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Showing posts from February, 2019

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





SRUM-DUMP and SRUM_DUMP_CSV Ported to Python 3

SRUM_DUMP and SRUM_DUMP_CSV have been ported to Python3 and are available for download from the PYTHON3 branch of my github page.

https://github.com/MarkBaggett/srum-dump/tree/python3

In moving to Python3 I also updated the modules that I depend upon to parse and create XLSX files and access the ESE database that contains the SRUM data.  I hope that this will fix the issue that some users have experienced with SRUDB.dat files that create very large spreadsheets.  If it does not please let me know and continue to use SRUM_DUMP_CSV.EXE to avoid the XLSX problem.

In moving to Python3 you will find the process to be faster.

If you would like to run the tools from source instructions for doing so are in the README on the github page.