Deep Packet Inspection

Deep Packet Inspection (DPI) analyzes network traffic, examining both packet headers and payload content to identify malicious activity and improve network security. Current research emphasizes using advanced machine learning techniques, particularly large language models (LLMs) and transformers, along with few-shot learning approaches to enhance the detection of novel malware and network anomalies, even with limited labeled data. This focus on efficient and adaptable models addresses the challenges posed by increasingly sophisticated cyberattacks and the ever-growing volume of network data, impacting both network security and the broader field of machine learning for network analysis. Furthermore, research is exploring alternative methods like computer vision techniques applied to PCAP data and novel feature engineering for improved accuracy and efficiency.