This is tool for offline and online processing of network packets and creating network flows.

Reading packets could be done in two modes

• offline from PCAP file
• online sniffing of LAN

We can annotate data using True labels or predicted labels

• Ture Labels: proving attack history log files, it can detect which flows are malicious
• Predicated Labels: We could also try to analyze network flows with pretrained model and predict its anomality.

positional arguments: action:sniff|convert Choose online sniffing of a LAN or offline converting PCAP file

options: -h, --help show this help message and exit --source > In online sniffing provide and in offline converting provide --interval interval in seconds interval to compute flows --attacks attack log csv file address attack file address for finding true flows' label --predictor model address of pre trained ml model to classify incoming flows --target_stream Target server address to stream out network flows --target_file csv file to output

1. sniffing from Wi-Fi lan without annotation and writing flows to file:

sniff --source   Wi-Fi   --interval   0.5   --target_file   output/sniffed.csv 

2. offline generating of network flows from PCAP file with True label annotation and writing flows to file::

Convert 
    --source        input/traffic.pcap
    --interval      0.5
    --attacks       input/attacker_machine_summary.csv
    --target_file   output/sniffed.csv 

3. offline generating of network flows from PCAP file with True label annotation and prediction and writing flows to file:

Convert 
    --source                input/traffic.pcap
    --interval              0.5
    --attacks               input/attacker_machine_summary.csv
    --predictor             input/predict_model.joblib
    --target_file           output/sniffed.csv  

or

Convert  --source  input/traffic.pcap --interval 0.5 --attacks  input/attacker_machine_summary.csv  --target_file  output/sniffed.csv 

4. offline generating of network flows from PCAP file with True label annotation and prediction and sending them to both target file and MQTT server with credential:

Convert 
    --source                    input/traffic.pcap
    --interval                  0.5
    --attacks                   input/attacker_machine_summary.csv
    --predictor                 input/predict_model.joblib
    --target_file               output/sniffed.csv 
    --target_connection         sample_connection.txt