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How Fidelity Bank Stays on Top of Cybersecurity with Prelert

Fidelity is a fourth-generation family-owned financial institution that traces its beginnings back to 1905. Today, the organization has grown to more than 450 employees and nearly $1.7 billion in assets across 23 offices in Kansas and Oklahoma.

In this Q&A, learn more about how Prelert’s machine learning anomaly detection solution helped make Fidelity Bank's IT team more efficient.

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Fighting Alert Fatigue

As originally published by Infosec Island

While there’s been a great deal of discussion surrounding the high-level value of behavioral analytics in mitigating losses due to cyberattacks, the realization of this benefit usually begins with relieving an organization’s employees from the dreaded condition known as "alert fatigue."

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Anomaly Detection in Elasticsearch 101: Population Outliers

My series on basic anomaly detection in Elasticsearch continues with this fourth entry - a discussion on how to leverage Prelert’s machine learning based approach to identify members of a population that are different than their peers.

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SIEM or SIEVE?

As originally published by Information Security Buzz

Security Information and Event Management (SIEM) systems have been the cornerstone of many IT security monitoring strategies. But as the threats facing organizations and the tools used to protect against them have become more complex, SIEMs have become more like sieves.

Sieve. /siv/ noun. 1. A utensil consisting of a wire or plastic mesh held in a frame, used for straining solids from liquids, for separating coarser from finer particles, or for reducing soft solids to a pulp.

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Anomaly Detection in Elasticsearch 101: Unstructured Data

My blog series on anomaly detection in Elasticsearch continues with this third installment - finding anomalies in unstructured data by first bringing structure via dynamic categorization from machine learning.

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Anomaly Detection in Elasticsearch 101: Metric Deviations

In the first blog of this series, we discussed how to detect changes in event rates. This, the second blog, focuses on detecting unusual temporal changes in metric values.

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Anomaly Detection in Elasticsearch 101: Event Count Change Detection

I’m kicking off a multi-part series of blogs around effective anomaly detection for data in Elasticsearch. These will cover the basics around several different kinds of fundamental use cases and data types. The first in the series is this article: Event Count Change Detection.

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Star Wars X – Attack of the DROWNs: Machine Learning-based Anomaly Detection Finds the DROWN SSLv2 Vulnerability

If you are working in the security space, you’ve probably heard of the recent critical DROWN Vulnerability (CVE-2016-0800,CVE-2016-0703) reported last month, which can be used by attackers to decrypt both passively eavesdropped and MITM-proxied TLS sessions putting millions of HTTPS/OpenSSL-secured sites at risk [1,6].

What’s interesting about this latest high-impact vulnerability is that it leverages a combination of protocols and misconfiguration of a target server, not a specific software security flaw as many vulnerabilities do, affecting a significant number of HTTPS, SMTP, SMTPS, IMAP, IMAPS, POP3, and POP3S servers supporting SSLv2.

According to the paper describing the DROWN vulnerability, approximately 11.5 million (33%) of all HTTPS servers (general version of the attack) / 26% of all HTTPS servers (special version of attack, fast enough to decrypt premaster online during a connection handshake) are affected by this vulnerability.

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Java 8 and Virtual Memory on Linux

The -Xmx option can be used to tell a JVM the maximum heap size it’s allowed to use.  The “top” command on Linux can report current resource usage for running processes.  But if the JVM really is respecting the maximum heap size specified by the -Xmx option, how come the virtual memory usage reported by “top” is so high?

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7 Reasons to Deploy Retail Order Analytics

The retail analytics market is growing almost as fast as the retail market itself. In fact, a recent study by Reportlinker estimates that the retail analytics market will grow from $2.2 billion in 2015 to $5.1 billion in 2020. The report finds that the factors driving this growth include the rise in retail data volumes, types and accumulation, as well as demand for omnichannel insights.

However, most retailers have not yet deployed analytics solutions at scale, and most who have are still in the early stages of deployment. That means it’s not too late to get started, and you still have a huge opportunity to gain an edge on your competitors using data. In fact, it’s never too late to get a competitive edge from your data.

To accomplish this, you will want to start at the top, using analytics to first detect revenue-impacting events, including operational issues such as a broken checkout button or internal process interruptions, business issues such as rapid consumer behavior changes, or even Internet infrastructure issues, as quickly as possible. We’ll refer to this subset of retail analytics as Retail Order Analytics. Below, we’ll explain how automated machine learning has proven itself to be the preferred technology for companies who want to stay competitive in today’s eCommerce landscape, and we’ll highlight some of the key mathematical challenges involved in accurately analyzing retail operations data to detect revenue-impacting events. Below, we share seven reasons to deploy retail order analytics and some key factors you should consider before deciding on any retail analytics solution:

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