This methodology assesses statistical anomalies within active network connections. It analyzes behavioral factors such as: Rapid changes in destination source addresses Suspicious JavaScript obfuscation techniques
AutoShun is ideal for:
This methodology cross-references indicators against known byte sequences or exact historical file structures. While highly precise and reliable for established threats, signature-based techniques are notoriously limited when encountering polymorphic malware that frequently alters its digital behavior to bypass static file matches. 2. Behavior-Based and Contextual Detection autoshun
The value proposition is simple: why wait for an attacker to hit your perimeter when you can block them based on their behavior elsewhere? Security teams use this actionable data to feed
By collecting raw threat indicators from across the public web and private honeynets, AutoShun processes incoming attacks into a clear, machine-readable format. Security teams use this actionable data to feed perimeter security defenses like Firewalls and Intrusion Prevention Systems (IPS). The Architecture of Automated Malware Detection autoshun