notebook/Fly_Catcher.ipynb

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    "<p align=\"center\" style=\"text-align:center;\">\n",
    "  <img width=\"200\" src=\"https://github.com/ANG13T/fly-catcher/blob/main/assets/logo.png?raw=true\" alt=\"Fly Catcher logo\" />\n",
    "</p>\n",
    "<h1 align=\"center\" style=\"font-size:50px !important;\">Fly Catcher</h1>\n",
    "<p align=\"center\" style=\"text-align:center;\">\n",
    "  <i>Fly Catcher monitors for malicious ADS-B signals in the 1090MHz frequency to detect for aircraft spoofing</i>\n",
    "   <br/><br/>\n",
    "  <b><a href=\"https://github.com/ANG13T/fly-catcher#features-️\">Learn More</a></b> | <b><a href=\"https://github.com/ANG13T/fly-catcher#build-it-yourself-️\">Build Guide</a></b> | <b><a href=\"https://medium.com/@angelinatsuboi/detecting-hacker-aircraft-using-artificial-intelligence-ec249baa866b\">Article</a></b> | <b><a href=\"https://github.com/ANG13T/fly-catcher#watch-it-in-action-\">Video</a></b> | <b><a href=\"https://github.com/ANG13T/fly-catcher/blob/main/assets/project_report.pdf\">Research Paper</a></b>\n",
    "  <br/><br/>\n",
    "</p>\n",
    "<p align=\"center\" style=\"text-align:center;\">\n",
    "    <i>Created by Angelina Tsuboi</i>\n",
    "</p>"
   ]
  },
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   "cell_type": "markdown",
   "id": "b96ccf18-17c7-4b2b-b7d4-540323133361",
   "metadata": {},
   "source": [
    "# Step #1: Parse through JSON Flight Log File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b949ca71-b331-4200-8813-bc3e2c10144a",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🛫 Detected 20 planes!\n",
      "Plane #1: 845f9f - 1 records\n",
      "Plane #2: 86d624 - 1 records\n",
      "Plane #3: a835c5 - 1 records\n",
      "Plane #4: 4d0104 - 1 records\n",
      "Plane #5: 86e4da - 1 records\n",
      "Plane #6: 71c083 - 1 records\n",
      "Plane #7: 76cdb9 - 1 records\n",
      "Plane #8: 7c1479 - 1 records\n",
      "Plane #9: 86ebb6 - 1 records\n",
      "Plane #10: abba6c - 1 records\n",
      "Plane #11: 7c531d - 1 records\n",
      "Plane #12: ab271f - 1 records\n",
      "Plane #13: 8694fa - 1 records\n",
      "Plane #14: ADCF46 - 1 records\n",
      "Plane #15: B49A8B - 1 records\n",
      "Plane #16: B96D38 - 1 records\n",
      "Plane #17: E63452 - 1 records\n",
      "Plane #18: B9FE57 - 1 records\n",
      "Plane #19: 8F79E5 - 1 records\n",
      "Plane #20: B0BB00 - 1 records\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "\n",
    "# Enter Flight Log JSON from Fly Catcher BELOW\n",
    "selected_file = \"./samples/testing/sample.json\" \n",
    "\n",
    "file = open(selected_file)\n",
    "json_contents = json.load(file)\n",
    "aircraft = json_contents[\"aircraft\"]\n",
    "\n",
    "planes = []\n",
    "\n",
    "class PlaneLog:\n",
    "    def __init__(self, hex):\n",
    "        self.hex = hex\n",
    "        self.planes = []\n",
    "    def add_plane(self, plane_record):\n",
    "        self.planes.append(plane_record)\n",
    "\n",
    "for record in aircraft:\n",
    "    matched_plane = False\n",
    "    for plane in planes:\n",
    "        if plane.hex == record[\"hex\"]:\n",
    "            plane.add_plane(record)\n",
    "            matched_plane = True\n",
    "    if matched_plane == False:\n",
    "        new_plane = PlaneLog(record[\"hex\"])\n",
    "        new_plane.add_plane(record)\n",
    "        planes.append(new_plane)\n",
    "            \n",
    "file.close()\n",
    "\n",
    "print(f\"🛫 Detected {len(planes)} planes!\")\n",
    "for i, plane in enumerate(planes):\n",
    "    print(f\"Plane #{i + 1}: {plane.hex} - {len(plane.planes)} records\")\n",
    "\n",
    "def find_plane(hex):\n",
    "    for plane in planes:\n",
    "        if plane.hex == hex:\n",
    "            return plane"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3617aed-836e-4c96-a609-57ab331eeede",
   "metadata": {},
   "source": [
    "# Step 2: Load the CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2648f98a-7938-4f3a-ae7b-bb833ab6b1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"Spoof_Detection.h5\")\n",
    "\n",
    "def predict_adsb_data(adsb_message):\n",
    "    try:\n",
    "        feature_vector = [\n",
    "            float(adsb_message.get('alt_baro', 0)),\n",
    "            float(adsb_message.get('gs', 0)),\n",
    "            float(adsb_message.get('track', 0)),\n",
    "            float(adsb_message.get('baro_rate', 0)),\n",
    "            float(adsb_message.get('lat', 0)),\n",
    "            float(adsb_message.get('lon', 0)),\n",
    "            float(adsb_message.get('seen_pos', 0)),\n",
    "            float(adsb_message.get('messages', 0)),\n",
    "            float(adsb_message.get('seen', 0)),\n",
    "            float(adsb_message.get('rssi', 0)),\n",
    "        ]\n",
    "    except ValueError as e:\n",
    "        print(f\"Error processing ADS-B message: {adsb_message}, Field: {e}\")\n",
    "        return None\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    features_scaled = scaler.fit_transform([feature_vector])\n",
    "\n",
    "    prediction = model.predict(np.array(features_scaled))\n",
    "    return int(round(prediction[0][0]))"
   ]
  },
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   "cell_type": "markdown",
   "id": "5981d3ef-0b4e-47c3-8d12-d5984508a0d8",
   "metadata": {},
   "source": [
    "# Step 3: Select an Aircraft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a86db2a9-e7b5-42cc-becf-f4af89a87118",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✈️ Plane 845f9f selected!\n"
     ]
    }
   ],
   "source": [
    "selected_aircraft = \"845f9f\" # Enter ICAO Designator\n",
    "aircraft_data = find_plane(selected_aircraft)\n",
    "\n",
    "if aircraft_data == None: \n",
    "    print(\"Aircraft not found! Please select a ICAO Designator listed in the first section.\")\n",
    "else:\n",
    "    print(f\"✈️ Plane {selected_aircraft} selected!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36054863-4c62-4506-9c62-0caeb33201e4",
   "metadata": {},
   "source": [
    "# Step 4: Show Results of CNN and Details of Aircraft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9714a09e-1ea8-42f4-8a45-d84f5fdbd892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 60ms/step\n",
      "Not Spoofed --- 0% prediction of being spoofed\n",
      "\n",
      "            Aircraft Details\n",
      "=========================================\n",
      "Hex (24-bit ICAO identifier): 845f9f\n",
      "Data Source: adsc\n",
      "Flight Callsign: KZ51    \n",
      "Aircraft Registration: JA11KZ\n",
      "Aircraft Type: B748\n",
      "Aircraft Barometric Altitude (feet): 31996\n",
      "Ground Speed (knots): 487.0\n",
      "True Track (degrees): 244.0\n",
      "Rate of Change of Barometric Altitude (feet/minute): 48\n",
      "Latitude: 57.32872\n",
      "Longitude: -177.562752\n",
      "Updated Time Ago (seconds): 873.399\n",
      "Total # of Mode S Messages: 4420465\n",
      "Last Timing Message (seconds): 803.5\n",
      "Signal Power (dbFs): -29.2\n"
     ]
    }
   ],
   "source": [
    "results = predict_adsb_data(aircraft_data.planes[0])\n",
    "text = (\"Spoofed\" if results > 0.5 else \"Not Spoofed\")\n",
    "percentage = results * 100\n",
    "print(f\"{text} --- {percentage}% prediction of being spoofed\")\n",
    "\n",
    "plane_values = [{\"key\": \"hex\", \"value\": \"Hex (24-bit ICAO identifier)\"}, {\"key\": \"type\", \"value\": \"Data Source\"}, {\"key\": \"flight\", \"value\": \"Flight Callsign\"}, {\"key\": \"r\", \"value\": \"Aircraft Registration\"}, {\"key\": \"t\", \"value\": \"Aircraft Type\"}, {\"key\": \"alt_baro\", \"value\": \"Aircraft Barometric Altitude (feet)\"}, {\"key\": \"gs\", \"value\": \"Ground Speed (knots)\"}, {\"key\": \"track\", \"value\": \"True Track (degrees)\"}, {\"key\": \"baro_rate\", \"value\": \"Rate of Change of Barometric Altitude (feet/minute)\"}, {\"key\": \"lat\", \"value\": \"Latitude\"}, {\"key\": \"lon\", \"value\": \"Longitude\"}, {\"key\": \"seen_pos\", \"value\": \"Updated Time Ago (seconds)\"}, {\"key\": \"messages\", \"value\": \"Total # of Mode S Messages\"}, {\"key\": \"seen\", \"value\": \"Last Timing Message (seconds)\"}, {\"key\": \"rssi\", \"value\": \"Signal Power (dbFs)\"}]\n",
    "\n",
    "def find_value(id):\n",
    "    for x in plane_values:\n",
    "        if x[\"key\"] == id:\n",
    "            return x\n",
    "\n",
    "print(\"\\n            Aircraft Details\")\n",
    "print(\"=========================================\")\n",
    "plane_data = aircraft_data.planes[0]\n",
    "\n",
    "for i in plane_data:\n",
    "    val = find_value(i)\n",
    "    if val != None:\n",
    "        val_data = val.get(\"value\")\n",
    "        print(f\"{val_data}: {plane_data[i]}\")"
   ]
  },
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   "id": "c82de97b-d834-471c-b0b3-e9ea1f7f7618",
   "metadata": {},
   "source": [
    "# Step 5: Display Aircraft on Map using Folium"
   ]
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   "execution_count": 54,
   "id": "d5f20a8d-c195-4ee7-93c1-af51e6b2151a",
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   "source": [
    "import folium\n",
    "from IPython.display import display\n",
    "plane_coordinates = (plane_data[\"lat\"], plane_data[\"lon\"])\n",
    "m = folium.Map(location = plane_coordinates, tiles ='OpenStreetMap', zoom_start=3)\n",
    "folium.Marker(location = plane_coordinates).add_to(m)\n",
    "display(m)"
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   "source": [
    "# Thank you so much for trying Fly Catcher!\n",
    "\n",
    "### To support my work, you can sponsor me on [GitHub](github.com/ANG13T)\n",
    "### Check out my other socials!\n",
    "- Website: [angelinatsuboi.com](angelinatsuboi.com)\n",
    "- Instagram: [@angelina_tsuboi](https://instagram.com/angelina_tsuboi)\n",
    "- Twitter: [@AngelinaTsuboi](https://twitter.com/AngelinaTsuboi)\n",
    "- GitHub: [@ANG13T](github.com/ANG13T)\n",
    "- Linkedin:[https://www.linkedin.com/in/angelina-tsuboi-322028211/](https://www.linkedin.com/in/angelina-tsuboi-322028211/)\n",
    "- YouTube: [@AngelinaTsuboi](https://www.youtube.com/@AngelinaTsuboi)"
   ]
  }
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