Extracting Suspicious IP Addresses from WhatsApp Network Traffic in Cybercrime Investigations
Da‐Yu Kao, En-Cih Chang, Fu-Ching Tsai · 2019
Sniffers are among the commonest approaches for capturing network traffic activities and collecting digital evidences in cybercrime investigations. The ubiquity of instant messaging (IM) apps on smartphones has provided criminals with communication channels that are difficult to decode. Moreover, investigators and analysts of cybercrimes are encountering increasingly large datasets. To combat criminal activity, law enforcement agencies (LEAs) often rely on call-record analysis. In this paper, cybercriminals are investigated by network forensics and sniffing techniques. Retrieving valuable information from specific IM apps is difficult because the criminal's IP address records are not easily recognisable on the Internet. Here, a criminal's identity is located more effectively by a packet filter framework that isolates the WhatsApp communication features from huge collections of network packets. A rule extraction method for sniffing packets is proposed that retrieves the relevant attributes from high-dimensional analysis based on geolocation and a pivot table. The utility of this methodology is illustrated on real-time network forensics and a lawful interception system in Taiwan. The methodology also meets the ISO/IEC 27043:2015 standards of fear, uncertainty, and doubt avoidance. Besides supporting LEAs in discovering criminal communication payloads, prosecuting cybercriminals and bringing them to justice, it improves the effectiveness of modern call-record analysis.