AI 네트워크 방어 게임 (Cyber Shield) 3 by A to Z 영웅 (사이드 사이드 프로젝트)

 

1) abstract

In recent years, the advent of multiple AI technologies, particularly those developed by OpenAI, has profoundly impacted the global landscape. On May 13, 2024, the release of ChatGPT-4o marked a significant milestone[1], capturing worldwide attention. Amid this rapid evolution in AI, current AI adaptation in cybersecurity largely focus on narrow tasks such as analyzing packets or images, or generating content. However, this limited scope raises the question: why restrict AI to passive analysis when it can be empowered to autonomously react to the data it processes? This research explores the implementation of an AI system that not only analyzes network packets using a K-Nearest Neighbors (KNN) model but also leverages the OpenAI API to autonomously respond to detected anomalies. The KNN model processes incoming data to classify it, while the OpenAI API dynamically generates appropriate responses to potential threats, enhancing the system's capacity to autonomously mitigate security risks. This approach exemplifies a paradigm shift from passive analysis to active, AI-driven intervention, broadening the scope of AI applications in cybersecurity and other fields.

 

 

2) introduction

The exponential expansion of data and the proliferation of networked systems in the modern digital age have brought with them previously unknown opportunities and difficulties[2]. Cybersecurity, a critical concern for individuals, organizations, and governments alike, has become increasingly complex and demanding. Traditional security measures, reliant on predefined rules and manual oversight, are often insufficient to address the sophisticated and evolving nature of cyber threats[3]. This inadequacy has prompted the integration of artificial intelligence (AI) into cybersecurity strategies, leveraging its capabilities to enhance threat detection and response mechanisms.

 

Artificial intelligence, particularly machine learning (ML) models, has been instrumental in identifying patterns and anomalies within large datasets, such as network traffic. These models, trained on historical data, can effectively detect deviations indicative of potential security breaches. Despite these advancements, the application of AI in cybersecurity has predominantly focused on passive analysis—monitoring and identifying threats without engaging in autonomous, proactive intervention. This passive approach, while useful, falls short of leveraging the full potential of AI to dynamically react to threats in real-time.

 

The concept of autonomous AI-driven cybersecurity envisions a system capable of not only detecting anomalies but also taking immediate, contextually appropriate actions to mitigate identified threats. Such a system would transform AI from a passive observer into an active participant in the cybersecurity landscape. This research aims to explore the feasibility and effectiveness of an AI system that integrates a K-Nearest Neighbors (KNN) model for packet analysis with an advanced AI framework to autonomously generate and execute responses to security threats.

 

By analyzing network packets using the KNN model, the system classifies incoming data to identify potential anomalies. Upon detection of a threat, the system leverages the capabilities of an AI framework to dynamically determine and execute the appropriate countermeasures. This approach not only enhances the speed and efficacy of threat response but also reduces the reliance on human intervention, thereby minimizing response times and potential errors.

 

This study seeks to demonstrate the practical implementation of such a system, evaluating its performance in real-world scenarios and assessing its impact on overall network security. By advancing the role of AI in cybersecurity from passive analysis to active intervention, this research contributes to a broader understanding of how autonomous AI systems can be harnessed to fortify digital defenses against ever-evolving cyber threats.