Improving Botnet host prediction with encryption and GRU for enhanced network security

Abstract

This paper examines the challenges of reliably and securely predicting Botnet hosts, a crucial aspect of network security. Existing Botnet detection systems often fail to address data privacy concerns and struggle with evolving attack methods. This study proposes an innovative approach to improve the security and accuracy of Botnet host prediction by integrating deep learning with encryption. The proposed method employs encryption techniques such as data encryption standard (DES) and blum-blum-shub (BBS) to protect sensitive data in a text data set of 2,100 IP addresses, consisting of Botnet hosts and benign hosts. Several pre-processing techniques, including moving average and missing value handling, are implemented to optimize the model performance. The effectiveness of the system is evaluated using performance metrics such as F1-score, recall, accuracy, and precision. Experimental results show that the proposed approach significantly outperforms existing methods in accuracy, which have not achieved the maximum accuracy per IP Host within a given time frame, while providing enhanced security through encryption on text data. The study concludes that combining deep learning with encryption on text data offers a promising solution for reliable and secure Botnet host prediction data. Future research will focus on testing larger and more diverse data sets, as well as analyzing the impact of different encryption techniques on the overall accuracy and security of the system.