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All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Briliant: Jurnal Riset dan Konseptual Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Sewagati
Baskoro Adi Pratomo
Institut Teknologi Sepuluh Nopember
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU Ravi Vendra Rishika; Baskoro Adi Pratomo; Shintami Chusnul Hidayati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1241

Abstract

Technological development and the growth of the internet today have a positive and revolutionary impact in various areas of human life, such as banking, health, science, and more. The presence of Open Data and Open API also facilitates the exchange of data and information between entities without the restrictions imposed by different regions and geographical areas. However, information openness not only has a positive impact but also makes data vulnerable to data theft, viruses, and various other types of cyber attacks. The large-scale data exchange that occurs across the network poses a challenge in detecting unusual activity and new cyber attacks. Therefore, the existence of an Intrusion Detection System (IDS) is urgently essential. The IDS helps system administrators detect cyber attacks and network anomalies, thus minimizing the risk of data leaks and intrusions. The research developed a new approach using time-based sequential clustered data sets in the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. This IDS model was implemented using the CIC-IDS 2018 data set, which has more than 4 million data lines. The capabilities and uniqueness of the LSTM and GRU models are used to classify and determine various attacks in IDS based on sequential data sets ordered by time and clustered according to the destination ports and protocols, such as TCP and UDP. The model was evaluated using the accuracy, precision, recall, and F-1 scores matrix, and the results showed that the time-based sequential clustered models in LSTM and GRU have an accurities of up to 97.21%. This suggests that this new approach is good enough to be applied to the future IDS models.
Co-Authors Al Kanza, Kalyana Putri Alzamzami, Moch. Nafkhan Arsyad, Hammuda Ary Mazharuddin Shiddiqi Bakhtiar, Fariz Andri Diana Purwitasari Diani, Nabila A'idah Emerson Eridiansyah Z Fadhila, Farah Dhia Fikriansyah, Irsyad Haykal, Muhammad Farhan Hudan Studiawan I Made Agus Adi Wirawan Pramudya, Rafli Raihan Prinandika, Arya Gading Radityo Anggoro, Radityo Ravi Vendra Rishika Royyana M. Ijtihadie Royyana Muslim Ijtihadie Saifuddin Saifuddin Saifuddin Saifuddin Santoso, Bagus Jati Santoso, Bagus Jati Shintami Chusnul Hidayati Supeno Djanali Teja, Andika Rahman Tohari Ahmad Vinorian, Muhammad Ersya Wahyu Suadi Waskhito Wibisono Waskitho Wibisono Zaini, Alfa Fakhrur Rizal