LOLBAS
SIGMA-detection-rules
| LOLBAS | SIGMA-detection-rules | |
|---|---|---|
| 5 | 2 | |
| 6,609 | 268 | |
| 1.2% | - | |
| 8.3 | 6.4 | |
| 8 days ago | 2 months ago | |
| XSLT | ||
| GNU General Public License v3.0 only | Creative Commons Zero v1.0 Universal |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LOLBAS
-
How do i replicate GTFOBins layout ?
I have an idea for a simar style website that i would like to create, and i was going to use GTFOBins as a template and ammend to fit my data, much like LOLBAS has.
-
should we write our own custom rule
Fo example the usage of living off the land binaries https://lolbas-project.github.io/ , when I've implemented rules in order to detect this ,I've seen a lot of false positive because of the context ,but the rule was correct.
- Living Off the Land Binaries
-
Lolbas: Living Off the Land Binaries, Scripts and Libraries
https://github.com/LOLBAS-Project/LOLBAS/tree/master
The associated github is much more informative about what the project does. Basically they document "undocumented" capabilities of signed microsoft binaries that can be used for red team work.
-
CISA bulletin – PRC state-sponsored cyber actor techniques [pdf]
Yes, see for example [1].
[1] https://github.com/LOLBAS-Project/LOLBAS
SIGMA-detection-rules
-
should we write our own custom rule
I am currently employed as a cyber analyst, and we've recently implemented an Endpoint Detection and Response (EDR) system. Upon closer inspection, I've observed that numerous events are not being flagged as alerts. This raises a crucial question: should I take the initiative to create custom rules to ensure these events are brought to our attention, or should I rely solely on the EDR's intrinsic capabilities to detect and classify threats? As a potential solution, I'm contemplating the implementation of rules based on Sigma, such as those available at the following repository: here. Your insights and experiences on the effectiveness of this approach would be greatly appreciated. Thank you for your time and assistance.
-
Installed Graylog. 7 million log entries per month. Now what?
Depending on whether this is up your alley either look for a MSSP/MDR/Managed BlaBla provider or head on to - https://github.com/splunk/security_content - https://www.elastic.co/guide/en/security/current/prebuilt-rules.html - https://github.com/mdecrevoisier/SIGMA-detection-rules - https://github.com/Azure/Azure-Sentinel to get an idea of what to look for. MITRE ATT&CK and the related DETT&CT should serve as an additional eye opener. Ah yes - forgot the bible on log management from Anton Chuvakin in the above list.
What are some alternatives?
UltimateAppLockerByPassList - The goal of this repository is to document the most common techniques to bypass AppLocker.
EVTX-to-MITRE-Attack - Set of EVTX samples (>270) mapped to MITRE ATT&CK tactic and techniques to measure your SIEM coverage or developed new use cases.
GoodHound - Uses Sharphound, Bloodhound and Neo4j to produce an actionable list of attack paths for targeted remediation.
Azure-Sentinel - Cloud-native SIEM for intelligent security analytics for your entire enterprise.
purple-team-exercise-framework - Purple Team Exercise Framework
security_content - Splunk Security Content
gitlab-watchman - Finding exposed secrets and personal data in GitLab
slack-watchman - Slack enumeration and exposed secrets detection tool
LOLDrivers - Living Off The Land Drivers
LOOBins - Living Off the Orchard: macOS Binaries (LOOBins) is designed to provide detailed information on various built-in "living off the land" macOS binaries and how they can be used by threat actors for malicious purposes.