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data science

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

Ransomworm: Don’t Cry – Act.

WannaCry

In July last year, we released our research report on the Anatomy of a Ransomware attack in which we looked into both the financial model of ransomware and then detection as it unfolds. Due to the recent WannaCry ransomware craze, we think it’s time to revisit. When we addressed ransomware last year, we made a significant comment about the ever-evolving nature of malicious software. We predicted that in the near future (evidently now) ransomware will move[…]

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Topics: data science, ransomware, SECURITY, SIEM, Uncategorized

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

The World Has Changed; Shouldn’t Your Security Change, Too?

From day one, Exabeam had a vision for something better than today’s SIEM solutions. We felt these products were fundamentally broken: SIEM log management was built on old, proprietary technology and was (over)priced by the byte; SIEM correlation rules were a mess and ineffective, and they caused more work for analysts than they eliminated. SIEM was broken and the opportunity to make something massively better was clear. Our first step was to win the UEBA[…]

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Topics: CUSTOMERS, 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

UEBA: When "E" Doesn't Stand for "Easy"

Three-letter acronyms are easy to remember and pronounce – adding more letters usually just adds friction. When Gartner renamed the User Behavior Analytics market from UBA to UEBA (i.e. User and Entity BA), it made the term more clunky but even more relevant. Most organizations understand the threat posed by user insiders, whether malicious or compromised. However, many don’t yet see the risks from “insider” machines, or as Gartner calls them, entities. While we are[…]

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Topics: data science, ransomware, 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

How to Leverage Behavioral Analytics to Reduce Insider Threat: Your Questions Answered

Last Thursday, we presented a webinar and discussed how UEBA technology can improve Insider Threat detection as well as overall SOC operational efficiency and noise reduction. I would like to thank the participants who were very active and showed interest by asking lots of questions. We felt we owed everyone the answers to the questions that were asked and may or may not have been answered during the webinar. And took the privilege to remove questions[…]

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Topics: data science, SECURITY
2017