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Welcome to Mark Baggett - In Depth Defense



I am the course Author of SANS SEC573 Automating Information Security with Python. Check back frequently for updated tools and articles related to course material.

Senior SANS Instructor
GSE #15
Internet Storm Center Handler
Penetration Testing and Incident Response Consultant
Technical Advisor to DoD for The SANS Institute
Founding President of the Greater Augusta ISSA
Cofounder of BSidesAugusta Security Conference


Tools:
FREQ SERVER - Tool and technique for detecting Malware Command and Control domains
DOMAIN_STATES - Tool for detecting "Baby Domains" used for phishing and Malware distribution
SRUM_DUMP - Forensics tool for extracting System Resource Utilization Monitoring artifacts
LIAM_NEESON - Proof of Concept Linux Hash Protection
HONEY_HASHES - Certainly Honey Tokens have been around since 2003 but I created a cool technique for creating fake SATs in memory that was turned into Dell Secure Works DCEPT framework.
VSSOWN - Tool & Technique for Using Microsoft Volume Shadow Copies for hiding malware and extracting artifacts
SDB Hacking - Using Application Compatibility in unexpected ways.
SET-KBLED - Utility for Managing Clevo and Sager Laptop LED Backlit Keyboards
Reassembler.py - Scapy based fragement reassembly engine
eapmd5crack.py - A password cracker for the EAP protocol


and more. Most of these tools are available on my github page. Follow me on twitter @markbaggett





FREQ and FREQ-SERVER UPDATE

While sitting in SANS SEC511 I listened to @sethmisenar lament the difficulty in using existing tools to detect DGA (Dynamically Generation Algorithm) hostnames used by malware. There are lots of AI based tools out there that do this but some are rather complex. I thought I could quickly write a tool that would work. In about 30 minutes I threw together some old code I had lying around from a SQL Inction tool I worked on and I had a working proof of concept. freq.py was born and it worked pretty well. A year later @securitymapper had me wrap it in a web interface so he could query it from a SIM and then the tool took off. It turns out to be a pretty effective technique and gained some popularity and wide use! This is a rewrite of the tool that incorporates some lessons learned and performance enhancments.
Improvements:
-Only one table is required for case sensitve or insensitive lookups. The tables are all case sensitive. You can turn off and on case sensitivity and the .probability lookups will do what is needed to make them case insensitive.
-Ignored char - Like ignore_case the characters are only ignored in the calculations of the probability. They are not ignored in the building of the table
-Speed. Like I said. It was a proof of concept and never really built with any performance in mind. This fixes that.
-Accuracy. Some errors in calulations were identified by Pepe Berta (thanks!). This fixes those and several others. If you find others let me know.
-Two calculations - I've added a second frequency score that i've calculated differently. It will requires some testing to see if it is more useful than the previous number in detecting random hosts.
Version Compatibility: freq.py and freq_server.py will work in either Python2 or Python3. 

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