AI Space Write-up: Unmasking the Space Hacker via MDS

Category: AI/ML
Difficulty: Easy
Points: 30
Challenge Description: You are assigned the important mission of locating and identifying the infamous space hacker. Your investigation begins by analyzing the data patterns and breach points identified in the latest cyber-attacks. Use the provided coordinates of the last known signal origins to narrow down his potential hideouts. Utilize advanced tracking algorithms to follow the digital footprint left by the hacker.

1. Initial File Interrogation: What's in distance_matrix.npy?

The adventure began with distance_matrix.npy. The .npy suffix pointed straight to a NumPy array, a common data format in Python. My first order of business was to peek inside.

I always like to start with a fresh Python virtual environment for CTF tooling:

# Setting up in the challenge directory
python -m venv venv
.\venv\Scripts\activate
pip install numpy

With numpy ready, I whipped up a small script (analyze_npy.py) to get the vital stats of the array:

# My quick inspection script: analyze_npy.py
import numpy as np

file_path = "distance_matrix.npy"
try:
data = np.load(file_path)
print(f"Matrix Shape: {data.shape}")
print(f"Data Type: {data.dtype}")
print("First few entries (5x5 sample):\n", data[:5, :5])
except Exception as e:
print(f"Error loading file: {e}")

The output was revealing: