Summer Observations

This summer, I had the opportunity to engage in fascinating cybersecurity research. This has prompted me to dive deeper into the field and explore how natural language processing (NLP) can enhance it.

The first main application of NLP to the field is identifying phishing emails. These emails are particularly dangerous because they can trick users into revealing sensitive information or downloading malicious software. Hence, filtering them out before users can even access them is very important, and NLP models happen to be great at this! They can check for suspicious keywords, spelling mistakes and other surface-level irregularities and use these text classification features to determine whether an email should be considered “phishing”. Furthermore, the models are not limited to searching for smaller red flags across individual words. NLP can be used to identify an email’s broader intent and emotion through sentiment analysis, which, for example, allows models to observe whether the email’s tone matches the common tone of fear or urgency of phishing emails. Named Entity Recognition is also a useful NLP tool, as it focuses on pinpointing the names of companies, individuals, and locations, helping identify impersonation attempts that may exist within these emails. Therefore, when an NLP model combines these different methods and adapts over time based on the phishing strategies it encounters, it becomes highly effective at reducing the number of phishing emails that bypass spam filters and, in turn, the harm they can cause.

Next, NLP is very helpful for analyzing logs. Logs are digital records of system activity that allow security teams to understand system errors and investigate attacks. However, in their raw form, these logs are usually voluminous and unstructured. While traditional methods of parsing logs rely on fixed rules, these do not work well when log formats change, unless the rules are consistently updated as well. Instead, NLP-based models, like CyBERT, treat logs as language and can identify unusual behavior even when the format differs by relying on deeper contextual understanding and pattern generalization. This reduces manual maintenance and enables security teams to gain insights more clearly and quickly.

Lastly, NLP can improve the efficiency and accuracy of threat intelligence. There is an overwhelming number of threat intelligence sources available on the Internet, from blogs to dark web forums and even social media. Although these are all very valuable for providing insight into current and future cyberthreats, the sheer volume of data and the fact that many sources are in languages other than English make it difficult for human analysts to interpret the information optimally. Hence, NLP algorithms can help by scanning sources, extracting patterns, drawing connections between attacks, and noting other relevant information about emerging threats. These threats can even be categorized by severity or other common themes, which allows cybersecurity teams to prioritize their response efforts and reduce the risk of overlooking potential harm. 

Overall, it is clear that natural language processing has numerous applications in cybersecurity. While it is not a perfect solution for tackling all cyberthreats, the intersection of NLP and cybersecurity is an area of research that will only continue to grow as machine learning models and artificial intelligence capabilities become more complex.

Credit: M K Pavan Kumar (Medium)

Works Cited

Hensley, MaKenna. “The Role of Natural Language Processing in Detecting Phishing Emails.” LinuxSecurity, 8 Aug. 2025, linuxsecurity.com/news/server-security/nlp-phishing-detection. Accessed 31 Aug. 2025.

Jeff. “NLP in Cybersecurity: Contextual Threat Analysis.” The Security Bulldog, 10 July 2025, securitybulldog.com/blog/nlp-in-cybersecurity-contextual-threat-analysis/. Accessed 31 Aug. 2025.

Kuriakose, Anil Abraham. “Leveraging Natural Language Processing (NLP) for Cyber Threat Analysis in MDR.” Algomox, 2 Jan. 2025, www.algomox.com/resources/blog/leveraging_nlp_cyber_threat_analysis_mdr/. Accessed 31 Aug. 2025.

Richardson, Bartley. “Changing Cybersecurity with Natural Language Processing.” NVIDIA Developer Technical Blog, 19 Oct. 2022, developer.nvidia.com/blog/changing-cybersecurity-with-natural-language-processing/. Accessed 31 Aug. 2025.