add cli interface

requirements.txt

@@ -0,0 +1,81 @@
+altair==5.0.1
+appdirs==1.4.4
+asttokens==2.2.1
+attrs==23.1.0
+backcall==0.2.0
+blinker==1.6.2
+cachetools==5.3.1
+certifi==2023.5.7
+charset-normalizer==3.2.0
+click==8.1.4
+comm==0.1.3
+debugpy==1.6.7
+decorator==5.1.1
+executing==1.2.0
+gitdb==4.0.10
+GitPython==3.1.31
+idna==3.4
+importlib-metadata==6.8.0
+ipykernel==6.24.0
+ipython==8.14.0
+jedi==0.18.2
+Jinja2==3.1.2
+joblib==1.3.1
+jsonschema==4.18.0
+jsonschema-specifications==2023.6.1
+jupyter_client==8.3.0
+jupyter_core==5.3.1
+lxml==4.9.3
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+nest-asyncio==1.5.6
+numpy==1.25.0
+packaging==23.1
+pandas==2.0.3
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.5.0
+platformdirs==3.8.1
+prompt-toolkit==3.0.39
+protobuf==4.23.4
+psutil==5.9.5
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==12.0.1
+pydeck==0.8.1b0
+Pygments==2.15.1
+Pympler==1.0.1
+pyshark==0.6
+python-dateutil==2.8.2
+pytz==2023.3
+pytz-deprecation-shim==0.1.0.post0
+pyzmq==25.1.0
+referencing==0.29.1
+requests==2.31.0
+rich==13.4.2
+rpds-py==0.8.10
+scapy==2.4.3
+scikit-learn==1.3.0
+scipy==1.11.1
+six==1.16.0
+smmap==5.0.0
+stack-data==0.6.2
+streamlit==1.24.1
+tenacity==8.2.2
+termcolor==2.3.0
+threadpoolctl==3.1.0
+toml==0.10.2
+toolz==0.12.0
+tornado==6.3.2
+traitlets==5.9.0
+typing_extensions==4.7.1
+tzdata==2023.3
+tzlocal==4.3.1
+urllib3==2.0.3
+validators==0.20.0
+watchdog==3.0.0
+wcwidth==0.2.6
+zipp==3.16.0

src/FlowDirection.py

@@ -1,3 +1,4 @@
+"""This function divide packets into forward and backward direction to understand flow pattern"""
 
 def flow_direction(flow_dict):
     flow_with_direction = dict()

src/extractFlow.py

@@ -1,5 +1,7 @@
 import pandas as pd
 
+"""This function extracts flow info from the pcap file and seperate them for futher processing"""
+
 def extract_flow_info(pcap):
     flow_dictionary = dict()
     columns = ['key','TimeStamp', 'srcIp', 'dstIp','srcPort', 'dstPort']

src/extractTimeStamps.py

@@ -1,3 +1,13 @@
+"""Extract the information regarding the time features from the flow
+
+FWD IAT max
+BWD IAT max
+Idle Mean time
+Active Max
+Active Mean
+Flow Bytes/s
+Fwd Avg Bulk Rate
+"""
 
 def custom_sort(packet): #a custome function to sort the packets w.r.t to their timestamp
     if 'tcp' in packet:

src/extract_flags.py

@@ -1,9 +1,9 @@
-
-
+"""This file extracts information regarding the flags in the packets
 # ACK Flag Count
 # Bwd URG Flags
 # PSH Flag Count
 # URG Flag Count
+"""
 
 #extract all flags from the packets; 
 

src/extract_size.py

@@ -1,4 +1,15 @@
 
+"""This file contains functions that extract various information regarding 
+the size of the packets and segment lengths from the flow 
+
+Bwd Packet Length Max
+Bwd Packet Length Mean
+Bwd Packet Length std
+Total Backward Packets
+Avg Fwd Segment Size
+"""
+
+
 def packet_length(flow_with_direction, packet_df):
     row = 0
     for key in flow_with_direction:  #extract a key from the dictionary

src/extract_window_size.py

@@ -1,4 +1,8 @@
 
+"""This file contains functions to extract following given features from the flow
+Init_Win_bytes_forward(Intial window bytes size in forward direction)
+act_data_pkt_fwd (actual data packets in forward direction)
+"""
 def init_window_size(flow_with_direction, packet_df):
     row = 0
     for key in flow_with_direction:

src/main.py

@@ -1,5 +1,7 @@
 import pandas as pd
+import argparse
 import pyshark
+import os
 from extractFlow import extract_flow_info
 from FlowDirection import flow_direction
 from extractTimeStamps import Time_main
@@ -9,7 +11,7 @@
 from predict_model import predict_output
 
 
-def main():
+def flow_file(pcap_file, flag):
     #define columns in the dataframe
     columns = [' Bwd IAT Max', ' Bwd URG Flags', ' Total Backward Packets',
        ' Fwd IAT Max', ' Active Max', ' URG Flag Count',
@@ -22,7 +24,7 @@ def main():
     try:
         packet_df = pd.DataFrame(columns=columns)
 
-        pcap = pyshark.FileCapture('pcap_files/example.pcap')
+        pcap = pyshark.FileCapture(pcap_file)
         flow_dictionary, features_df = extract_flow_info(pcap)
         flow_with_direction = flow_direction(flow_dictionary)
         Time_main(flow_with_direction, packet_df)
@@ -34,21 +36,66 @@ def main():
 
         #drop extra columns from the dataframe !!
         packet_df.drop(['dummy',18],axis = 1,inplace=True)
-        # print(packet_df)
 
-        predict_output(packet_df, features_df)
+        if flag == "flow":
+            print("Output generated Successfully\n", packet_df.describe())
+
+            flow_csv_path = 'flow.csv'
 
-        mapping_dict = {0:"BENIGN",4:"DoS Hulk",2:"DDoS", 10:"PortScan",3:"DoS GoldenEye",5:"DoS Slowhttptest",
-                        6:"DoS slowloris", 7:"FTP-Patator",11:"SSH-Patator",1:"Bot",12:"Web Attack � Brute Force",
-                        8:"Heartbleed ", 9:"Infiltration",13:"Web Attack � Sql Injection",14:"Web Attack � XSS"}     
+            packet_df.to_csv(flow_csv_path, index=False)
+            print(f"Flow CSV file generated: {flow_csv_path}")
 
-        features_df['result'] = features_df['result'].replace(mapping_dict)   
-
-        """----------------------saving to csv file------------------------"""
-        features_df.to_csv("ansDF.csv",index=False)
     except Exception as e:
         print("An error occured: ", e.args[0])
 
+    return packet_df,features_df
+
+def result_file(packet_df, features_df):
+    # print(packet_df)
+        try:
+            predict_output(packet_df, features_df)
+
+            mapping_dict = {0:"BENIGN",4:"DoS Hulk",2:"DDoS", 10:"PortScan",3:"DoS GoldenEye",5:"DoS Slowhttptest",
+                            6:"DoS slowloris", 7:"FTP-Patator",11:"SSH-Patator",1:"Bot",12:"Web Attack � Brute Force",
+                            8:"Heartbleed ", 9:"Infiltration",13:"Web Attack � Sql Injection",14:"Web Attack � XSS"}     
+
+            features_df['result'] = features_df['result'].replace(mapping_dict)   
+
+            """----------------------saving to csv file------------------------"""
+
+            print("Output generated Successfully\n",features_df['result'].value_counts())
+
+            flow_csv_path = 'result.csv'
+
+            features_df.to_csv(flow_csv_path, index=False)
+            print(f"Result CSV file generated: {flow_csv_path}")
+
+        except Exception as e:
+            print("An error occured: ", e.args[0])
+
+def main():
+
+    parser = argparse.ArgumentParser(description="Network Intrusion Tool/flanker-toolX")
+    parser.add_argument('-f', '--flow', action='store_true', help="Generate flow.csv file")
+    parser.add_argument('-r', '--result', action='store_true', help="Get result")
+    parser.add_argument('--pcap', type=str, help="Path to the uploaded pcap/pcapng file")
+
+    args = parser.parse_args()
+
+    if args.flow and args.pcap:
+        if os.path.isfile(args.pcap) and args.pcap.endswith('.pcap') or args.pcap.endswith('.pcapng'):
+            pkt_df, ft_df = flow_file(args.pcap, "flow")
+        else:
+            print("Invalid pcap file. Please provide a valid path to a .pcap file.")
+    elif args.result and args.pcap:
+        if os.path.isfile(args.pcap) and args.pcap.endswith('.pcap') or args.pcap.endswith('.pcapng'):
+            pkt_df, ft_df = flow_file(args.pcap,"res")
+            result_file(pkt_df, ft_df)
+        else:
+            print("Invalid pcap file. Please provide a valid path to a .pcap/.pcapng file.")
+    else:
+        print("Invalid option. Please choose either --flow or --result, and provide a valid --pcap file path.\nuse --help for more information")
+
     return
 
 """---------------------------------------------------------------------------------------------"""

src/predict_model.py

@@ -1,3 +1,7 @@
+
+"""This Fcuntion takes the extracted flow and predict in the pre-trained model
+and Will resturn the result to the user"""
+
 import joblib
 def predict_output(packet_df, features_df):
     model = joblib.load('ML_model/saved_model.pkl')