akamai/luda

Malicious actors often reuse code to deploy their malware, phishing website or CNC server. As a result, similiaries can be found on URLs path by inspecting internet traffic. Moreover, deep learning models or even regular ML model do not fit for inline deployment in terms of running performance. However, regexes ( or YARA rules ) can be deployed on a proxy and work in real time on all the traffic. LUDA can take a set of malicious and benign URLs and return a list of regexes ready to be deployed inline !

44
/ 100
Emerging

LUDA helps network security professionals and incident responders automatically create detection rules for malicious URLs. It takes a collection of known malicious and benign URLs as input, analyzes their patterns, and outputs highly effective regular expressions (regexes) that can be instantly deployed on network proxies to block threats in real-time. This is ideal for security teams seeking to automate the creation of high-performance URL filtering rules.

No commits in the last 6 months.

Use this if you need to generate precise, high-performance regex rules from lists of malicious URLs for real-time threat detection on network proxies.

Not ideal if you're looking for a general-purpose machine learning model for threat detection that doesn't require immediate, inline deployment via regexes.

network-security threat-detection phishing-prevention malware-analysis incident-response
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 19 / 25

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Stars

75

Forks

17

Language

Python

License

GPL-3.0

Last pushed

Oct 30, 2023

Commits (30d)

0

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