# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 3 Large Language Models for Networking
### Network Management  
Network management involves the continuous monitoring, control, and optimization of network performance, security, and reliability. LLMs provide advanced capabilities for automating and enhancing these tasks. Their sophisticated language processing power allows for improved operational efficiency, proactive security measures, and dynamic adjustments to network conditions, ultimately leading to more resilient and adaptive network systems. For cybersecurity, Cyber Sentinel in [171] is a task-oriented cybersecurity dialogue system leveraging chained GPT-4 models and prompt engineering to explain potential threats and take proactive security actions, enhancing transparency and decision-making in network management. SecureBERT in [162] is a cybersecurity language model trained on a large corpus of cybersecurity text to automate critical tasks in Cyber Threat Intelligence by capturing text connotations and transforming natural language text into machine-readable formats. The work in [170] evaluates the effectiveness of LLMs in solving cybersecurity Capture-The-Flag challenges. CyBERT [160], a domain-specific BERT model fine-tuned with a large corpus of cybersecurity data, is designed to enhance the performance of various cybersecurity-specific downstream tasks for Security Operations Centers by processing dense, fine-grained textual threat, attack, and vulnerability information. The work in [172] explores the use of LLMs in automating cyber campaigns, presenting a framework for a plan-act-report loop and prompt chaining to direct sequential decision processes in threat campaigns, highlighting the potential and ethical implications of LLMs in enhancing threat actor capabilities in network management. The study in [176] introduces a GPT-2-based approach to enhance network threat detection, aiming to improve the efficiency and accuracy of identifying and issuing early warnings for network threats. For malware detection, the work in [161] proposes using a BERT-based transformer architecture for automatically detecting and classifying malicious software in Android applications through static analysis of preprocessed source code features. The study in [163] proposes using LLMs for detecting malicious code by analyzing assembly instructions from Portable Executable files, evaluating their effectiveness in network management. For intrusion detection, HuntGPT [175] leverages the GPT-3.5 model as a conversational agent to enhance the explainability and user-friendliness of threat detection in network management. For specific types of cyberattacks, research includes the introduction of a novel Man-in-the-Middle (MITM) attack scheme on Internet of Things (IoT) devices using the MQTT protocol, featuring an MQTT Parser and a BERT-based adversarial model to generate malicious messages, effectively evading various anomaly detection mechanisms in network management [159]. Net-GPT in [173] is an LLM-empowered offensive chatbot designed to launch Unmanned Aerial Vehicle (UAV)-based MITM attacks by mimicking network packets between UAVs and Ground Control Stations, leveraging fine-tuned LLMs on an edge server to enhance predictive accuracy and adaptability in network management. For Distributed Denial of Service (DDoS) attack, DoLLM in [180] utilizes open-source LLMs to analyze non-contextual network flows by projecting them into a semantic space of LLMs, enhancing the detection of Carpet Bombing DDoS attacks. ShieldGPT in [177]is a comprehensive DDoS mitigation framework that leverages LLMs to detect attacks, represent network traffic, inject domain knowledge, and provide actionable mitigation measures through tailored prompt engineering and representation schemes. Security-BERT [178], a novel architecture utilizing the BERT model for cyber threat detection in IoT networks, leverages a privacy-preserving encoding technique to achieve high accuracy and efficiency in identifying various attack types, making it suitable for deployment on resource-constrained IoT devices.