Anirudh Kondaveeti

Anirudh Kondaveeti is a Principal Data Scientist and the lead for Security Data Science at Pivotal. Prior to joining Pivotal, he received his B.Tech from Indian Institute of Technology (IIT) Madras and Ph.D. from Arizona State University (ASU) specializing in the area of machine learning and spatio-temporal data mining. He has developed statistical models and machine learning algorithms to detect insider and external threats and "needle-in-hay-stack" anomalies in machine generated network data for leading industries.

  • Detecting Risky Assets in an Organization Using Time-Variant Graphical Model

    Detecting Risky Assets in an Organization Using Time-Variant Graphical Model

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  • Insider Threat Detection: Detecting Variance in User Behavior using an Ensemble Approach

    Insider Threat Detection: Detecting Variance in User Behavior using an Ensemble Approach

    Insider threat detection is a topic of growing interest these days due to the increasing number of cyber attacks. Understanding user activity is crucial to detect malicious insiders.

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  • Graph Mining For Detecting Coordinated Network Attacks

    Graph Mining For Detecting Coordinated Network Attacks

    In this series of blog posts, Pivotal’s Jin Yu and Anirudh Kondaveeti discuss how data science techniques can be used to detect network security threats. This post introduces the use of graph...

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  • Sequential Pattern Mining Approach for Watering Hole Attack Detection

    Sequential Pattern Mining Approach for Watering Hole Attack Detection

    As malware techniques continue to evolve, it becomes increasingly challenging to detect network security threats, especially Advanced Persistent Threats (APTs) that are orchestrated by...

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  • Multivariate Time Series Forecasting for Virtual Machine Capacity Planning

    Multivariate Time Series Forecasting for Virtual Machine Capacity Planning

    In this blog, we continue our blog series on multivariate time series to apply this modeling approaches for forecasting virtual machine capacity planning. This technique can be broadly applied to...

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  • Case Study: Using Data Science to Detect Defects In Semiconductors

    Case Study: Using Data Science to Detect Defects In Semiconductors

    In this post, Anirudh Kondaveeti, a Principal Data Scientist at Pivotal, provides an in-depth, real-world example of how data science applies to mechanical and materials engineering in the...

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  • Forecasting Time Series Data with Multiple Seasonal Periods

    Forecasting Time Series Data with Multiple Seasonal Periods

    Time series data is produced in domains such as IT operations, manufacturing, and telecommunications. Examples of time series data include the number of client logins to a website on a daily...

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