Backup To The Rescue: Automated Forensic Techniques For Advanced Website-Targeting Cyber Attacks
Sridhar, Ranjita Pai
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The last decade has seen a significant rise in non-technical users gaining a web presence, often via the easy-to-use functionalities of Content Management Systems (CMS). In fact, over 60% of the world’s websites run on CMSs. Unfortunately, this huge user population has made CMS-based websites a high-profile target for hackers. Worse still, the vast majority of the website hosting industry has shifted to a “backup and restore” model of security, which relies on error-prone AV scanners to prompt non-technical users to roll back to a pre-infection nightly snapshot. My cyber forensics research directly addresses this emergent problem by developing next-generation techniques for the investigation of advanced cyber crimes. Driven by economic incentives, attackers abuse the trust in this economy: selling malware on legitimate marketplaces, pirating popular website plugins, and infecting websites post-deployment. Furthermore, attackers are exploiting these websites at scale by carelessly dropping thousands of obfuscated and packed malicious files on the webserver. This is counter-intuitive since attackers are assumed to be stealthy. Despite the rise in web attacks, efficiently locating and accurately analyzing the malware dropped on compromised webservers has remained an open research challenge. This dissertation posits that the already collected webserver nightly backup snapshots contain all required information to enable automated and scalable detection of website compromises. This dissertation presents a web attack forensics framework that leverages program analysis to automatically understand the webserver’s nightly backup snapshots. This will enable the recovery of temporal phases of a webserver compromise and its origin within the website supply chain.