If you’ve used a chatbot, predictive text to complete a thought in an email, or pressed “0” to speak to an operator, you’ve encountered natural language processing (NLP). As more companies embrace NLP, the subfield is expanding beyond these popular use cases of machine-to-human communication towards machines interpreting both human and non-human language. This creates an exciting opportunity for organizations to stay ahead of evolving cybersecurity threats.
NLP combines linguistics, computer science and AI to support machine learning of human language. Human language is surprisingly complex and relying on structured rules leaves machines with an incomplete understanding of it. NLP allows machines to contextualize and learn instead of relying on rigid coding, so they can adapt to different dialects, new expressions, or questions that programmers never anticipated.
NLP research has driven the evolution of AI technology, such as neural networks, which are instrumental in machine learning in various fields and use cases. NLP has primarily been exploited in machine-to-human communication to simplify interactions for businesses and consumers.
NLP for cybersecurity
NLP was designed to allow machines to learn to communicate like humans, with humans. Many services we use today take advantage of automatic communications with each other or in translation to become intelligible to humans. Cybersecurity is the perfect example of an area where IT analysts can feel like they’re talking to more machines than people.
NLP can be used in cybersecurity workflows to help with breach protection, identification, and scale and scope analysis. Here’s how it works:
1) In the short term, NLP can be easily leveraged to improve and simplify breach protection against phishing attempts. In this context, NLP can be exploited to understand the behavior of “bot” or “spam” in the text of an e-mail sent by a machine impersonating a human, and it can be used to understand the internal structure of the email itself to identify patterns of spammers and the types of messages they send. This example is the first extension of NLP, originally designed to understand only human language and now applied to understand the combination of human language mixed with machine-level headers.
2) In the medium term, NLP can be leveraged to analyze logs, a use case for cyBERT. In today’s rules-based system, the mechanisms and systems required to analyze raw logs and prepare them for analysts are fragile and require significant development and maintenance resources. Using NLP, raw log analysis becomes more flexible and less likely to break when changes occur in log generators and sensors. Going one step further, the neural networks used for analysis can generalize beyond the logs they were exposed to during training – creating methods to turn raw data into rich, analyst-ready content without the need for software. write explicit rules for these new or changed log types.
As a result, NLP models are more accurate when analyzing logs than traditional rules while being more flexible and fault tolerant.
3) In the longer term, fully synthetic languages can be created that represent machine-to-machine and human-to-machine communications. If two machines can create an entirely new language, that language can then be analyzed using NLP techniques to identify errors in grammar, syntax, and composition, all of which can be interpreted as anomalies and contextualized for analysts. . This new development can help identify known issues or attacks when they occur, and can also identify misconfigurations and completely unknown attacks, helping analysts be more effective and efficient. These apps are just the beginning for NLP.
To learn more about how natural language processing can be used for cybersecurity, register to attend Build AI-enhanced, Next-generation Cybersecurity Solutions [S41549]one of many free virtual sessions taking place March 21-24 during NVIDIA GTC.
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