Main Reference PaperDetecting Anomalous Behavior in Cloud Servers by Nested-Arc Hidden SEMI-Markov Model with State Summarization, IEEE Transactions on Big Data, 2018 [Python/Apache Storm]
  • A proposed anomaly detection approach is applied for streaming of large scale data. It is detected by using following algorithms such as, state summarization and a novel nested-arc hidden semi-Markov model (NAHSMM). State summarization is used to extract usage behavior reflective states from a raw sequence. The proposed NAHSMM is used to form an anomaly detection algorithm with a forensic module to achieve the normal behavior threshold in the training phase.

Description
  • A proposed anomaly detection approach is applied for streaming of large scale data. It is detected by using following algorithms such as, state summarization and a novel nested-arc hidden semi-Markov model (NAHSMM). State summarization is used to extract usage behavior reflective states from a raw sequence. The proposed NAHSMM is used to form an anomaly detection algorithm with a forensic module to achieve the normal behavior threshold in the training phase.

  • To detect the anomalies from large scale data.

  • To improve the scalability.

Aim & Objectives
  • To detect the anomalies from large scale data.

  • To improve the scalability.

  • A technique is contributed that improves the accuracy of the proposed scheme.

Contribution
  • A technique is contributed that improves the accuracy of the proposed scheme.

  • 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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