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Binary classification aims to separate elements of a given dataset into two groups on the basis of some learned classification rule. It has extensive applications from security analytics, fraud detection, malware identification, and much more. Being a supervised machine learning method, binary classification relies on the presence of labeled training data that can be used as examples from which a model can learn what separates the classes.
As COVID-19 continues to make clear, being adaptable and resilient when the world changes can help a business stay alive. At Elastic, we know from experience that being distributed helps build a strong company that can scale and adapt as new challenges arise. In the spirit of open source and our relationship with the Elastic community, we’ve been offering tips and tricks on our blog and on social media about how to work effectively while remote.
A difficult question we come across with many customers is ‘what does normal look like for my network?’. There are many reasons why monitoring for changes in network behaviour is important, with some great examples in this article - such as flagging potential security risks or predicting potential outages.
Managing a network more effectively has been something our customers have been asking us about for many years, but it has become an increasingly important topic as working from home becomes the new normal across the globe. In this blog series, I thought I’d present a few analytical techniques that we have seen our customers deploy on their network data to: Better understand their network and Develop baselines for network behaviour and detect anomalies.
I wrote a quick script the other day to pull some SCOM Alert data for testing and thought I’d share a summarized version for anyone looking for easy options to extract their SCOM data. The below sample script pulls all of the SCOM Alerts from your Management Group and pushes them via REST to a specified endpoint. Using the Operations Manager module makes this pretty quick and painless. I’ve called out a couple of easy tweaks you can make below to the script itself.
More than 2,000 developers from around the world participated in our open source bot hackfest, which we hosted on HackerEarth from January 10 through March 2. The goal of the event was to work with our community to create open source chatbots that integrate with Mattermost to accelerate DevOps and DevSecOps workflows, and we received many amazing submissions! We gave away $10,000 in prizes, including $6,000 cash to our top contributors.