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<rss version="2.0"><channel><title>Peakhour.IO - Anomaly Detection</title><link>https://www.peakhour.io/</link><description></description><lastBuildDate>Fri, 10 Nov 2023 00:00:00 +1100</lastBuildDate><item><title>Dive into CVSS Scores</title><link>https://www.peakhour.io/blog/confluence-cvss-vectors/</link><description>&lt;p&gt;Understand CVSS by examining the Atlassian CVE-2023-22515 and CVE-2023-22518.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Fri, 10 Nov 2023 00:00:00 +1100</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-11-10:/blog/confluence-cvss-vectors/</guid><category>Interest</category><category>Threat Detection</category><category>DevSecOps</category><category>Application Security</category><category>Anomaly Detection</category><category>Credential Stuffing</category><category>Core Web Vitals</category></item><item><title>A Risk Based Approach To Vulnerability Scoring</title><link>https://www.peakhour.io/blog/epss-explained/</link><description>&lt;p&gt;An in-depth exploration of EPSS, its data-driven approach to assessing cybersecurity threats, and how it complements CVSS.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Fri, 10 Nov 2023 00:00:00 +1100</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-11-10:/blog/epss-explained/</guid><category>Interest</category><category>Threat Detection</category><category>Application Security</category><category>DevSecOps</category><category>Anomaly Detection</category><category>DDoS</category><category>Credential Stuffing</category></item><item><title>When Bots Break Bad</title><link>https://www.peakhour.io/blog/when-good-bots-break-bad/</link><description>&lt;p&gt;Even 'good' bots can end up abusing your site and impacting performance, learn why and how to stop it.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">Dan</dc:creator><pubDate>Tue, 16 May 2023 13:00:00 +1000</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-05-16:/blog/when-good-bots-break-bad/</guid><category>Bots</category><category>Bot Management</category><category>SEO</category><category>Residential Proxies</category><category>DNS</category><category>Web Performance</category><category>Anomaly Detection</category></item><item><title>Advanced Anomaly Detection</title><link>https://www.peakhour.io/blog/advanced-anomaly-detection-rrcf-application-security/</link><description>&lt;p&gt;Deep dive into Robust Random Cut Forest (RRCF) implementation for real-time anomaly detection in Application Security Platforms. Learn how advanced machine learning algorithms enhance threat detection and automated response capabilities.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Mon, 15 May 2023 13:00:00 +1000</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-05-15:/blog/advanced-anomaly-detection-rrcf-application-security/</guid><category>Security</category><category>Threat Detection</category><category>Anomaly Detection</category><category>DDoS</category><category>DevSecOps</category><category>Bot Management</category><category>Application Security</category></item><item><title>Double MAD?</title><link>https://www.peakhour.io/blog/double-mad/</link><description>&lt;p&gt;This article explores the use of Double Median Absolute Deviation (Double MAD) for anomaly detection in time series data, particularly in skewed or non-symmetric distributions.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Mon, 15 May 2023 13:00:00 +1000</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-05-15:/blog/double-mad/</guid><category>Technical</category><category>Anomaly Detection</category><category>Threat Detection</category><category>Bot Management</category><category>Residential Proxies</category><category>DDoS</category></item><item><title>Double MAD vs the Rest</title><link>https://www.peakhour.io/blog/double-mad-vs-zscore/</link><description>&lt;p&gt;A look at the limitations of Double MAD for anomaly detection, and a comparison with the Z-score method, to help you choose the right approach for your data.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Mon, 15 May 2023 13:00:00 +1000</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-05-15:/blog/double-mad-vs-zscore/</guid><category>Technical</category><category>Anomaly Detection</category></item><item><title>Scaling anomaly detection with RRCF</title><link>https://www.peakhour.io/blog/rrcf-scaling/</link><description>&lt;p&gt;Discusses strategies for scaling the Robust Random Cut Forest (RRCF) algorithm for large-scale anomaly detection, including using summary statistics, buffering input, and parallelisation.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Mon, 15 May 2023 13:00:00 +1000</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-05-15:/blog/rrcf-scaling/</guid><category>Technical</category><category>Anomaly Detection</category><category>Threat Detection</category></item><item><title>Applied RRCF - thresholding techniques.</title><link>https://www.peakhour.io/blog/rrcf-thresholding/</link><description>&lt;p&gt;Explores various thresholding techniques like Median Absolute Deviation (MAD), Min/Max, and Z-Score for interpreting Robust Random Cut Forest (RRCF) anomaly scores, crucial for classifying data points as normal or anomalous.&lt;/p&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">AC</dc:creator><pubDate>Mon, 15 May 2023 13:00:00 +1000</pubDate><guid isPermaLink="false">tag:www.peakhour.io,2023-05-15:/blog/rrcf-thresholding/</guid><category>Technical</category><category>Anomaly Detection</category><category>Threat Detection</category></item></channel></rss>