Pramod N

Pramod N

Bengaluru, Karnataka, India
2K followers 500+ connections

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About

Currently Head Product and Data science for Rapido. I'm responsible for product-led…

Activity

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Experience

  • Rapido Graphic

    Rapido

    Bangalore Urban, Karnataka, India

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    Bengaluru Area, India

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    Bangalore

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    Bengaluru Area, India

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    Bangalore

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    Bengaluru Area, India

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    Bengaluru Area, India

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    Bangalore

Publications

  • SNIDS: An Intelligent Multiclass Support Vector Machines based NIDS

    International Conference on Emerging Trends in Electrical, Communication and Information Technologies (ICECIT-­‐2012)

    In this paper we present a statistical machine learning approach to the IDS using the Support Vector Machine (SVM).

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  • Effects of Information Filters – A Phenomenon on the Web

    International Journal On Information Retrieval and Research (IJIRR) Volume 2, Issue 2, IGI Global Publishers

    This article emperically analyzes the information filters commonly seen and analyzes their correctness and effects. Filters employed by Google’s search engine are used to analyse the effects of filtering on the web. A plausible solution to the unintentional -­‐ “errors” of filtering phenomenon is also discussed.

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  • gNIDS : Rule Based Network Intrusion Detection System using Genetic Algorithms

    International Journal of Intelligent Systems Technologies and Applications (IJISTA), Inderscience Publishers

    Abstract –Detection of intrusions in computer networks has been a growing problem motivating widespread research in computer science to develop better Intrusion Detecting Systems (IDS). The existing IDS have been quite static and lack the ability to adjust themselves to the new network traffic and hence new kinds of attack. In this paper, we present Genetic Algorithm (GA) based machine learning approach to identify such harmful/attack type of connections. The algorithm takes into consideration…

    Abstract –Detection of intrusions in computer networks has been a growing problem motivating widespread research in computer science to develop better Intrusion Detecting Systems (IDS). The existing IDS have been quite static and lack the ability to adjust themselves to the new network traffic and hence new kinds of attack. In this paper, we present Genetic Algorithm (GA) based machine learning approach to identify such harmful/attack type of connections. The algorithm takes into consideration different features in network connections such as source and destination IP, type of protocol and status of the connection to generate a classification rule set. The proposed method is efficient with respect to good detection rate and low false positives. The experimental results demonstrate the lower execution time of the proposed algorithm. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing.

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Honors & Awards

  • Best Achiever

    Department of Computer Science and Engineering, Association of Computer Engineers

  • Statement of Accomplishment for completing advanced track of Artificial Intelligence

    Professors Sebastian Thrun and Peter Norvig

Languages

  • English

    Native or bilingual proficiency

  • Kannada

    Native or bilingual proficiency

  • Hindi

    Professional working proficiency

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