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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia Progresif: Jurnal Ilmiah Komputer Buletin Ilmiah Sarjana Teknik Elektro Jurnal Abdi Insani Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) SmartComp The Indonesian Journal of Computer Science Tanggap Masyarakat untuk Aksi dan Sinergi
Ashari, Wahid Miftahul
Universitas AMIKOM Yogyakarta
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics

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Support Vector Machine Classification Algorithm for Detecting DDoS Attacks on Network Traffic Irawan, Yoki; Pramitasari, Rina; Ashari, Wahid Miftahul; Yansyah, Aiko Nur Hendry
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10003

Abstract

Distributed Denial of Service (DDoS) attacks represent a significant danger in network security because they can lead to extensive service interruptions. With these attacks increasingly mirroring regular traffic, smart and effective detection systems are essential. This research seeks to assess the efficacy of the Support Vector Machine (SVM) classification algorithm in identifying DDoS attacks in network traffic. The data utilized is CICIDS2017, focusing on the subset Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv, which contains both legitimate traffic and DDoS attacks like DoS-Hulk, DoS-GoldenEye, and DDoS. The preprocessing stage included eliminating duplicates and null entries, label binary encoding, normalization through Min-Max Scaler, and feature selection applying the Chi-Square technique. The data was divided into 80% for training and 20% for testing purposes. The Radial Basis Function (RBF) kernel was utilized to train the SVM model, and hyperparameter optimization was performed with GridSearchCV. The evaluation of the model's performance was conducted through accuracy, precision, recall, F1-score, confusion matrix, and visual representations including ROC and Precision-Recall Curves. The findings indicate that prior to tuning, the model reached an accuracy of 97%, which increased to 99% post-tuning, accompanied by an F1-score of 0.99. This shows that the SVM algorithm, when paired with appropriate preprocessing and optimization, is very efficient in identifying DDoS attacks within network traffic.
Pengaruh Load Balancing Pada Ser Pengaruh Load Balancing Pada Serangan DDoS Menggunakan Nginx: Pengaruh Load Balancing Pada Serangan DDoS Menggunakan Nginx Satrya Bhayangkara, Dimas; Miftahul Ashari, Wahid
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4118

Abstract

DDoS (Distributed Denial of Service) attacks are one of the most common cyberattacks. This attack can make a server experience an error. Various methods have been used to overcome this attack, one of which is load balancing. Load balancing is responsible for dividing the workload among various servers evenly. In this study, we used Nginx load balancing. The research was conducted by sending 100000, 300000, 400000, and 500000 requests. Throughput after using load balancing shows superiority, with an average of 9,581 kb/s compared to not using load balancing. Response time using load balancing is also better than not using load balancing, with an average of 4507.23 ms. However, the packet loss shows no packet loss, which is 0% after using load balancing and before using load balancing. The effect of load balancing on Nginx can prevent DDoS attacks with a load balancing algorithm that is still good enough to use.
Co-Authors Andriyan Dwi Putra Arginanta, Dallarizki Asharudin, Firman Devi Wening Astari Erfina Nurussa'adah Fajar Nur Aini Dwi Fatimah Fatimah, Fajar Nur Aini Dwi Febrian Putra Pratama Firman Asharudin Firman Asharudin Indrajati, Damar Jeki Kuswanto Jeki Kuswanto Koprawi, Muhammad Lukman Lukman Muhammad Koprawi Muhammad Tofa Nurcholis Muhammad Wildan Nenden Ranuma Ratri Nevy Bella Samantha Nugraha, Anggit Ferdita Nur'aini Putra, Andriyan Dwi Putra, Wahyu Sukestyastama Ramadhan, Rafiq Fajar Rina Pramitasari Rohmaniah, Diana Satrya Bhayangkara, Dimas Senie Destya Setiawan, Rangga Wahyu Sismoro, Heri Sunu Pamungkas, Hanfazano Rahmadimasti Yansyah, Aiko Nur Hendry Yoki Irawan