## 5 Results and Discussion
### RQ3: What Emerging Technologies Address Cyber-Attacks in the MSCN?
#### 5.3.2 Future Trends in Maritime Cybersecurity  
As maritime cybersecurity evolves, generative AI and large language models (LLMs) are emerging as transformative technologies capable of addressing various cyber-attacks, from social engineering to data integrity threats. Unlike traditional Industry 4.0 technologies, which focus on improving operational efficiency, generative AI and LLMs offer new capabilities for automating attack detection, generating adaptive defenses, and responding to increasingly sophisticated threats.  
For example, authors of ref. [142] discusses the role of LLMs in creating adaptive cybersecurity systems that can detect anomalous traffic patterns and mitigate DoS/DDoS attacks in real-time. By leveraging the pattern recognition and reasoning capabilities of LLMs, these systems can identify subtle variations in network activity, enabling them to prevent service disruptions more effectively than traditional methods. Additionally, in ref. [143], the authors demonstrate how model-based attack detection and mitigation techniques can be applied to protect against data integrity attacks such as malware and  
Figure 8: Industry 4.0 technologies identified in the literature review.  
ransomware. These AI-driven models allow for early detection and intervention, and when integrated into vessel and port operations, they offer the potential for real-time threat detection, significantly improving the speed and accuracy of defensive measures.  
In the context of autonomous vessels and unmanned surface vehicles (USVs), generative AI is opening new possibilities for secure navigation and monitoring. The authors of ref. [144] highlight how machine learning methods, specifically generative adversarial imitation learning (GAIL), can be applied to prevent spoofing, jamming, and hijacking attacks. By learning from expert navigation patterns, these AI-driven systems can adapt to changing environments and detect unusual behavior in real-time, enhancing the security of autonomous maritime operations. Furthermore, authors of ref. [145] explores the application of generative AI and ship design, showing how early integration of risk assessments and security features during the design phase can reduce vulnerabilities. This approach improves the overall resilience of vessels by embedding cybersecurity measures directly into the design process, offering long-term protection against potential threats.  
Finally, generative AI also plays a pivotal role in defending against social engineering attacks, such as phishing and spear phishing. The authors of ref. [146] examine how AI-generated content can be exploited to create highly convincing phishing campaigns, increasing the likelihood of maritime personnel being deceived. However, these same technologies can also be used defensively. By analyzing communication patterns and identifying deceptive content in real-time, natural language processing (NLP) models can detect phishing attempts and prevent identity fraud. The authors of ref. [147] further support this by demonstrating how generative AI can mimic human communication styles, making phishing attacks more difficult to detect. In response, NLP models trained in malicious communication patterns can serve as critical tools for protecting the MSCN from increasingly sophisticated social engineering attacks.