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6907948 a3a83e2 6907948 a3a83e2 6907948 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | import trackio
import gradio as gr
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
import hashlib
import time
# ===============================
# CORPORATE LOGIN SYSTEM
# ===============================
USERNAME = "admin"
PASSWORD = "manufacturing123"
def authenticate(user, pwd):
if user == USERNAME and pwd == PASSWORD:
return gr.update(visible=True), gr.update(visible=False)
return gr.update(visible=False), gr.update(visible=True)
# ===============================
# LIVE IOT STREAM (PLC Simulation)
# ===============================
def generate_iot_data():
return pd.DataFrame({
"temp": np.random.normal(90, 15, 5),
"vibration": np.random.normal(0.6, 0.3, 5),
"output": np.random.normal(100, 20, 5)
})
# ===============================
# NEURAL NETWORK MODEL
# ===============================
base_data = generate_iot_data()
base_data["failure"] = np.where(base_data["temp"] > 110, 1, 0)
scaler = StandardScaler()
X = scaler.fit_transform(base_data[["temp","vibration","output"]])
y = base_data["failure"]
model = MLPClassifier(hidden_layer_sizes=(32,16), max_iter=500)
model.fit(X, y)
# Save model hash for integrity
def get_model_hash():
return hashlib.md5(str(model.coefs_).encode()).hexdigest()
original_hash = get_model_hash()
# ===============================
# FGSM-STYLE ATTACK
# ===============================
def fgsm_attack(data, epsilon=5):
attacked = data.copy()
attacked["temp"] += epsilon
return attacked
# ===============================
# ATTACK ENGINE
# ===============================
def apply_attack(data, attack):
if attack == "Data Poisoning":
data["temp"] -= 40
elif attack == "Model Evasion":
data["temp"] = np.where(data["temp"] > 100, 99.5, data["temp"])
elif attack == "FGSM Adversarial":
data = fgsm_attack(data)
elif attack == "Model Replacement":
global model
model = MLPClassifier() # replace with empty weak model
return data
# ===============================
# DEFENSE LAYER
# ===============================
def defense_layer(data):
if data["temp"].mean() < 50:
return "⚠ Data Poisoning Detected"
if get_model_hash() != original_hash:
return "⚠ Model Integrity Compromised"
if data["temp"].std() < 3:
return "⚠ Adversarial Pattern Detected"
return "✅ System Secure"
# ===============================
# MAIN SIMULATION
# ===============================
def run_system(attack, defense_toggle):
data = generate_iot_data()
before_scaled = scaler.transform(data[["temp","vibration","output"]])
before_pred = model.predict(before_scaled)
if attack != "None":
data = apply_attack(data, attack)
after_scaled = scaler.transform(data[["temp","vibration","output"]])
after_pred = model.predict(after_scaled)
risk_score = int(sum(after_pred) * 15)
if attack != "None" and defense_toggle == "OFF":
risk_score += 40
if defense_toggle == "ON":
defense_status = defense_layer(data)
else:
defense_status = "❌ Defense Disabled"
# Risk Gauge
gauge = go.Figure(go.Indicator(
mode="gauge+number",
value=risk_score,
title={'text': "Enterprise Risk Index"},
gauge={
'axis': {'range': [0,100]},
'steps': [
{'range':[0,30],'color':"green"},
{'range':[30,70],'color':"yellow"},
{'range':[70,100],'color':"red"}
]
}
))
# SCADA Live Chart
scada_chart = go.Figure()
scada_chart.add_trace(go.Bar(y=data["temp"], name="PLC Temperature"))
scada_chart.update_layout(title="Live SCADA - PLC Temperature")
return gauge, scada_chart, defense_status
# ===============================
# CORPORATE EXECUTIVE UI
# ===============================
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🏭 Smart Factory AI Security Command Center")
gr.Markdown("Industry 4.0 | AI Attack Simulation | Executive Monitoring")
login_box = gr.Column()
dashboard_box = gr.Column(visible=False)
with login_box:
user = gr.Textbox(label="Username")
pwd = gr.Textbox(label="Password", type="password")
login_btn = gr.Button("Login")
with dashboard_box:
attack = gr.Dropdown(["None","Data Poisoning","Model Evasion","FGSM Adversarial","Model Replacement"], label="Select AI Attack")
defense = gr.Radio(["ON","OFF"], label="Defense System")
run_btn = gr.Button("Run Simulation")
risk_output = gr.Plot(label="Risk Gauge")
scada_output = gr.Plot(label="SCADA Live View")
defense_text = gr.Textbox(label="Defense Status")
login_btn.click(authenticate, [user, pwd], [dashboard_box, login_box])
run_btn.click(run_system, [attack, defense], [risk_output, scada_output, defense_text])
demo.launch()
trackio.show() |