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All Journal International Journal of Advances in Intelligent Informatics Frontier Energy System and Power Engineering
Leonel Hernandez
Institución Universitaria de Barranquilla
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism Hanafi Hanafi; Andri Pranolo; Yingchi Mao; Taqwa Hariguna; Leonel Hernandez; Nanang Fitriana Kurniawan
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.942

Abstract

An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical model to remove outliers in pre-processing, SDAE, responsible for reducing data dimensionality, and LSTM-Attention, responsible for producing attack classification tasks. The model was implemented into the NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics measures. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.
Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations Aji Prasetya Wibawa; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Agung Bella Putra Utama; Leonel Hernandez
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p16-25

Abstract

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.
Co-Authors Ade Kurnia Ganesh Akbari Agung Bella Putra Utama Aji Prasetya Wibawa Akhmad Fanny Fadhilla Alfiansyah Putra Pertama Triono Andien Khansa’a Iffat Paramarta Andri Pranolo Faradini Usha Setyaputri Hanafi Hanafi Nanang Fitriana Kurniawan Taqwa Hariguna Yingchi Mao