They must not be overly or too loosely constrained, making it extremely difficult to write hundreds of regexes while keeping an appropriate constraining balance. Writing a set of regexes in the first place is hard.While this was a great initial solution, regexes come with several big challenges: Traditionally, log parsing has been done with regular expression matching, called regexes. In this blog, we’ll walk you through how we use machine learning (ML) to solve log parsing, the experiments we ran in collaboration with NVIDIA to determine how to deploy our models at scale using the NVIDIA Triton Inference Server, and our next steps. Historically, this has been solved using complex sets of rules, but new approaches, combined with increases in computational power, are enabling fast log parsing using neural networks, providing significant parsing advantages. Given the volume (petabytes per day) and value of the data within machine logs, log parsing must be scalable, accurate, and cost efficient. The log parser is extracting the following fields: timestamps, dvc (device number), IP addresses, port numbers, etc. This nice structured data can then be fed into downstream cybersecurity pipelines.įor example, the image below shows the desired input for a log parser (an unstructured log), and its output (a structured map from field name to its value): This transformation is called “log parsing”, where all the different entities, or “fields”, are extracted from unstructured text. We can use artificial intelligence techniques on logs to detect cybersecurity threats within the network, but we must first take the raw, unstructured logs, and transform them into an easily-digestible, structured format. Although some logs are written in a structured format (e.g JSON, XML), many applications write logs in an unstructured, hard-to-digest way. Machine logs are generated by appliances, applications, machinery, and networking equipment, (switches, routers, firewalls, etc.) Every event, along with its information, is sequentially written to a log file containing all of the logs. Log Parsing is the First Step in Cybersecurity ![]() ![]() The complexity of this interconnected ecosystem now requires one to assume that the adversary is already within the network and consequently must be detected there, not just at the perimeter. Sophisticated cyberattacks can easily hide inside this data-centric world, making traditional perimeter-only security models obsolete. A global workforce, combined with the growing need for data, is driving an increasingly distributed and complex attack surface that needs to be protected. Large amounts of data no longer reside within siloed applications.
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