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Enhanced Network Anomaly Detection Using Deep Learning Based on U-Net Model

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 716))

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Abstract

Enhanced Network anomaly detection is an open topic that expects to recognize network traffic for security purposes. Anomaly detection is currently one of the critical difficulties in many areas. As data multiplies needs tools to process and analyze different data types. The motivation behind the anomaly detection method is to detect when an entity differs from its normal behaviour. Due to the increasing complexity of calculations and the qualities of the data, it isn’t easy to choose a tool for all types of anomalies. To overcome these issues, this work proposed Support Vector Regression Boosting (SVRB), used for anomaly detection and optimization-based deep learning rates to manage the nearest update and static structure for random weight vector features. These nonlinear methods investigate assessing the elements in secret elements and time complexities. Our model provides a complete description of the recurrence patterns resulting from complex traffic dynamics during “noisy” network anomalies, categorized by computable changes in the statistical effects of the traffic time series. Use the UNIBS dataset to evaluate the performance of the enhanced Support Vector Regression Boosting (SVRB). The simulation results show the improved Support Vector Regression Boosting (SVRB) can achieve higher accuracy using this dataset features.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, India

    P. Ramya, S. G. Balakrishnan & A. Vidhiyapriya

Authors
  1. P. Ramya
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  2. S. G. Balakrishnan
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  3. A. Vidhiyapriya
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Corresponding author

Correspondence to S. G. Balakrishnan .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Ramya, P., Balakrishnan, S.G., Vidhiyapriya, A. (2023). Enhanced Network Anomaly Detection Using Deep Learning Based on U-Net Model. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_24

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