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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Computer Science and Information Technologies
Irwansyah Saputra
Nusa Mandiri University
Computer Science & IT Education Engineering

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Improving Botnet host prediction with encryption and GRU for enhanced network security Omega Joel Patria Moata; Irwansyah Saputra
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p141-158

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.
SMART CONTRACT-DRIVEN QUEUE MANAGEMENT FOR EFFICIENT ONLINE TICKET PURCHASING ON BLOCKCHAIN Mery Oktaviyanti Puspitaningtyas; Happid Ridwan Ilmi; Yulita Ayu Wardani; Irwansyah Saputra
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7367

Abstract

This study investigates how smart contract-driven queue management can be utilized to increase online ticket purchasing efficiency via blockchain technology. The system is designed to manage ticket purchase queues transparently and securely, using smart contracts written in the Solidity programming language and the Ionic UI framework. In addition, the system is connected with MetaMask as a transaction wallet, allowing users to purchase tickets directly and securely. Ganache serves as a testing environment for replenishing wallet balances without involving real transactions. The First In First Out (FIFO) approach is used to manage the transaction queue, with the first purchased ticket being processed first by the administrator. The administrator accepts each transaction, which is then confirmed by MetaMask. When the transaction is confirmed, the system automatically updates the ticket status. The implementation results show that this system effectively optimizes ticket transaction management transparently and securely. This work also makes a significant contribution to the application of blockchain technology for better management of online ticket purchasing systems, as well as minimizing the possibility of transaction errors and fraud.
Co-Authors Happid Ridwan Ilmi Mery Oktaviyanti Puspitaningtyas Omega Joel Patria Moata Yulita Ayu Wardani