Photos from National Cyber Security Services's post


Maltrail:-- #Malicious #traffic #detection #system.

Maltrail is a #malicious traffic detection system, utilizing publicly available (black)lists containing malicious and/or generally suspicious trails, along with static trails compiled from various AV reports and custom user-defined lists, where trail can be anything from #domain name (e.g. zvpprsensinaix.com for #Banjori malware), URL (e.g. hXXp://109.162.38.120/harsh02.exe for known malicious executable), IP address (e.g. 185.130.5.231 for known attacker) or #HTTP User-Agent header value (e.g. #sqlmap for automatic #SQL #injection and #database takeover tool). Also, it uses (optional) advanced heuristic #mechanisms that can help in the discovery of unknown threats (e.g. new #malware).

#Downlaod #Link:-

https://github.com/stamparm/maltrail

AMLIDS:-- #Android #Machine #Learning #Intrusion #Detection System is written in C#

Android App (written in C# with Xamarin Forms):-
1. Create the Android App to collect the hypothetical #information needed to create the model
2. Add LiteDb and syncing to the gRPC service
3. Add background service to do the #syncing automatically
4. Add ML detections from the background service

#Download #Link:-

https://github.com/jcapellman/AMLIDS


AMLIDS:-- #Android #Machine #Learning #Intrusion #Detection System is written in C#

Android App (written in C# with Xamarin Forms):-
1. Create the Android App to collect the hypothetical #information needed to create the model
2. Add LiteDb and syncing to the gRPC service
3. Add background service to do the #syncing automatically
4. Add ML detections from the background service

#Download #Link:-

https://github.com/jcapellman/AMLIDS