In the digital age, organised crime groups increasingly leverage advanced technologies to enhance their operations and evade detection. They use end-to-end encryption to secure communications, posing challenges for law enforcement in intercepting and analysing these messages. However, intelligence agencies can employ strategic techniques like metadata extraction and analysis, known as 'PCAP analysis.' This method involves examining metadata and traffic patterns of IP communications, allowing agencies to gain insights into connections between multiple suspects, their activities and patterns-of-life without decrypting the actual content.
Understanding PCAP Data
PCAP (Packet Capture) data is a critical tool in criminal investigations, capturing raw network traffic to provide a detailed snapshot of communication between devices. This data is essential for evidence collection, and suspects’ communication analysis allowing investigators to reconstruct network events and detect suspicious activities. PCAP data can be accessed via lawful interception by law enforcement agencies or through packet sniffers like Wireshark. Its integration with other data sources offers a comprehensive intelligence picture, making it invaluable for security agencies in understanding and analysing suspects' activities. Read more about it in our blog: (we will link the other blog here)
Deep Packet Inspection (DPI) in PCAP Analysis
Deep Packet Inspection is an advanced method of examining the data packets that make up network traffic. Unlike traditional packet filtering, which only examines packet headers, DPI inspects both the headers and the data payloads of packets. This allows for a more comprehensive understanding of the traffic, enabling the identification of specific applications or devices that generated the traffic.
How DPI Works
Existing challenges in analysing PCAP data
PCAP files from high-speed networks are often large, necessitating substantial storage and computational resources, which can strain existing technical infrastructure.
Drawing useful information from PCAP data requires specialised tools and expertise. Analysts must also have a good understanding of network protocols and packet analysis techniques.
The growing prevalence of encryption in network traffic is a major obstacle as encrypted content within communications cannot be accessed.
How AI is useful: Some powerful use cases
Artificial intelligence (AI) is crucial in processing the extensive data captured through PCAP, identifying patterns, detecting anomalies, and highlighting suspicious activities in network traffic, which aids law enforcement in tackling sophisticated crimes.
AI excels at recognising patterns by mining historical records to detect criminal behaviours. This enables investigators to focus on suspicious activities and respond proactively.
AI identifies abnormalities or deviations from normal behaviour, such as sudden spikes in data transfers, which could indicate data theft or other malicious actions. This helps investigators concentrate on promising lines of inquiry.
AI analyses complex financial transactions to reveal suspicious patterns and associations, such as unusual transaction sequences or constant transfers between unrelated accounts. These insights help identify and disrupt financial networks supporting organised crime or terrorism.
Conclusion
AI will revolutionise high-tech criminal investigations by enabling law enforcement agencies to identify patterns, inconsistencies, and malicious financial transactions hidden within the volumes of data contained in PCAP files. By leveraging AI and its capabilities, analysts can increase the speed, precision and relevance of network traffic analysis during their investigations.