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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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Journal : JURIKOM (Jurnal Riset Komputer)

 1 
Optimalisasi Seleksi Fitur Untuk Deteksi Serangan Pada IoT Menggunakan Classifier Subset Evaluator Kurniabudi Kurniabudi; Abdul Harris; Elvira Rosanda
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4618

Abstract

The Internet of Things (IoT) enables a wide variety of intelligent devices to connect and interact. The rapid development of technology and protocols as well as the growth of networks, makes IoT a security risk. The increasing number of interconnected intelligent electronic equipment has an impact on the complexity of the network and the increase in the volume of network traffic resulting in high-dimensional data. The feature selection technique has been proven to reduce very large (high-dimensional) network traffic data in the Intrusion Detection System (IDS). The feature selection technique is also faced with the problem of imbalanced data. In real network traffic data tends to be imbalanced, where attack traffic is less than normal data. IoT as a complex network produces a large number of features. However, not all features are relevant for identifying normal traffic and attacks. The right feature selection technique is needed to produce optimal features. In this study, a wrapper-based feature selection technique is proposed using a subset evaluator classifier with the J48 algorithm. The dataset used is CICIDS-2017 MachineLearningCSV version. Of the 78 features analyzed using the proposed method, 15 features were generated as optimal features. Optimal features are used for anomaly detection using the Random Forest algorithm. The experimental results show that attack detection with optimal features produces an average accuracy of 99.87% on training and testing data.
Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network Eko Arip Winanto; Kurniabudi Kurniabudi; Sharipuddin Sharipuddin; Ibnu Sani Wijaya; Dodi Sandra
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5298

Abstract

The complex network in the Internet of Things is challenging to maintain network security. With network complexity including data, protocols, sizes, communications, standards, and more, it becomes difficult to implement an intrusion detection system (IDS). One way to improve IDS on complex IoT networks is by using deep learning to detect attacks that occur on complex IoT networks. Recurrent neural network (RNN) is a deep learning method that enhances the detection of complex IoT networks because it takes into account the current input as well as what has been learned from previously received inputs. When making decisions about RNNs, consider current information as well as what has been learned from previous input. Therefore, this study proposes the RNN method to improve the performance of attack detection systems on complex IoT networks. The results of this experiment show satisfactory results by increasing the performance of the accuracy detection system in complex IoT networks which reaches 87%.
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.
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