1  import random
 2  import time
 3  
 4  class BreachBrain:
 5      def __init__(self):
 6          self.strategies = {
 7              "PLC": ["Fuzz_Modbus", "Logic_Overwrite", "Buffer_S7"],
 8              "CLOUD": ["JWT_Injection", "S3_Bucket_Scan", "Meta_Data_Siphon"],
 9              "IOT": ["Replay_Attack", "Provisioning_Hijack", "Beacon_Flood"]
10          }
11          self.knowledge_base = {}
12  
13      def analyze_target(self, target_type):
14          print(f"[*] [BRAIN] Analyzing {target_type} defenses...")
15          # Self-Learning: Pick a mutation of the strategy
16          strategy = random.choice(self.strategies[target_type])
17          return strategy
18  
19      def evolve_exploit(self, target_type, result):
20          if result == "FAIL":
21              print(f"[-] [BRAIN] Strategy Failed. Recirculating error data...")
22              # Mutation: In a real AI, this would call the LLMSwarm to re-code
23              new_strategy = f"Mutated_{random.choice(self.strategies[target_type])}"
24              self.strategies[target_type].append(new_strategy)
25              return new_strategy
26          else:
27              print(f"[+] [BRAIN] SUCCESS! Solidifying logic for {target_type}.")
28              return "STABLE"
29  
30  def run_breach_loop():
31      brain = BreachBrain()
32      targets = ["PLC", "CLOUD", "IOT"]
33      
34      for t in targets:
35          attempt = brain.analyze_target(t)
36          print(f"[!] [ACTION] Applying {attempt} to {t}...")
37          
38          # Simulate a 50/50 failure to show self-learning
39          outcome = "FAIL" if random.random() > 0.5 else "SUCCESS"
40          
41          if outcome == "FAIL":
42              next_move = brain.evolve_exploit(t, "FAIL")
43              print(f"[+] [EVOLVED] Next attempt will use: {next_move}")
44          else:
45              brain.evolve_exploit(t, "SUCCESS")
46          time.sleep(2)
47  
48  if __name__ == "__main__":
49      run_breach_loop()