Abstract--Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. T o fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases. Future networks, such as Sixth Generation (6G) networks, are envisioned to integrate Artificial Intelligence (AI) into their networks to be AI-Native networks [1] to improve performance, efficiency, and scalability [2].

A model set of opportunities and obstacles have emerged with the advent of the digital age. Cyber security testing has become increasingly important as organizations look to protect their networks, data, and systems from malicious attacks. As technology continues to evolve and become more sophisticated, artificial intelligence (AI) is tapped up as a way to improve cybersecurity testing. Although, AI has the potential to significantly reduce the time and effort needed to find and fix vulnerabilities, as well as to detect and respond to threats more quickly. AI can also help to identify patterns and uncover hidden threats that may have gone undetected.

As ChatGPT continues to make headlines in the news and tech blogs everywhere, I wanted to try it out for myself to see how I can use it in my own life. What better than to utilize it to write a new blog post summarizing an AWS service announced at re:Invent? I first went to ChatGPT with a simple question about summarizing the new service Amazon Security Lake. The result I got was a well written blog post summarizing all of it's capabilities. Amazon Security Lake is a new service from Amazon Web Services (AWS) that provides a central repository for storing, analyzing, and managing security data at scale.

Banking and investing in the years to come are unlikely to resemble what they looked like or how they worked for our grandparents. An enormous amount of data and a high rate of digitization continue to transform the landscape of the financial services sector and how customers engage with it. Ubiquitous and vast varieties of data, combined with the sector's reliance on technology, pose new challenges, threats and vulnerabilities. The newly formed industry-guided research center, the Center for Research toward Advancing Financial Technologies (CRAFT), funded by the National Science Foundation's Industry-University Cooperative Research Center Program, is setting out to leverage the opportunities for advancement and innovation in the industry and to address the many threats and vulnerabilities it faces. At a launch event for CRAFT in the fall of 2021, industry leaders discussed many examples of such pressing challenges and research opportunities.

Two terms which Information Security relies on, in order to face many of the current challenges. Recent reports prove that a high number of security professionals are convinced that Machine Learning enhances their ability to prioritize threats and vulnerabilities and increases the productivity of security personnel. The challenges today for cybersecurity have a broad range, from the intelligence and threat research phase to the Incident Response. Many security professionals are struggling from insufficient resources to keep current on new threats and vulnerabilities. Also, when they set their defending mechanisms, it's difficult not το get overwhelmed by the vast number of alerts when the Security Incident and Event Management (SIEM) mechanism is not tuned and optimized.

As digital technology progresses, there has been a growing number of cyberattacks in the last couple of years. By the first half of 2019, 4.1 billion records were exposed due to data breaches. That number is multiplying every day as more people and businesses move online. Cyberattacks are critical for every organization as they can bring the entire organization to a standstill. In such scenarios, security is a necessity for all organizations.

As we hand data and control over to the machines for autonomous driving, even if Artificial Intelligence (AI) is still rudimentary, how can businesses maintain proper security controls, asks Andrew Foxcroft, regional director, Radware? Apple recently found itself in the shoes of many other organisations when it discovered that a former employee, working in the tech giant's autonomous vehicle development team, had stolen trade secrets. The hardware engineer planned to take the data and join an intelligent electric vehicle start-up, but his plot was foiled after internal investigators noticed a spike in his network activity. Cases like this are common – in fact, some 30% of CXOs say that insider threats put their companies' security and IP at risk. The act of stealing data shows the competitive nature of being first to launch new technology.