An Improved Intrusion Detection Systems using BGAN

Intrusion Detection Systems (IDS) utilize machine learning to improve their performance in monitoring computer net-works' potential attacks. It is important to understand that the KDD Cup 99 dataset contains simulation data of computer network attack forms. However, because of the complexity of this data, ma-chine learning is required to determine the form of attacks. Artifi-cial Neural Network (ANN) and Random Forest (RF) techniques to classify forms of attacks against machine learning-based Intrusion Detection Systems. However, the performance of ANN and RF is still not optimal with Lower Quartile value below “0.5” for ANN and RF. As a result, we suggest Boundary Seeking Generative Ad-versarial Network (BGAN) to improve performance of ANN and RF in detecting attacks. Results indicate that BGAN can improve ANN and RF performance.

• Publication year

  2022

• Venue

  IEEE International Conference on Consumer Electronics

• Publication date

  2022-07-06

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/ICCE-Taiwan55306.2022.9869144
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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