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Challenge Name Challenge link Type Lazaretto ✔ Download Forensics Hotel ✔ Download Forensics Subscription Download Forensics Geology docker pull cyctf/geology Forensics imPOSTer ✔ Download Forensics Mach ✔ Download Mobile Lazaretto Challege Description Writeup we were provided with an ad1 file using FTKimager we were able to open it and it had only windows event logs So i just dumped the files and using the famous ericzimmerman tool EvtxECmd i was able to pares the full events into a csv file using this command...

Description This Ubuntu Linux honeypot was put online in Azure in early October to watch what happens with those exploiting CVE-2021-41773. Initially, there was a large number of crypto miners that hit the system. You will see one cron script meant to remove files named kinsing in /tmp. This was a way of preventing these miners so more interesting things could occur. Challenge Files: sdb.vhd.gz VHD of the main drive obtained through an Azure disk snapshot ubuntu....

Description A Linux server was possibly compromised and a forensic analysis is required in order to understand what really happened. Hard disk dumps and memory snapshots of the machine are provided in order to solve the challenge. Challenge Files: victoria-v8.kcore.img: memory dump done by dd’ing /proc/kcore. victoria-v8.memdump.img: memory dump done with memdump. Debian5_26.zip: volatility custom Linux profile. Challenge Link : https://cyberdefenders.org/labs/41 Supportive Tools: Volatilty 2.6 FTK imger HxD Writeup Q1 The attacker was performing a Brute Force attack....

Challenge We were provided with this info https://77.87.243.155 username: alpha password: 900d1uck734m41ph4 and we will use it to login to the web kalIbox that we will use to solve the machine Writeup I had a little idea that they have the machine on the same box so Ichecked the hosts to know the ip cat /etc/hosts yup I was right the ip was there 192.168.204.3 funbox11 let’s scan it using nmap...

You can get the data from Kaggle Importing Needed Lib import os from tensorflow.keras import layers from tensorflow.keras import Model from tensorflow.keras.preprocessing.image import ImageDataGenerator import tensorflow as tf import matplotlib.pyplot as plt import PIL Getting the Data Ready train_datagen = ImageDataGenerator(rescale = 1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') test_datagen = ImageDataGenerator( rescale = 1.0/255) train_generator = train_datagen.flow_from_directory('Gender/Train', batch_size =256 , class_mode = 'binary', target_size = (64, 64)) validation_generator = test_datagen....