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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Information Management For Educators And Professionals (IMBI) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi METIK JURNAL Building of Informatics, Technology and Science Just TI (Jurnal Sains Terapan Teknologi Informasi) Brahmana : Jurnal Penerapan Kecerdasan Buatan Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Buletin Poltanesa Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Informatika Wicida Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam eProceedings of Engineering J-Icon : Jurnal Komputer dan Informatika Journal of Informatics, Electrical and Electronics Engineering Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Sasambo: Jurnal Abdimas (Journal of Community Service)
Pitrasacha Adytia
Sistem Informasi, STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Neuroscience Social Sciences Transportation Other

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Journal : Buletin Poltanesa

 1 
Identifikasi Serangan Low-Rate DDOS Berbasis Deep Learning Wahyuni Wahyuni; Pitrasacha Adytia
Poltanesa Vol 23 No 2 (2022): Desember 2022
Publisher : P2M Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i2.1737

Abstract

LowRate DDoS (LDDoS) is a variation of DDoS attack that sends fewer packets than conventional DDoS attacks. However, by sending a smaller number of packets and using a unique attack period, low-rate DDoS is very effective in reducing the quality of an internet network-based service due to full access. On the other hand, the low-rate DDoS with its nature also makes it difficult to detect because it looks more mixed with normal user access. The Deep Learning model that will be used in this research is the RNN LSTM (Long Short Term Memory) model. LSTM is a neural network architecture which is good enough to process sequential data. This model is better than the simple RNN model. The research method is adapted to the SKKNI No. 299 of 2020. However, this research will be carried out until the model development stage, namely the evaluation model. From the results of the research that has been done, it can be concluded that the RNN LSTM model can be used to classify low-rate DDOS attacks using feature selection. The accuracy of the training data on the validation data is around 98% and after visualizing the data for accuracy and loss, it can be concluded that the model is quite good, aka there is no underfitting or overfitting. While the accuracy obtained for testing data is 0.97%.
Identifikasi Serangan Low-Rate DDOS Berbasis Deep Learning Wahyuni, Wahyuni; Adytia, Pitrasacha
Poltanesa Vol 23 No 2 (2022): Desember 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i2.1737

Abstract

LowRate DDoS (LDDoS) is a variation of DDoS attack that sends fewer packets than conventional DDoS attacks. However, by sending a smaller number of packets and using a unique attack period, low-rate DDoS is very effective in reducing the quality of an internet network-based service due to full access. On the other hand, the low-rate DDoS with its nature also makes it difficult to detect because it looks more mixed with normal user access. The Deep Learning model that will be used in this research is the RNN LSTM (Long Short Term Memory) model. LSTM is a neural network architecture which is good enough to process sequential data. This model is better than the simple RNN model. The research method is adapted to the SKKNI No. 299 of 2020. However, this research will be carried out until the model development stage, namely the evaluation model. From the results of the research that has been done, it can be concluded that the RNN LSTM model can be used to classify low-rate DDOS attacks using feature selection. The accuracy of the training data on the validation data is around 98% and after visualizing the data for accuracy and loss, it can be concluded that the model is quite good, aka there is no underfitting or overfitting. While the accuracy obtained for testing data is 0.97%.
Co-Authors Adha Trisna Lidya Alvin Hernanda Ekaputra Amelia Yusnita Andriawan Arriyanti, Eka Asmaramany, Dimas Astin, Siti Namira Rizqi Aulia Abdul Rahman Bustomi , Tommy Didi Kuswandi Ekawati Yulsilviana Ekawati, Hanifah Fariz Mirza Ahmad Hanifah Ekawati Hanifah Ekawati Harianto, Kusno Heny Pratiwi Heny Pratiwi I Nengah Riandika Ika Nurmila Avinda Ita Arfyanti Kasim, Fadly Kelik Sussolaikah Lisda Aulia Rahmi Maulana Umar Muhammad Ahsanu Qaulan Muhammad Fachri Sanjaya Muhammad Fahmi Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Niansyah, Sugih Nurhuda, Asep Nursobah Nursobah, Nursobah Pahrudin, Pajar Pratama, Jerio Putra Rahmat Hidayat Reza Andrea Rizky Zakariyya Rasyad SA'AD, MUHAMMAD IBNU Salmon Salmon Salmon Salmon Salmon, Salmon Solly Aryza Supriady Supriady Sussolaikah, Kelik Tommy Bustomi Ujang Sumarwan Wahyuni - Wahyuni wahyuni wahyuni Yudha Satria Yuli Adam Prasetyo Yunita Yunita Yunita Yunita