iCOP:live forensics to reveal previously unknown criminal media on P2P networks

The increasing levels of criminal media being shared in peer-to-peer (P2P) networks pose a significant challenge to law enforcement agencies. One of the main priorities for P2P investigators is to identify cases where a user is actively engaged in the production of child sexual abuse (CSA) media – they can be indicators of recent or on-going child abuse. Although a number of P2P monitoring tools exist to detect paedophile activity in such networks, they typically rely on hash value databases of known CSA media. As a result, these tools are not able to adequately triage the thousands of results they retrieve, nor can they identify new child abuse media that are being released on to a network. In this paper, we present a new intelligent forensics approach that incorporates the advantages of artificial intelligence and machine learning theory to automatically flag new/previously unseen CSA media to investigators. Additionally, the research was extensively discussed with law enforcement cybercrime specialists from different European countries and Interpol. The approach has been implemented into the iCOP toolkit, a software package that is designed to perform live forensic analysis on a P2P network environment. In addition, the system offers secondary features, such as showing on-line sharers of known CSA files and the ability to see other files shared by the same GUID or other IP addresses used by the same P2P client. Finally, our evaluation on real CSA case data shows high degrees of accuracy, while hands-on trials with law enforcement officers demonstrate the toolkit’s complementarity to extant investigative workflows

Disambiguation of Residential Wired and Wireless Access in a Forensic Setting

Abstract—Thousands of cases each year of child exploitation on P2P file sharing networks lead from an IP address to a home. A first step upon execution of a search warrant is to determine if the home’s open Wi-Fi or the closed wired Ethernet was used for trafficking; in the latter case, a resident user is more likely to be the responsible party. We propose methods that use remotely measured traffic to disambiguate wired and wireless residential medium access. Our practical techniques work across the Internet by estimating the perflow distribution of inter-arrival times for different home access network types. We observe that the change of inter-arrival time distribution is subject to several residential factors, including differences between OS network stacks, and cable network mechanisms. We propose a model to explain the observed patterns of inter-arrival times, and we study the ability of supervised learning classifiers to differentiate between wired and wireless access based on these remote traffic measurements. I