port-api.py

import sys
import time
import json
import requests
from flask import Flask, jsonify, request
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from flask_cors import CORS

app = Flask(__name__)
CORS(app)
data_file = "data.json" # json file storing the ports

def process_ports():
    try:
        with open(data_file, "r") as file: # reading the json file
            data = json.load(file) # store as data
            ports = data.get("ports", []) # ports is the array of ports in data
            guess = predict_ports(ports) # call ai method to guess function 
        data["predicted_use_case"] = guess
        
        with open(data_file, "w") as file: # open json file and write the ai prediction to it
            json.dump(data, file, indent=2)
        
        print("Prediction saved")
    except Exception as e:
        print(f"Error processing ports: {e}")


training = [ # X AXIS - is the category of service
             # Y AXIS - is the port (listed in columns)
             # 1 = this service is associated with this port
             # 0 = not
    [1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, "Web browsing/hosting"], 
    [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, "SSH remote access"],
    [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, "Web server with database"],
    [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, "Database access"],
    [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, "Remote desktop (RDP)"],
    [0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, "Email server (SMTP, IMAP, POP3)"],
    [0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, "Secure email (SMTPS, IMAPS, POP3S)"],
    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, "Local development server"],
    [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, "Enterprise hosting with full admin access"],
    [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, "DNS server"],
    [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, "FTP file transfer"],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, "VPN access (OpenVPN)"],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, "Minecraft server hosting"],
]
columns = [
    "Port 22", "Port 80", "Port 443", "Port 3306", "Port 3389", 
    "Port 3000", "Port 25", "Port 110", "Port 143", "Port 465", "Port 993", "Use Case"
]

df = pd.DataFrame(training, columns=columns) # makes data set

# gives every row a unique numerical value 
label_mapping = {label: idx for idx, label in enumerate(df["Use Case"].unique())}
reverse_label_mapping = {v: k for k, v in label_mapping.items()}
df["Use Case"] = df["Use Case"].map(label_mapping)

X = df.drop(columns=["Use Case"])
y = df["Use Case"] 

clf = DecisionTreeClassifier()
clf.fit(X, y) # actually makes the decision tree and stores the x and y coordinates 

# function for the ai prediction
def predict_ports(ports):
    port_mapping = {
        22: "Port 22",
        80: "Port 80",
        443: "Port 443",
        3306: "Port 3306",
        3389: "Port 3389",
        3000: "Port 3000",
        25: "Port 25",
        110: "Port 110",
        143: "Port 143",
        465: "Port 465",
        993: "Port 993",
    }
    # this converts it to a dataset instead of a dictionary, containing 1s and 0s
    input_vector = {col: 0 for col in X.columns}  
    for port in ports:
        if port in port_mapping:
            input_vector[port_mapping[port]] = 1  

    input_df = pd.DataFrame([input_vector])
    prediction = clf.predict(input_df)[0]
    return reverse_label_mapping.get(prediction, "Unknown") # unknown for some adverse scenario

@app.route("/save-ports", methods=["POST"])
def save_ports():
    try:
        data = request.json
        with open(data_file, "w") as file:
            json.dump(data, file, indent=2)
        
        process_ports()  # Generate prediction immediately after saving
        
        return jsonify({"message": "Ports saved successfully"}), 200
    except Exception as e:
        return jsonify({"error": str(e)}), 500

@app.route("/get-ports", methods=["GET"])
def get_ports():
    try:
        with open(data_file, "r") as file:
            data = json.load(file)
        
        return jsonify(data), 200
    except Exception as e:
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(debug=True, port=5012)