File System Forensic Analysis
Communications of the ACM
SQL Server Forensic Analysis
Computing in Science and Engineering
Live Analysis: Progress and Challenges
Computing in Science and Engineering
All your contacts are belong to us: automated identity theft attacks on social networks
Proceedings of the 18th international conference on World wide web
Secure provenance: the essential of bread and butter of data forensics in cloud computing
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
A Practical Attack to De-anonymize Social Network Users
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Detecting and characterizing social spam campaigns
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Forenscope: a framework for live forensics
Proceedings of the 26th Annual Computer Security Applications Conference
Friend-in-the-Middle Attacks: Exploiting Social Networking Sites for Spam
IEEE Internet Computing
Technical Issues of Forensic Investigations in Cloud Computing Environments
SADFE '11 Proceedings of the 2011 Sixth IEEE International Workshop on Systematic Approaches to Digital Forensic Engineering
Feature: Forensic investigation of cloud computing systems
Network Security
PyFlag - An advanced network forensic framework
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Appinspect: large-scale evaluation of social networking apps
Proceedings of the first ACM conference on Online social networks
Social engineering attacks on the knowledge worker
Proceedings of the 6th International Conference on Security of Information and Networks
| Hi-index | 0.00 |
Recently, academia and law enforcement alike have shown a strong demand for data that is collected from online social networks. In this work, we present a novel method for harvesting such data from social networking websites. Our approach uses a hybrid system that is based on a custom add-on for social networks in combination with a web crawling component. The datasets that our tool collects contain profile information (user data, private messages, photos, etc.) and associated meta-data (internal timestamps and unique identifiers). These social snapshots are significant for security research and in the field of digital forensics. We implemented a prototype for Facebook and evaluated our system on a number of human volunteers. We show the feasibility and efficiency of our approach and its advantages in contrast to traditional techniques that rely on application-specific web crawling and parsing. Furthermore, we investigate different use-cases of our tool that include consensual application and the use of sniffed authentication cookies. Finally, we contribute to the research community by publishing our implementation as an open-source project.