update contributors

README.md

@@ -168,6 +168,7 @@ Thanks go to these people!
     </tr>
     <tr>
        <td align="center" valign="top" width="14.28%"><a href="https://github.com/kingxiaofire"><img src="https://github.com/kingxiaofire.png" width="100px;" alt="Yan Wang"/><br /><sub><b>Yan Wang</b></sub></a></td>
+       <td align="center" valign="top" width="14.28%"><a href="https://github.com/Awsnaser"><img src="https://github.com/Awsnaser.png" width="100px;" alt="Aws Jaber"/><br /><sub><b>Aws Jaber</b></sub></a></td>
     </tr>
   </tbody>
 </table>

docs/_docs/contributing.md

@@ -36,4 +36,5 @@ should be on the list but is not):
 - Nils Forsgren, software development.
 - Bength Roland Pappila, software development.
 - Yu Hu, software development.
-- Yan Wang, software development.
+- Yan Wang, software development.
+- Aws Jaber, software development.

examples/eval/cyborg_scenario_two/eval_on_base_env.py

@@ -13,22 +13,29 @@
         maximum_steps=100, red_agent_distribution=[1.0], reduced_action_space=True, decoy_state=True,
         scanned_state=True, decoy_optimization=False, cache_visited_states=False)
     csle_cyborg_env = CyborgScenarioTwoDefender(config=config)
-    num_evaluations = 10000
-    max_horizon = 100
+    num_evaluations = 1
+    max_horizon = 20
     returns = []
     seed = 215125
     random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
-    print("Starting policy evaluation")
+    # print(csle_cyborg_env.action_id_to_type_and_host)
+    # import sys
+    # sys.exit(0)
+    # print("Starting policy evaluation")
     for i in range(num_evaluations):
         o, _ = csle_cyborg_env.reset()
         R = 0
         t = 0
         while t < max_horizon:
-            a = ppo_policy.action(o=o)
+            # a = ppo_policy.action(o=o)
+            a = 4
             o, r, done, _, info = csle_cyborg_env.step(a)
+            table = csle_cyborg_env.get_true_table()
+            print(table)
+            print(r)
             R += r
             t += 1
         returns.append(R)
-        print(f"{i}/{num_evaluations}, avg R: {np.mean(returns)}, R: {R}")
+        # print(f"{i}/{num_evaluations}, avg R: {np.mean(returns)}, R: {R}")

examples/eval/cyborg_scenario_two/evaluate_on_wrapper_env.py

@@ -3,12 +3,14 @@
 import random
 from csle_common.metastore.metastore_facade import MetastoreFacade
 from gym_csle_cyborg.envs.cyborg_scenario_two_wrapper import CyborgScenarioTwoWrapper
+from gym_csle_cyborg.dao.red_agent_type import RedAgentType
 from gym_csle_cyborg.dao.csle_cyborg_wrapper_config import CSLECyborgWrapperConfig
 
 if __name__ == '__main__':
     ppo_policy = MetastoreFacade.get_ppo_policy(id=58)
     config = CSLECyborgWrapperConfig(maximum_steps=100, gym_env_name="",
-                                     save_trace=False, reward_shaping=False, scenario=2)
+                                     save_trace=False, reward_shaping=False, scenario=2,
+                                     red_agent_type=RedAgentType.B_LINE_AGENT)
     env = CyborgScenarioTwoWrapper(config=config)
     num_evaluations = 10000
     max_horizon = 100
@@ -25,6 +27,7 @@
         while t < max_horizon:
             a = ppo_policy.action(o=o)
             o, r, done, _, info = env.step(a)
+            env.show
             R += r
             t += 1
         returns.append(R)

simulation-system/libs/csle-common/tests/test_snort_ids_controller.py

@@ -418,6 +418,7 @@ def test_get_snort_managers_info(
         :param mock_get_ports:mock_get_ports
         :param mock_get_ips: mock_get_ips
 
+
         :return: None
         """
         mock_get_ips.return_value = ["10.0.0.1", "10.0.0.2"]