SANS Digital Forensics and Incident Response Blog: Category - Computer Forensics

SANS CTI Summit & Training Twitter Contest

We're delighted to announce a Twitter-based contest here with a fantastic prize. And, participating in this one is really easy. Check it out. On February 3rd through 10th, SANS will be running our fourth annual Cyber Threat Intelligence Summit & Training (https://www.sans.org/event/cyber-threat-intelligence-summit-2016) in Alexandria, VA. This event will focus on enabling organizations to build effective … Continue reading SANS CTI Summit & Training Twitter Contest


DFIR Summit 2016 - Call for Papers Now Open

The 9th annual Digital Forensics and Incident Response Summit will once again be held in the live musical capital of the world, Austin, Texas. The Summit brings together DFIR practitioners who share their experiences, case studies and stories from the field. Summit attendees will explore real-world applications of technologies and solutions from all aspects of … Continue reading DFIR Summit 2016 - Call for Papers Now Open


Using ProcDOT Plugins to Examine PCAP Files When Analyzing Malware

ProcDOT is a free tool for analyzing the actions taken by malware when infecting a laboratory system. ProcDOT supports plugins, which could extend the tool's built-in capabilities. This article looks at two plugins that help examine contents of the network capture file loaded into ProcDOT. Continue reading Using ProcDOT Plugins to Examine PCAP Files When Analyzing Malware


Update for DensityScout

There's a new build of DensityScout available (https://cert.at/downloads/software/densityscout_en.html). For the new build a scenario has been addressed where DensityScout could start to hang/loop during file computation. Happy DensityScout-ing ... Christian Continue reading Update for DensityScout


Timeline analysis with Apache Spark and Python

This blog post introduces a technique for timeline analysis that mixes a bit of data science and domain-specific knowledge (file-systems, DFIR). Analyzing CSV formatted timelines by loading them with Excel or any other spreadsheet application can be inefficient, even impossible at times. It all depends on the size of the timelines and how many different … Continue reading Timeline analysis with Apache Spark and Python