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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ipteks Terapan The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Informatika Dan Tekonologi Komputer (JITEK) pendidikan, science, teknologi, dan ekonomi Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi Bulletin of Informatics and Data Science Jurnal Pengabdian Masyarakat Unama
Kurniabudi, Kurniabudi
Universitas Dinamika Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health Other

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Detection of UDP Flooding DDoS Attacks on IoT Networks Using Recurrent Neural Network Warcita; Kurniabudi; Eko Arip Winanto
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.79601

Abstract

Internet of Thing (IoT) is a concept where an object can transfer data through a network without requiring human interaction. Complex IoT networks make it vulnerable to cyber attacks such as DDoS UDP Flood attacks, UDP Flood attacks can disrupt IoT devices. Therefore, this study proposes an attack detection method using a deep learning approach with the Recurrent Neural Network (RNN) method. This study uses Principle Component Analysis (PCA) to reduce the feature dimension, before learning using RNN. The purpose of this study is to test the combined performance of the PCA and RNN methods to detect DDoS UDP Flood attacks on IoT networks. The testing in this study used 10 datasets sourced from CICIOT2023 containing UDP Flood and Benign DDoS traffic data, and the testing was carried out using three epoch parameters (iterations), namely 10, 50, and 100. The test results using RNN epoch 100 were superior, showing satisfactory performance with an accuracy value of 98%, precision of 99%, recall of 99%, and f1-score of 99%. Based on the experimental results, it can be concluded that combining PCA and RNN is able to detect UDP Flooding attacks by showing high accuracy.
Deteksi Serangan ICMP Flood pada Internet of Things dengan Feature Selection dan Machine Learning Harid, Harid; Kurniabudi, Kurniabudi; Harris, Abdul
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8554

Abstract

IoT devices have played an important role in driving DDoS attacks, and are a threat to IoT networks. One of them is the ICMP Flood attack. To overcome attacks on IoT, one of them uses an Intrusion Detection System (IDS). However, on the other hand, IDS has challenges in handling the complexity of high-dimensional data. One of the suggested solutions to overcome the problem of data dimensions is the use of feature selection techniques. The Forward Selection feature selection technique is used to eliminate irrelevant features. This study compares the performance of the Random Forest and SVM algorithms. For experimental purposes, this study used the CICIoT2023 dataset, which represents IoT traffic. The use of Forward Selection obtained 11 selected features that will be used in the machine learning process using the Random Forest and SVM methods. Feature selection affects the computation time or processing time, because the fewer features used, the more the system's workload in carrying out the classification process. The test results show that the use of feature selection improves the performance of random forest with an accuracy of 100%. Meanwhile, the SVM model gets better accuracy by using feature selection with the highest accuracy of 99.4508% in the supplied test set test.
Two-steps feature selection for detection variant distributed denial of services attack in cloud environment Kurniabudi, Kurniabudi; Winanto, Eko Arip; Sharipuddin, Sharipuddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3945-3957

Abstract

The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%.
Persepsi Kesesuaian dan Kepuasan Penggunaan Media Sosial pada Perkuliahan: Pengujian Model Kurniabudi, Kurniabudi; Assegaf, Setiawan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3645.714 KB) | DOI: 10.25126/jtiik.201856907

Abstract

Penerimaan teknologi merupakan faktor penting, untuk keberlanjutan penggunaan sebuah teknologi. Model-model pengukuran telah banyak dikembangkan, namun belum mempertimbangkan kesesuaian dan kepuasan dalam penggunaan teknologi berkelanjutan. Pada penelitian yang sebelumnya penulis telah mengembangkan model kepuasan dan kesesuaian (Task-fit and Satisfaction Model) untuk mengidentifikasi persepsi dosen terhadap kesesuaian dan kepuasan penggunaan facebook sebagai sarana komunikasi dan informasi pada perkuliahan, namun belum diuji. Artikel ini menyajikan proses pengujian terhadap model tersebut. Responden penelitian ini adalah dosen di indonesia khususnya yang menggunakan facebook. Data penelitian dikumpulkan dengan menggunakan metode survey online. Metode Structural Equation Modeling (SEM) dan Partial Least Square (PLS) digunakan untuk analisis data. Hasil pengujian hipotesis memperlihatkan perceived task-fit, utilization dan satisfaction secara signifikan mempengaruhi continuance intention. Pengujian juga memperlihatkan bahwa Perceived task fit , confirmation, dan Service quality secara signifikan mempengaruhi satisfaction. Terdapat korelasi positif perceived task-fit terhadap utilization, dan service quality terhadap confirmation. Sedangkan pengujian coefficient of determination (R2), memperlihatkan continuance intention memperoleh nilai R2= 0.723, hal ini menunjukkan bahwa perentasi besarnya kemampuan model dalam memprediksi persepsi kesesuaian dan kepuasan dosen terhadap penggunaan facebook dalam perkuliahan sebesar 72.3%. AbstractAcceptance of technology is an important factor, for the continued use of a technology. Measurement models have been developed, but not many consider perceived of fitness and satisfaction in receiving technology. In the previous research the authors has developed a Task-fit and Satisfaction Model to identify lecturers' perceptions of the suitability and satisfaction of facebook usage as a means of communication and information on lectures, the model have not test yet. This paper aim to present the testing process for this model. Responden this research is a lecturer in Indonesia especially who use facebook. Data collected by online survey method. SEM with PLS approach used to data analysis. The results of hypothesis testing show that perceived task-fit, utilization and satisfaction significantly influence continuance intention. The results also show that Perceived task fit, confirmation, and Service quality significantly affect satisfaction. There is a positive correlation of perceived task-fit to utilization, and service quality to confirmation. While the coefficient of determination test, shows continuance intention obtained the value of R2 = 0.723, This shows that the magnitude of the model's ability to predict perceptions of fitness and lecturer satisfaction towards the use of Facebook in lectures is 72.3%.
Optimizing Autoencoder-Based Feature Selection for Attack Detection in IoT Networks via Machine Learning Approaches Winanto, Eko Arip; Kurniabudi, Kurniabudi; Sharipuddin, Sharipuddin
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.104

Abstract

The Internet of Things (IoT) presents significant security challenges as the number of connected devices continues to grow. One critical approach in developing efficient attack detection systems is the selection of relevant features to reduce model complexity without compromising accuracy. This study evaluates the effectiveness of Autoencoders as a feature reduction method for IoT network intrusion detection systems. Three machine learning algorithms are employed for comparative analysis: K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM). The dataset is evaluated both before and after feature reduction using an Autoencoder, with performance assessed based on accuracy, precision, recall, F1-score, training time, and the number of features. Experimental results demonstrate that the Autoencoder can reduce the number of features by up to 30% without significantly degrading performance. In fact, the NB and SVM models exhibit improvements in both accuracy and training efficiency. The KNN model shows a minimal performance decline, which remains within acceptable limits. Overall, the Autoencoder proves to be an effective method for feature reduction, maintaining or even enhancing detection efficiency and performance. These findings support the use of Autoencoders as an efficient feature selection technique in IoT-based attack detection systems.
Deteksi Serangan ICMP Flood pada Internet of Things dengan Feature Selection dan Machine Learning Harid, Harid; Kurniabudi, Kurniabudi; Harris, Abdul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8554

Abstract

IoT devices have played an important role in driving DDoS attacks, and are a threat to IoT networks. One of them is the ICMP Flood attack. To overcome attacks on IoT, one of them uses an Intrusion Detection System (IDS). However, on the other hand, IDS has challenges in handling the complexity of high-dimensional data. One of the suggested solutions to overcome the problem of data dimensions is the use of feature selection techniques. The Forward Selection feature selection technique is used to eliminate irrelevant features. This study compares the performance of the Random Forest and SVM algorithms. For experimental purposes, this study used the CICIoT2023 dataset, which represents IoT traffic. The use of Forward Selection obtained 11 selected features that will be used in the machine learning process using the Random Forest and SVM methods. Feature selection affects the computation time or processing time, because the fewer features used, the more the system's workload in carrying out the classification process. The test results show that the use of feature selection improves the performance of random forest with an accuracy of 100%. Meanwhile, the SVM model gets better accuracy by using feature selection with the highest accuracy of 99.4508% in the supplied test set test.
Co-Authors Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Rahim Abdul Rahim Ahmad Heryanto Albertus Edward Mintaria Albertus Edward Mintaria Ammar panji Pratama Bedine Kerim Bedine Kerim Candra Adi Rahmat Chindra Saputra Darmawijoyo, Darmawijoyo Dede Andri Wahyudin Deris Stiawan Dodi Sandra Dodi Sandra Dr. Hendri, S.Kom., S.H., M.S.I., M.H Eko Arip Winanto Eko Arip Winanto Elvi Yanti Elvi Yanti Elvira Rosanda Erick Fernando Erick Fernando Erick Fernando B311087192 Fachruddin Febriyan Nurmansyah Harid, Harid Harris, Abdul Hendri Hendri Hendri Hendri Hendy Saryanto Herry Mulyono Ibnu Sani Wijaya Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Rofi’i Irawan, Beni Irfan, Fadhel Muhammad Kurniabudi Lola Yorita Astri, Lola Yorita Minal Juadli Mintaria, Albertus Edward Mohd Yazid bin Idris Mohd Yazid Bin Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Rafly Ramadhan Muhammad Riza Pahlevi Mulyono, Herry Nabila Kamila Hasna Pandapotan Siagian Pareza Alam Jusia, Pareza Alam Purnama, Benni Putri Nawang Wulan Rahman saibi Rahmat Budiarto Rahmat Budiarto Realensi Realensi Rilis Pebriyanti Siringo Ringo Ryan Sihopong Parlindungan Siregar Samsuryadi Samsuryadi Setiawan Assegaf Sharipuddin, Sharipuddin Sharipuddin, Sharipuddin Shelby Amalia Sandi Siagian, Pandapotan Suwaldo Aris Ferry Hutabarat Syamsul Arifin Syifqi, Achmad Triokta Putra Ulil Amri, Nugraha Valensia, Vally Veronica Veronica VERONICA VERONICA WILLY RIYADI Winarno Wirmaini, Wirmaini Yudi Novianto Yudi Novianto Yundari, Yundari Zulwaqar Zain Mohtar