Main Reference PaperSemi-supervised machine learning approach for DDoS detection, Applied Intelligence, 2018 [Java/Python/R]
  • An online sequential semi-supervised ML approach is proposed for DDoS detection. It is based on network Entropy estimation, Co-clustering, Information Gain Ratio, and Exra-Trees algorithm. The unsupervised approach allows to reduce the irrelevant normal traffic data for DDoS detection Whereas the supervised part allows to reduce the false positive rates of the unsupervised part and to classify the DDoS traffic accurately.

+ Description
  • An online sequential semi-supervised ML approach is proposed for DDoS detection. It is based on network Entropy estimation, Co-clustering, Information Gain Ratio, and Exra-Trees algorithm. The unsupervised approach allows to reduce the irrelevant normal traffic data for DDoS detection Whereas the supervised part allows to reduce the false positive rates of the unsupervised part and to classify the DDoS traffic accurately.

  • To accurately classify the anomalous traffic and to reduce the false positive rates

  • To detect the DDOs attacks.

+ Aim & Objectives
  • To accurately classify the anomalous traffic and to reduce the false positive rates

  • To detect the DDOs attacks.

  • The proposed scheme evaluates the performance in real-world datasets.

+ Contribution
  • The proposed scheme evaluates the performance in real-world datasets.

  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

+ Software Tools & Technologies
  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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