Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 27 | Issue : 03 | Page :
    By

    Jermina F,

  • Kanishka v,

  • Rithika Shree A,

  • Suman Adithya S R,

  • Yogapriyan M,

  1. Asst. Professor, Department of CSE(cyber security) karpagam college of engineering, Coimbatore Tamilnadu, Tamil Nadu, india
  2. Student, Department of CSE(cyber security) karpagam college of engineering, Coimbatore Tamilnadu, Tamil Nadu, India
  3. Student, Department of CSE(cyber security) karpagam college of engineering, Coimbatore Tamil nadu, Tamil Nadu, India
  4. Student, Department of CSE(cyber security) karpagam college of engineering, Coimbatore Tamil nadu, Tamil Nadu, India
  5. Student, Department of CSE(cyber security) karpagam college of engineering, Coimbatore Tamil nadu, Tamil Nadu, India

Abstract

The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic data. To select the most relevant features, a hybrid method combining Extra Trees Classifier (ETC) and Random Forest (RF) algorithms was employed. This approach helps identify the most important features, effectively lowering dimensionality and enhancing the overall performance of the model. After selecting the features, network traffic classification is carried out using a combination of neural network models, such as Convolutional Neural Networks (CNN), DenseNet, and an advanced version known as Enhanced Deep Convolutional Neural Network (EDCNN). By combining the strengths of different neural network architectures, the ensemble method creates a more reliable and effective classification model. CNNs are adept at capturing spatial hierarchies in data, DenseNet enhances feature propagation and reduces the vanishing gradient problem, while EDCNN combines the advantages of CNN with additional layers to improve detection capabilities. The experimental results clearly demonstrate that the proposed approach—combining hybrid feature selection with an ensemble neural network—leads to a significant improvement in the accuracy of detecting malicious network traffic when compared to conventional or traditional detection methods. The model consistently outperforms baseline techniques by effectively identifying subtle and complex patterns within the data that are often overlooked by simpler algorithms. This notable enhancement in detection capabilities can be attributed to the synergy between intelligent feature selection, which filters out irrelevant or redundant information, and the robustness of ensemble learning, which leverages the strengths of multiple neural network models to make more reliable predictions. This research makes a valuable contribution to the ongoing efforts in strengthening cybersecurity frameworks. It provides evidence that incorporating advanced machine learning strategies, especially those that utilize both feature optimization and model aggregation, can lead to more proactive and precise threat detection systems. The findings underscore the potential of such approaches in real-world applications, where the rapid identification of threats is crucial for protecting sensitive data and maintaining the integrity of network operations. As cyber threats continue to evolve in sophistication, this work highlights the importance of adopting innovative, data-driven solutions to stay ahead of potential attackers.

Keywords: Extra Trees Classifier (ETC), Enhanced Deep Convolutional Neural Network (EDCNN), Random Forest (RF),Malicious Network Traffic Detection ,Hybrid Feature Selection

[This article belongs to Nano Trends – A Journal of Nano Technology & Its Applications ]

How to cite this article:
Jermina F, Kanishka v, Rithika Shree A, Suman Adithya S R, Yogapriyan M. Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network. Nano Trends – A Journal of Nano Technology & Its Applications. 2025; 27(03):-.
How to cite this URL:
Jermina F, Kanishka v, Rithika Shree A, Suman Adithya S R, Yogapriyan M. Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network. Nano Trends – A Journal of Nano Technology & Its Applications. 2025; 27(03):-. Available from: https://journals.stmjournals.com/nts/article=2025/view=228946


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Regular Issue Subscription Review Article
Volume 27
Issue 03
Received 25/03/2025
Accepted 14/04/2025
Published 09/10/2025
Publication Time 198 Days
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