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<p>In today's digital world, organizations connect devices to the Internet for a sustainable and convenient experience. However, this exposes systems to severe cyber-attacks, creating an urgent need for security devices. Due to new technology, the growing complexity of networks necessitates a strong Intrusion Detection System (IDS) to monitor traffic and detect malicious activities. This paper proposes a novel framework that uses deep learning techniques to transform tabular data into image data for known cyber-attack detection. The model includes dimensionality reduction techniques, such as PCA and UMAP, employed sequentially, along with an autoencoder for data transformation. Three different deep learning-based models are used to identify different attacks: a modified Convolutional Neural Network (CNN) enhanced with an attention mechanism, a discriminator neural network from a Wasserstein Conditional Generative Adversarial Network with Gradient Penalty (WCGAN-GP), and a voting classifier that aggregates predictions from the modified CNN and the trained discriminator of WCGAN-GP to improve detection accuracy. Experiments are conducted on the NSL-KDD dataset to showcase enhanced accuracy, precision, recall, and f1-score performance. The proposed model achieves an accuracy of 93%. Compared to the state-of-the-art technique, it demonstrates superior performance over other models. Results indicate that the proposed system recognizes known cyber-attacks efficiently while effectively managing large-scale datasets.</p>

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Converting tabular to image data to design IDS applying deep learning techniques

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Phool Kumari,
  • Arpita Srivastava,
  • Ditipriya Sinha

摘要

In today's digital world, organizations connect devices to the Internet for a sustainable and convenient experience. However, this exposes systems to severe cyber-attacks, creating an urgent need for security devices. Due to new technology, the growing complexity of networks necessitates a strong Intrusion Detection System (IDS) to monitor traffic and detect malicious activities. This paper proposes a novel framework that uses deep learning techniques to transform tabular data into image data for known cyber-attack detection. The model includes dimensionality reduction techniques, such as PCA and UMAP, employed sequentially, along with an autoencoder for data transformation. Three different deep learning-based models are used to identify different attacks: a modified Convolutional Neural Network (CNN) enhanced with an attention mechanism, a discriminator neural network from a Wasserstein Conditional Generative Adversarial Network with Gradient Penalty (WCGAN-GP), and a voting classifier that aggregates predictions from the modified CNN and the trained discriminator of WCGAN-GP to improve detection accuracy. Experiments are conducted on the NSL-KDD dataset to showcase enhanced accuracy, precision, recall, and f1-score performance. The proposed model achieves an accuracy of 93%. Compared to the state-of-the-art technique, it demonstrates superior performance over other models. Results indicate that the proposed system recognizes known cyber-attacks efficiently while effectively managing large-scale datasets.