As technology evolves, so does the way we protect our digital lives. One of the latest ways to combat cybercrime is natural language processing (NLP), which uses machine learning to interpret human language.
The Hug Project is an excellent example by Cox Communications to integrate this approach to connect people that were physically apart. Cox internet plans are customized to suit nationwide household needs for internet connectivity.
Natural language processing (NLP) is the technology behind chatbots, predictive text that finishes your thought in an email, or pressing “0” to talk to an operator.
The sub-field of Natural Language Processing is evolving beyond the famous use cases of machine-human communication to interpret human and non-human speech.
The goal of NLP is to support machine learning of human language by combining linguistics, computer science, and artificial intelligence. The complexity of human language renders machines unable to understand it if they rely on structured rules.
Machines can learn and contextualize using NLP rather than rigid encoding to adapt to different dialects, new expressions, or questions that programmers hadn’t anticipated.
The research on NLP contributed to the development of AI-like neural networks that are instrumental to machine learning across various fields and use cases. Traditionally, NLP has been used to simplify the interaction between humans and machines, such as between enterprises and consumers.
NLP for Cybersecurity
The goal of NLP is to enable machines to learn to communicate with humans as if they were humans. In today’s world, we rely on machine communications to communicate with each other or translate data into something we can understand.
Cybersecurity is an excellent example of a field in which IT analysts can feel like they talk to more machines than to people.
Cybersecurity workflows can benefit from natural language processing to protect against breaches, identity thefts, and determine their scale and scope. Here are three ways NLP is changing Cybersecurity:
- Breach Protection
By leveraging NLP, you can increase and simplify phishing breach protection in the short term.
NLP can understand “bot” or “spam” behavior in email text sent through Cox internet plans service by a machine posing as a human and examine the internal structure of the email itself to identify spam patterns and the types of messages spammers send.
NLP was initially developed to understand human language. It has been now extended to understanding human language mixed with machine-level headers using this example.
- Parse Logs
The use of NLP for parsing logs can be leveraged in the medium term. Currently, existing rules-based systems have brittle mechanisms and systems that rely on significant development and maintenance resources to parse raw logs and prepare them for analysts.
NLP enhances the flexibility of parsing raw logs and lessens the likelihood of broken parsing when the log generators and sensors are changed.
In addition, neural networks for parsing can generalize beyond the logs they were exposed to during training, allowing analysts to extract rich content from raw data without writing custom rules for new or changed log types.
Consequently, NLP models are superior to traditional rules for parsing log data, yet more flexible and fault-tolerant.
- Integrated Communication
An integrated machine-to-machine and human-to-machine communication can be achieved through entirely synthetic languages in the long term. For example, suppose two machines can create an entirely new language. Then, the language can be analyzed with NLP techniques to find grammar, syntax, and composition errors that can be interpreted as anomalies.
New developments can help identify known issues or attacks and identify completely unknown misconfigurations and attacks, allowing analysts to be more efficient. The possibilities are endless with NLP.
Integrated risk management solutions allow organizations to use their vulnerability information in various ways. However, it usually involves multiple siloed products, resulting in information that is difficult to comprehend, navigate and maintain.
AI handles this issue and can harmonize across various frameworks and standards. As well as mapping multiple control actions to a specific control, Cybersecurity will soon be able to verify compliance requirements across other controls and frameworks. Additionally, continuous NLP training facilitates proper harmonization in assessment across frameworks.
The NLP engine matches keywords in telemetry to specific controls and control actions, automating the cross walking process in a way not yet seen in the industry. There are currently many cybersecurity solutions that crosswalk manually and inexactly.
Natural language processing (NLP) has changed Cybersecurity. NLP allows computers to recognize threats and vulnerabilities in real-time by understanding human speech and text.This has made it possible for cyber security teams to respond more quickly and effectively to potential threats. For example, Windstream internet lets you stream unlimited data at affordable prices without any data caps.