper manner. In future the work can further be enhanced by means of adding some crypto security features to enhance the security level more in the proposed work.