Cutting Through the Hype: How to Effectively Apply ML to Cybersecurity presented at FloCon2019 2019

by Jason Kichen,

Summary : Current cybersecurity challenges represent a machine-scale problem and large amounts of automation are required to solve it. Data scales will continue to grow, further compounding the challenge. Defenders need to use the internal network and host log data that is already at their disposal, cross-network and cross-host, to discover the presence of sophisticated adversaries. This talk will detail a machine-learning based approach for how to solve this difficult problem--automated internal network monitoring, with low false positive rates--to find sophisticated adversaries and their campaigns.It will discuss the three fundamental requirements to achieve effective monitoring with a reasonable, practical amount of resources:1.Focus on the adversary campaign holistically: Using a campaign-oriented framework for monitoring also simplifies what needs to be monitored. You only monitor behaviors the adversary must perform, the ones they cannot avoid, to succeed in their mission. This reduces the noise, false positives and level of effort required by analysts.2.Automation, machine learning, and interpretability: It is not possible today to directly model the problem: the community does not have enough examples of “known bad” (identified, true APT campaigns) and networks are too complex and varied. To frame this as a machine learning problem, it needs to be broken down into multiple sub-problems of monitoring for individual surprising behaviors. E.g., is this an unusual number of pings? Is this an unusually large data movement?3.Adapt to ever-changing environments and adversaries: the training of models must be automatic and not require human intervention, meaning they must train on data in situ, must be retrained and updated frequently to stay relevant, support using a variety of raw data sources, and be easily updatable to account for the latest and greatest adversary tactics.Any approach that lacks these necessary pieces will not scale to large networks or will lag behind evolving adversaries.Attendees will Learn:1) Knowledge of the ways ML can be effectively and ineffectively applied to the challenges of cybersecurity, so they are more educated on to evaluate different tools for their unique environments2) A strategic understanding of how to frame the problem of advanced threat detection so that machine learning can be effectively applied3) A more in-depth understanding of the core behaviors in the adversary campaign, and how that enables a reduction in false positives