Pendekatan Ensemble Learning untuk klasifikasi serangan DDoS

Muhammad Oriza Nurfajri, Guntur Budi Herwanto — Sat, 19 Jul 2025 00:00:00 +0000

This research proposes an ensemble learning approach for classifying Distributed Denial of Service (DDoS) attacks using the CIC-DDoS2019 dataset. DDoS attacks remain a significant threat to network security, necessitating efficient detection methods. We developed an ensemble model combining Random Forest, Gradient Boosting, and AdaBoost classifiers to enhance detection accuracy. Our methodology involves preprocessing the CIC-DDoS2019 dataset, extracting relevant features, and implementing both binary classification (benign vs. attack) and multiclass classification (attack type identification). The experimental results show that our ensemble model achieves an F1-score of 0.9967 for binary classification, with Gradient Boosting performing best among individual models. The multiclass classification reaches an accuracy of 0.8742 in distinguishing between different types of DDoS attacks. This research demonstrates that ensemble learning significantly improves the accuracy and reliability of DDoS attack detection compared to single-model approaches.

Keywords: Ensemble learning; DDoS attack; network security; CIC-DDoS2019; machine learning.

Analisis Kualitas Citra Steganografi Berbasis Spasial Pada Metode Least Significant Bits dan Pixel Value Differencing

Nizar Haris Masruri — Sat, 06 Sep 2025 00:00:00 +0000

Steganography is a method in network security systems that functions to hide confidential information by inserting it into other media without changing the authenticity of the information. One of the steganography media is images. Image steganography has two domains: spatial and frequency. The spatial domain works on the pixels of an image, and the frequency domain works on the image frequency. Spatial image steganography is said to be good if the level of stego-image damage is very small. There are several intermediate methods in spatial steganography, namely: LSB, PVD, GLM, EBE, RPE, and others. However, there are two most popular methods, namely LSB (Least Significant Bit) and PVD (Pixel Value Differencing). Therefore, this study aims to test the quality of stego-images produced by these two methods. Testing is carried out using the MSE and PSNR value parameters. A stego-image is considered good when the MSE value is close to 0 and the PSNR value is above 30 dB. From the test data used, this study produced an MSE value of 0.0006 for LSB stego-images and 0.007 for PVD stego-images. The PSNR value was 90 dB for LSB stego-images and 72 dB for PVD stego-images. This study concluded that LSB stego-images have less damage than PVD stego-images, so the researchers recommend using the LSB method in carrying out the steganography process.

Keywords: Spatial;MSE;PSNR;LSB;PVD

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Pendekatan Ensemble Learning untuk klasifikasi serangan DDoS

Analisis Kualitas Citra Steganografi Berbasis Spasial Pada Metode Least Significant Bits dan Pixel Value Differencing