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The Use of Natural Language Processing for Identifying and Mitigating Threats
As technology advances, the battle between cyber criminals and organizations intensifies. Cyber threats have become more sophisticated, complex, and widespread, posing a significant risk to the security and integrity of sensitive data.
In Q1 2023 alone, the number of global cyber attacks increased by 7%, with an average of 1,248 attacks reported per week. In a separate report by The Independent Advisor, 350 million potential records were compromised in notable data breaches in the first five months of 2023 alone. These numbers are staggering. Yet, organizations are not ignoring these findings.
When faced with the likelihood of losing up to $4.35m to a single data breach, organizations are hardly left with any choice but to take data security seriously. Several innovative strategies and technologies are emerging to effectively detect, identify, and mitigate these threats. In cyber threat intelligence, Natural Language Processing (NLP), which seeks to identify and analyse the motives and operations of threat actors, has emerged as a powerful tool for fighting back against cyber attacks.
Natural Language Processing and Cyber Threats
According to the latest estimates, over 300 million terabytes of data are created every day, and these numbers are increasing by the year as we witness more technological advancements. Ever since the proclamation that “The world’s most valuable resource is no longer oil, but data”, cyber attackers have been unrelenting in their attempts to seize this data for nefarious purposes.
Much of this data existing on the cloud consists of text, often unstructured. This has made artificial intelligence and machine learning a significant part of not just how the data are processed but also how they are protected. Given NLP’s primary focus on extracting valuable insights from text data, there is a lot of potential in this field for mitigating cyber threats.
Threat Intelligence Gathering and Analysis
One of the first ways organizations can apply NLP in addressing cyber threats is to automate the analysis of security reports as well as other details retrievable on the internet.
NLP techniques such as text classification, sentiment analysis, and entity recognition can then be used to extract valuable insights and identify potential threats. This has become necessary due to the vast amounts of information that security experts and researchers have to wade through in addressing cyber threats.
Threat Detection and Monitoring
It’s one thing to analyse security reports, but it is another to stop them right as they are happening. Where does NLP come in when it comes to real-time threat detection?
If you consider the case of insider threats, where there is often a trail in communication as a precursor to malware injection, such as an email with a malicious code or link, it’s not hard to determine how NLP systems may be used to detect anomalous patterns in textual data being exchanged. This also includes phishing emails, traffic logs, and event data.
In fact, by training OpenAI’s GPT on malicious network traffic, various university researchers were able to develop modified versions of the language learning model that could detect threats with incredible accuracy. Many technology experts are optimistic about the potential of Large Language Models (LLM), so it remains to be seen how much more can be achieved with these systems.
Incident Response
Even if NLP could be used to gather information and detect active threats, could it also be used to stop full-blown attacks? The answer is a categorical yes, as the following example demonstrates. One of the major challenges with responding to cybersecurity incidents in the AI age is the sheer scale of attacks. NLP can improve this significantly by automating and streamlining various aspects of incident handling. Once a threat is detected, an NLP model could launch a clean sweep of all company data to detect malicious information.
Another useful NLP response is in the categorization of security incidents and record-keeping. Cybersecurity handlers often need to organize incidents by severity, impact, relevance, and other means. If an NLP system is deployed to undertake tasks such as analyzing incident reports, email notifications, system logs, and automatically assigning appropriate priority levels, the human handlers can focus squarely on removing the threat from the system or network.
Challenges of NLP in Cyber Threat Intelligence
For all its good, it is important to note that NLP models (just like other AI systems) derive their intelligence from the training data with which they have been fed. Cybercriminals will not stop trying to compromise a system by bypassing detection systems. Hence, NLP models need to be updated regularly to stay ahead of the latest threats and even zero-day attacks.
More so, it is important to note that these threat actors have started using NLP systems and chatbots such as ChatGPT to launch attacks. For instance, attackers could generate human-like text to write several phishing emails all at once. Or they could even launch their own chatbots that persuasively trick users into exposing their personal information.
Organisations need to reinforce their cybersecurity education for employees with this knowledge in order to raise their guard. More so, organisations that build LLMs and other NLP models need to responsibly and ethically handle personal and sensitive data to protect users.
Use Cases
Various companies have attempted to introduce new LLM-powered generative AI systems to address the challenges of threat intelligence, such as threat overload, toilsome tools, and the talent gap. For instance, cybersecurity professionals can enhance threat detection and observe anomalous patterns and even masked words by using language models that are pre-trained on data from the dark web. Another use case is in parsing cybersecurity logs in order to make it easier to identify and respond to cyber threats. Overall, NLP can revolutionize cyber threat intelligence by extending the breadth of intelligence coverage across data sources and over multiple languages (a useful necessity, especially considering the spate of attacks from foreign nations).
Conclusion
NLP is a rapidly evolving field, and new applications for threat detection are being developed all the time. As NLP technology continues to improve, it is likely that it will play an even greater role in protecting organizations from compromises and people from harm.
About the Author:
Michael Usiagwu is an Entrepreneur, Tech Pr Expert and CEO of Visible Links Pro. He assists various organizations to stay abreast of the latest technology. Some of his insightful content can be seen in Readwrite, InfoSecurity Magazine, Hackernoon, and lots more. He’s very much open to assist organizations to increase their latest technology development.
Editor’s Note: The opinions expressed in this guest author article are solely those of the contributor, and do not necessarily reflect those of Tripwire, Inc.
