The rule 'Spike in Network Traffic' is designed to detect anomalous surges in network traffic using machine learning. It leverages a specific machine learning job identified as 'high_count_network_events' to establish a baseline for normal traffic patterns and identifies spikes that deviate significantly from this baseline, triggered by an anomaly threshold of 75. Such spikes could suggest malicious activities including data breaches, denial-of-service attacks, or other forms of network abuse, as they can lead to extensive or unexpected data transfers. The rule is configured to analyze traffic over a 30-minute period with alerts generated every 15 minutes. It includes provisions for false positives, such as legitimate business activities causing sudden traffic increases or misconfigurations in network applications. To function correctly, this rule requires the prior installation of machine learning jobs alongside integrations with Elastic Defend or Network Packet Capture systems. The setup process is detailed in the documentation, ensuring that organizations can prepare their systems to deploy this detection capability effectively. Additionally, the rule includes extensive guidelines for triage and analysis, suggesting steps for responding to potential alerts and validating any detected spikes against normal operational activities. An emphasis is placed on evaluating traffic source and destination, protocol types, and the timing of anomalies against known business workflows to distinguish between genuine threats and benign traffic fluctuations.