The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Threat detection is as easy as finding a needle in a forest (even for machine learning)

Submitted by Ashish Verma (@vermaashish) on May 31, 2019

Session type: Full talk of 40 mins Status: Rejected


Last decade has seen an exponential rise in digital adoption of enterprises. We have moved on from just being an internet to internet of things and now internet of everything. Although connectedness has painted much brighter future but this has also provided an opportunity for cyber criminals. Cyber security has now become one of the top priorities of enterprises.
But threat detection is like finding a needle in a forest. There is enormous amounts of data generated in a modern enterprise (with no well defined perimeter) and you got to absorb, feel and then identify those rare malicious activities in the real time.
Considering the muscle required to deal with the scale and variety of enterprise security data, machine learning comes with a great potential to solve cybersecurity problems.
In this talk, I would like to cover:
a) Some enterprise cybersecurity challenges
b) How machine learning solves them
c) What are the challenges associated with generating value from machine learning in enterprise cybersecurity environment
d) Future of machine learning in cyber security


  1. Introduction to enterprise cyber security landscape
  2. High level overview of typical stages of an attack
  3. Problems with existing rule based approaches
  4. Gravity of the problem that we are dealing with from data volume, velocity and variety perspective
  5. What kind of data representation helps for security data
  6. How machine learning deals with threat detection (feature engineering and modeling techniques, etc.)
  7. Current challenges with machine learning in enterprise cyber security
  8. Future of machine learning in cyber security

Speaker bio

Ashish Verma is a machine learning researcher working in Qualys (Pune). He is currently leading machine learning research and building a machine learning based cybersecurity product for Qualys.
In the past he has built a scalable AI platform for digital marketing as co-founder of He is also currently authoring a book on application of deep learning in cybersecurity.



  • AB

    Abhishek Balaji


    a year ago

    Hi Ashish,

    Thank you for submitting a proposal. We need to see detailed slides and a preview video to evaluate your proposal. Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?
    • Who is the audience for this talk?

    We need your updated slides and preview video by Jun 27, 2019 to evaluate your proposal. If we do not receive an update, we’d be moving your proposal for evaluation under a future event.

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