Article Per Year (5 Year)
p-Index From 2020 - 2025
1.286
P-Index
This Author published in this journals
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

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU Rishika, Ravi Vendra; Pratomo, Baskoro Adi; Hidayati, Shintami Chusnul
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.
Graph-Structured Network Traffic Modelling for Anomaly-Based Intrusion Detection Pratomo, Baskoro Adi; Haykal, Muhammad Farhan; Studiawan, Hudan; Purwitasari, Diana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.94959

Abstract

The increasing complexity of cyber threats demands more advanced network intrusion detection systems (NIDS) capable of identifying both known and emerging attack patterns. In this study, we propose a graph-based anomaly detection approach for network intrusion detection, where network traffic is modeled as graph structures capturing both attribute and topological information. Five graph anomaly detection models—DOMINANT, OCGNN, AnomalyDAE, GAE, and CONAD—are implemented and evaluated on the UNSW-NB15 dataset. The constructed graphs use info_message attributes as nodes, with edges representing sequential traffic relationships. Experimental results show that the Graph Autoencoder (GAE) and Dual Autoencoder (AnomalyDAE) models outperform other methods, achieving F1-scores of 0.8728 and 0.7939, respectively. These findings demonstrate that reconstruction-based approaches effectively capture complex network behaviors, highlighting the potential of graph-based methods to enhance the robustness and accuracy of modern NIDS. Future work will explore dynamic graph modeling, attention mechanisms, and optimization techniques to further improve detection capabilities.
Co-Authors Al Kanza, Kalyana Putri Alzamzami, Moch. Nafkhan Arsyad, Hammuda Ary Mazharuddin Shiddiqi Damayanti, Putri Diana Purwitasari Diani, Nabila A'idah Emerson Eridiansyah Z Fadhila, Farah Dhia Fariz Andri Bakhtiar Fikriansyah, Irsyad Haykal, Muhammad Farhan Hidayati, Shintami Chusnul Hudan Studiawan I Made Agus Adi Wirawan Junaidi Junaidi Pramudya, Rafli Raihan Prinandika, Arya Gading Radityo Anggoro, Radityo Rishika, Ravi Vendra Royyana M. Ijtihadie Royyana Muslim Ijtihadie Saifuddin Saifuddin Saifuddin Saifuddin Santoso, Bagus Jati Santoso, Bagus Jati Supeno Djanali Teja, Andika Rahman Tohari Ahmad Vinorian, Muhammad Ersya Wahyu Suadi Waskhito Wibisono Waskitho Wibisono Wibowo, Prasetyo Yuniasri, Dini Zaini, Alfa Fakhrur Rizal