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User Behavior Anomaly Detection Meets Distributed Computing

User Entity Behavior Analytics (UEBA) analyzes log data from different sources in order to find anomalies in users’ or entities’ behaviors. Depending on enterprise sizes and available log sources, data feeds can range from tens of gigabytes to terabytes a day. Typically, we need 30 days, if not more, to build proper behavior profiles. This calls for an analytics platform that is capable of ingesting and processing this volume of data. In this blog, I[…]

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Topics: data science

Too Many Alerts… Just Give Me the Interesting Ones!

Security analysts often wrestle with the high volume of alerts generated from security systems and much like the protagonist in The Boy Who Cried Wolf, many alerts tend to be ignored. Human analysts quickly learn to ignore repeated alerts in order to focus on the interesting ones.  Learning to screen out repeated alerts as false positives allows analysts to focus their finite time where it matters most. A natural question, then, is whether we can[…]

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Topics: data science, SECURITY

A Machine Learning Study on Phishing URL Detection

Many network attack vectors start with a link to a phishing URL. A carefully crafted email containing the malicious link is sent to an unsuspecting employee. Once he or she clicks on or responds to the phishing URL, the cycle of information loss and damage begins. It would then seem highly desirable to nip the problem early by identifying and alerting on these malicious links. In this blog, I’ll share some research notes here on[…]

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Topics: data science, SECURITY

First-time Access to an Asset - Is it Risky or Not?: A Machine Learning Question

Looking for outliers or something different from the baseline is a typical detection strategy in user and entity behavior analytics (UEBA). One example is a user’s first-time access to an asset such as a server, a device or an application. The logic is sound and is often used as an example in the press for behavior-based analytics. However, it is an open secret among the analytics practitioners that alerts of this type has a high[…]

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Topics: data science, SECURITY

A User and Entity Behavior Analytics Scoring System Explained

How risk assessment for UEBA (user entity behavior analytics) works is not unlike how humans assess risk in our surrounding environment. When in an unfamiliar setting, our brain constantly takes in data regarding objects, sound, temperature, etc. and weighs different sensory evidence against past learned patterns to determine if and what present risk is before us. A UEBA system works in a similar manner. Data from different log sources, such as Windows AD, VPN, database,[…]

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Topics: data science, SECURITY

Who do I belong to? Dynamic Peer Analysis for UEBA Explained

In user and entity behavior analytics (UEBA), a security alert is best viewed in context as discussed in my past webinar. A user’s peer groups provide useful context to identify and calibrate that user’s alerts. If a user does something unusual on the network, such as logging on to a server or accessing an application for the first time, we may reduce or amplify the risk score of this activity depending on whether the peers[…]

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Topics: data science, SECURITY, Uncategorized

A User and Entity Behavior Analytics System Explained – Part III

In this blog series, I’ve talked about the applicability of data science for user entity behavior analytics (UEBA).  The use of statistical analysis is best driven by expert knowledge; some machine learning examples were given to find contextual information for alert prioritization.  In this blog, let’s explore more use cases and examples where machine learning applies.  An Entity Categorization Example In my last post, I discussed how a data-driven classifier can be used to determine[…]

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Topics: data science

A User and Entity Behavior Analytics System Explained – Part II

In my last blog, I talked about the role of statistical analysis in a User Entity Behavior Analytics (UEBA) system.   Expert-driven statistical modeling is a key and core component of an anomaly detection system.  It is intuitive and easy to use and understand for analysts of all levels.  In part II of this series, I’ll discuss the role of machine learning in a UBA system. Machine learning is a method that is used to devise[…]

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Topics: data science

A User and Entity Behavior Analytics System Explained – Part I

This 3-Part blog series will demonstrate how data analytics of a User Entity Behavior Analytics (UEBA) product is at work to address cyber threats. In concept, a UEBA system such as Exabeam’s monitors network entities’ behaviors in an enterprise and flags behaviors that deviate from the norm.  While the benefits are understandable, there are many challenges.  In this blog series, I’ll focus only on the data analytics part of the system that has proven to[…]

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Topics: data science, SECURITY

Project Alignment, Hiring Shortfall As Top Big Data Challenges

2015 was an exciting ride for Exabeam.  For 2016, we are to scale for growth.  I am both happy and concerned about the thought.  I am happy because folks at Exabeam, from the top to bottom, and among data science, security, and platform engineering, are fully aligned.  This is critical for our company success.  I don’t take this for granted as in my past consulting years, I saw many interesting data analytics efforts fall or stall[…]

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Topics: data science
2017