Uploading to PyPi

README.md

@@ -35,6 +35,7 @@ proxy.sh [target] [port]
   - `-s` or `--serve` to start the proxy server
   - `-t` or `--train` to train the model
     - Default
+
 Example:
 ```bash
 python -m quasar_firewall [options] [target] [port]

quasar_project_firewall.egg-info/PKG-INFO → quasar.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
-Name: quasar-project-firewall
-Version: 1.0.1.1
+Name: quasar
+Version: 1.0.0
 Summary: An Artificial Intelligent firewall that detects malicious HTTP requests
 Home-page: https://github.com/kyro-dev/Quasar-Project
 Author: Kyro Dev
@@ -43,6 +43,7 @@ Description: # Quasar Firewall
           - `-s` or `--serve` to start the proxy server
           - `-t` or `--train` to train the model
             - Default
+
         Example:
         ```bash
         python -m quasar_firewall [options] [target] [port]
@@ -70,6 +71,8 @@ Description: # Quasar Firewall
 
 
 Platform: UNKNOWN
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Information Technology
 Classifier: Programming Language :: Python :: 3
 Classifier: License :: OSI Approved :: GNU General Public License (GPL)
 Classifier: Operating System :: OS Independent

quasar.egg-info/SOURCES.txt

@@ -0,0 +1,21 @@
+README.md
+setup.py
+quasar/__init__.py
+quasar/__main__.py
+quasar/app.py
+quasar/dataset.py
+quasar/detector.py
+quasar/detector_flask.py
+quasar/evolution.py
+quasar/neuralnet.py
+quasar/request.py
+quasar/request_parser.py
+quasar/visualize.py
+quasar.egg-info/PKG-INFO
+quasar.egg-info/SOURCES.txt
+quasar.egg-info/dependency_links.txt
+quasar.egg-info/entry_points.txt
+quasar.egg-info/requires.txt
+quasar.egg-info/top_level.txt
+scripts/proxy.sh
+scripts/train.sh

quasar_project_firewall.egg-info/requires.txt → quasar.egg-info/requires.txt

@@ -1,12 +1,12 @@
-autopep8==1.5.4
+autopep8
 bleach==3.2.1
 certifi==2020.12.5
-chardet==4.0.0
+chardet
 click==7.1.2
 colorama==0.4.4
 cycler==0.10.0
 docutils==0.16
-Flask==1.1.2
+Flask
 graphviz==0.15
 idna==2.10
 itsdangerous==1.1.0
@@ -17,7 +17,7 @@ kiwisolver==1.3.1
 MarkupSafe==1.1.1
 matplotlib==3.3.3
 neat-python==0.92
-numpy==1.19.4
+numpy==1.19.3
 packaging==20.7
 Pillow==8.0.1
 pkginfo==1.6.1
@@ -27,7 +27,7 @@ pyparsing==2.4.7
 python-dateutil==2.8.1
 pywin32-ctypes==0.2.0
 readme-renderer==28.0
-requests==2.25.0
+requests
 requests-toolbelt==0.9.1
 rfc3986==1.4.0
 scikit-learn==0.23.2
@@ -36,7 +36,7 @@ six==1.15.0
 threadpoolctl==2.1.0
 toml==0.10.2
 tqdm==4.54.1
-twine==3.2.0
 urllib3==1.26.2
 webencodings==0.5.1
-Werkzeug==1.0.1
+wheel
+Werkzeug

quasar/__init__.py

@@ -0,0 +1,9 @@
+"""
+Quasar
+
+A fully artificaially intelligent firewall
+"""
+
+__version__ = "1.0.0"
+__author__ = 'Kyro'
+__credits__ = 'KyroDev'

quasar/__main__.py

@@ -10,7 +10,7 @@
 
 parser = argparse.ArgumentParser(description="AI Firewall")
 parser.add_argument("-t", "--train", help="Train the model",
-                    action="store_true")
+                    nargs='?', const=True)
 parser.add_argument("-s", "--serve", nargs=2, metavar=("target", "port"),
                     help="Start the proxy server on 'port' with proxy destination being 'target'")
 

quasar/dataset.py

@@ -6,7 +6,14 @@
 from .request import Request
 
 
-def parse_dataset(f:str):
+def get_directory(dir_name: str = ''):
+    from pathlib import Path
+
+    path_to_quasar = Path(__file__).parents[0]
+    return path_to_quasar / dir_name
+
+
+def parse_dataset(f: str):
     """
         - Parses the dataset
         - Input dataset must be xml
@@ -27,7 +34,7 @@ def parse_dataset(f:str):
         class_elem = sample.find('class')
         req = sample.find('request')
         data = {}
-        
+
         for item in req:
             tag = item.tag
             if tag.lower() not in ["uri", "query", "body"]:
@@ -40,7 +47,7 @@ def parse_dataset(f:str):
 
         requests.append(Request(data).to_dict())
 
-    file = open("dataset.json", "w")
+    file = open("./datasets/dataset.json", "w")
 
     file.write(json.dumps(requests))
 
@@ -49,26 +56,27 @@ def parse_dataset(f:str):
     return requests
 
 
-def load_dataset(file:str="./datasets/web-application-attacks-datasets/ecml_pkdd/learning_dataset.xml"):
+def load_dataset(file: str = f"{get_directory('datasets') / 'web-application-attacks-datasets/ecml_pkdd/learning_dataset.xml'}"):
     """
         Handles dataset loading, returns parsed dataset in `List[Request]` form
     """
+    os.chdir(str(get_directory()))
 
     global data
     data = None
     reqs: List[Request] = []
 
-    if not os.path.exists("./dataset.json"):
+    if not os.path.exists("./datasets/dataset.json"):
         data = parse_dataset(file)
 
     try:
-        with open("./dataset.json") as f:
+        with open("./datasets/dataset.json") as f:
             data = json.load(f)
             for d in data:
                 reqs.append(Request(d))
-    except json.decoder.JSONDecodeError:
-        os.remove('./dataset.json')
-        raise RuntimeError("An error occured, please try again later")
+    except json.decoder.JSONDecodeError as e:
+        import traceback
+        traceback.print_exc()
 
     return reqs
 

quasar/detector_flask.py

@@ -9,6 +9,13 @@
 from .request_parser import BodyParser, QueryParser
 
 
+def get_directory(dir_name: str):
+    from pathlib import Path
+
+    path_to_quasar = Path(__file__).parents[0]
+    return str(path_to_quasar / dir_name)
+
+
 class DetectorMiddleware(object):
     """
         Middleware for detecting hacks
@@ -17,12 +24,13 @@ class DetectorMiddleware(object):
     def __init__(self, app: Flask):
         print("Loading models")
         self.app = app
-        self.evolution: Evolution = Evolution.load('499', './models')
+        self.evolution: Evolution = Evolution.load(
+            '499', get_directory("models"))
         self.body_parser = BodyParser.load()
         self.query_parser = QueryParser.load()
         self.detector = Detector(
             self.evolution, self.body_parser, self.query_parser)
-        
+
         print("Finished loading")
 
         try:
@@ -44,13 +52,12 @@ def __call__(self, environ, start_response):
             "is_hack": None
         })
 
-        valid = self.detector.predict(data, request.get_data(), request.query_string)
-
+        valid = self.detector.predict(
+            data, request.get_data(), request.query_string)
 
         if valid:
             # Its a hack
             res = Response("ERROR 404", 404)
             return res(environ, start_response)
 
         return self.app(environ, start_response)
-

quasar/evolution.py

@@ -43,12 +43,15 @@ def train(self, config_path: str):
 
         winner = self.p.run(self.__eval_genome__, self.generations)
 
-        node_names = {-1: "method", -2: "content-type", -3: "protocol", 0: "Hack probability"}
+        node_names = {-1: "method", -2: "content-type", -
+                      3: "protocol", 0: "Hack probability"}
 
         visualize.draw_net(self.__get_config__(
             config_path), winner, view=True, filename="./visualizations/model", node_names=node_names)
-        visualize.plot_stats(self.stats, filename="./visualizations/avg_fitness.svg", ylog=False, view=True)
-        visualize.plot_species(self.stats, filename="./visualizations/speciation.svg", view=True)
+        visualize.plot_stats(
+            self.stats, filename="./visualizations/avg_fitness.svg", ylog=False, view=True)
+        visualize.plot_species(
+            self.stats, filename="./visualizations/speciation.svg", view=True)
 
         return winner
 
@@ -68,13 +71,16 @@ def predict(self, input: Request):
 
     @staticmethod
     def load(checkpoint: str, session: str = '.'):
+        from pathlib import Path
         """
             Loads checkpoint with the prefix of `neat-chekpoint-`
             - checkpoint: desired checkpoint id
             - session: session directory (defaults to current directory)
         """
+        path = Path(str(session)) / f"neat-checkpoint-{str(checkpoint)}"
+
         point: Population = neat.Checkpointer.restore_checkpoint(
-            f'{str(session)}/neat-checkpoint-{str(checkpoint)}')
+            f'{str(path)}')
 
         return Evolution(point)
 
@@ -199,7 +205,7 @@ def train(self, config_path: str):
 
         pe = neat.ThreadedEvaluator(
             multiprocessing.cpu_count() - 1, self.__eval_genome__)
-        
+
         winner = self.p.run(pe.evaluate, self.generations)
 
         node_names = {-1: "method", -2: "headers", -3: "protocol", -
@@ -211,7 +217,7 @@ def train(self, config_path: str):
         visualize.plot_species(self.stats, view=True)
 
         return winner
-    
+
     @staticmethod
     def load(checkpoint: str, session: str = '.'):
         """

quasar_project.egg-info/PKG-INFO

@@ -0,0 +1,80 @@
+Metadata-Version: 2.1
+Name: quasar-project
+Version: 1.0.0
+Summary: An Artificial Intelligent firewall that detects malicious HTTP requests
+Home-page: https://github.com/kyro-dev/Quasar-Project
+Author: Kyro Dev
+Author-email: [email protected]
+License: UNKNOWN
+Description: # Quasar Firewall
+        An AI powered firewall designed to detect mallicious HTTP requests and decide weather to process them or not.
+
+        ```
+        pip install quasar-project-firewall
+        ```
+
+        # Usage
+        ## As Flask Middleware
+        *Note: Model must be trained before starting proxy server*
+        ```python
+        from detector_flask import DetectorMiddleware
+        ...
+        app.wsgi_app = DetectorMiddleware(app.wsgi_app)
+        ...
+        ```
+        ## Starting Proxy From Command Line
+        *Note: Must have python installed on local machine and added to PATH*<br>
+        *Note: Model must be trained before starting proxy server*
+
+        ### **With Bash**
+        Call `proxy.sh` with paramaters `target` and  `port`
+        - `target` is the proxy destination
+          - defaults to `http://localhost:8080`
+        - `port` is the local port to run the server on
+          - defaults to 5000
+
+        Example:
+        ```bash
+        proxy.sh [target] [port]
+        ```
+
+        ### **As Python Module**
+        - `options`
+          - `-s` or `--serve` to start the proxy server
+          - `-t` or `--train` to train the model
+            - Default
+
+        Example:
+        ```bash
+        python -m quasar_firewall [options] [target] [port]
+        ```
+
+        Redirect requests from original server to proxy server
+
+
+        ## Training the Model
+        *Note: Must have python installed on local machine and added to PATH*
+
+        ### **With Bash**
+        Call `train.sh`
+
+        Example:
+        ```bash
+        train.sh
+        ```
+
+        ### **As Python Module**
+        Example:
+        ```bash
+        python -m quasar_firewall -t
+        ```
+
+
+Platform: UNKNOWN
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Information Technology
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: GNU General Public License (GPL)
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown

quasar_project.egg-info/SOURCES.txt

@@ -0,0 +1,21 @@
+README.md
+setup.py
+quasar/__init__.py
+quasar/__main__.py
+quasar/app.py
+quasar/dataset.py
+quasar/detector.py
+quasar/detector_flask.py
+quasar/evolution.py
+quasar/neuralnet.py
+quasar/request.py
+quasar/request_parser.py
+quasar/visualize.py
+quasar_project.egg-info/PKG-INFO
+quasar_project.egg-info/SOURCES.txt
+quasar_project.egg-info/dependency_links.txt
+quasar_project.egg-info/entry_points.txt
+quasar_project.egg-info/requires.txt
+quasar_project.egg-info/top_level.txt
+scripts/proxy.sh
+scripts/train.sh

quasar_project.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

quasar_project.egg-info/entry_points.txt

@@ -0,0 +1,3 @@
+[console_scripts]
+quasar = quasar.__main__:main
+