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Sharpening First-Time Access Alert for Insider Threat Detection

Residents participating in a neighborhood crime watch look out for signs of suspicious activity.  A new car parked on the street is probably the first thing to register in a resident’s mind.  Other hints like the time of day, what the driver carries, or how he loiters around all add up before one decides to call the police.  A User Behavior Analytics (UBA) system works much the same way, with various statistical indicators jointly working[…]

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Topics: data science

Anomalous User Activity Detection in Enterprise Multi-Source Logs

Network users’ activities generate events every day.  Logged events collected from multiple sources are valuable for user activity profiling and anomaly detection.  A good analytics use case for insider threat detection is to see if a user’s collection of events today is anomalous to her historical daily collections of events.  In an earlier blog, I highlighted a method to address this use case that leverages distributed computing built on HDFS and Apache Spark.  In this[…]

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Topics: data science

Account Resolution via Market Basket Analysis

Machine learning and statistical analysis have many practical applications in the detection of malicious user and entities as part of  User & Entity Behavior Analytics (UEBA) solutions.  Threat detection typically garners attention, this is as true on the show floor of security conferences, as it is for the text of marketing material.  Equally important, although less mentioned, is the application of machine learning for context estimation. Contextual information such as whether the machine is a[…]

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Topics: data science

UBA, UEBA, & SIEM: Security Management Terms Defined

This blog post seeks to define and explain some common acronyms involved in today’s security management landscape. What is UBA?   UBA stands for User Behavior Analytics and it’s an analytics led threat detection technology. UBA uses machine learning and data science to gain an understanding of how users (humans) within an environment typically behave, then to find risky, anomalous activity that deviates from their normal behavior and may be indicative of a threat. How[…]

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Topics: SECURITY, Uncategorized

Why is Critical Infrastructure So Vulnerable to Insider Threats?

Why is Critical Infrastructure so Vulnerable to Insider Threats?

A recent article in the Washington Post “Russia has developed a cyberweapon that can disrupt power grids, according to new research” came as a real reminder of the constant risk critical infrastructure operators face. The malware, which researchers have dubbed CrashOverride, is known to have disrupted only one energy system in Ukraine but could be deployed against U.S. electric transmission and distribution systems. The consequences of insider threats to critical infrastructure operators are much greater[…]

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Topics: SECURITY

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

5 Aspects to Consider When Evaluating SIEM Solutions

Considering SIEM Solutions

The SIEM category is quite mature; all Magic-Quadrant-Leader products are more than a decade old. In fact, the youngest product is 14 years old. When these products were in their prime, design requirements were different: an enterprise-class product might be expected to store 50 TB of logs; correlation rules were considered a major advance over signatures for detection; searches were judged on speed and it was acceptable to require complex search syntax; finally, the hard[…]

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Topics: SIEM

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
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