ANALYZE AND PREDICT OF HUMAN CYBER ATTACKERS USING ARTIFICIAL NEURAL NETWORK

Abstract

ABSTRACT: One of the world's biggest issues nowadays is cyber-attacks. Every day, they wreak serious economic harm to both persons and nations. . It constitutes criminal activity and when conducted on a large scale, it has the ability to undermine entire national economies. This paper reviews the various machine learning algorithms that have been developed for cyber security, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests, and Deep Learning. One technique to this problem is to apply actual statistics to determine the final results of the assault and discover the position of the party. The pre-processed image is passed through the Convolution, RELU and Pooling layer for feature extraction. A fully connected layer and a classifier is applied in the classification part of the image. This study uses ML methods to analyse cyber-crime consuming two patterns and to forecast how the specified characteristics will furnish to the detection of the cyber-attack methodology and perpetrator. Based on the comparison of eight distinct machine-learning methods, one can say that their accuracy was quite comparable. The Support Vector Machine (SVM) Linear outperformed all other cyber attack tactics in terms of accuracy. With a high degree of accuracy, the first model allowed us to forecast the types of attacks that the victims were most likely to experience. Future research directions include developing more robust machine learning algorithms, improving feature selection methods, developing more sophisticated deep learning models, and integrating human expertise with machine learning algorithms to improve their overall effectiveness.