Read and process pcap files using this nifty tool.
This tool can read pcap files, process them internally and write them to one or more sinks. Currently there are mappers written for pcap length conversion and protocol normalization. I also have written sinks to write the pcap file to console, csv file or http endpoint.
usage: pcap-processor [-h] [--map {length,protocol}]
[--sink {console,kafka,http,csv,grpc}] [--version]
file [file ...]
Read and process pcap files using this nifty tool.
positional arguments:
file pcap file to read
optional arguments:
-h, --help show this help message and exit
--map {length,protocol}
enable a mapper with the given name. You can use this
option multiple times to enable more than one mappers
--sink {console,kafka,http,csv,grpc}
enable a sink with the given name. You can use this
option multiple times to enable more than one sinks
--version show program's version number and exit
Currently the pcap-processor reads only packets with IP or IPV6 layers. Feel free to modify the code based on your requirement. If you find any enhancement, please send a Pull Request.
pcap-reader relies on external command line tool: tshark and some Python modules.
Install tshark using the following command in Ubuntu and its derivatives:
sudo apt install tsharkInstall Python dependencies using the following command:
pip3 install -r requirements.txtThis tool is developed as part of my research project. If you are using this tool in your research, please cite the following paper:
Citation:
Loganathan, G., Samarabandu, J., & Wang, X. (2018). Sequence to Sequence Pattern Learning Algorithm for Real-time Anomaly Detection in Network Traffic. In 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) (CCECE 2018). Quebec City, Canada.
BibTex
@INPROCEEDINGS{Loga1805:Sequence,
AUTHOR="Gobinath Loganathan and Jagath Samarabandu and Xianbin Wang",
TITLE="Sequence to Sequence Pattern Learning Algorithm for Real-time Anomaly
Detection in Network Traffic",
BOOKTITLE="2018 IEEE Canadian Conference on Electrical \& Computer Engineering (CCECE)
(CCECE 2018)",
ADDRESS="Quebec City, Canada",
DAYS=13,
MONTH=may,
YEAR=2018,
KEYWORDS="Seq2Seq; Anomaly Detection",
ABSTRACT="Network intrusions can be modeled as anomalies in network traffic in which
the expected order of packets and their attributes deviate from regular
traffic. Algorithms that predict the next sequence of events based on
previous sequences are a promising avenue for detecting such anomalies. In
this paper, we present a novel multi-attribute model for predicting a
network packet sequence based on previous packets using a
sequence-to-sequence (Seq2Seq) encoder-decoder model. This model is trained
on an attack-free dataset to learn the normal sequence of packets in TCP
connections and then it is used to detect anomalous packets in TCP traffic.
We show that in DARPA 1999 dataset, the proposed multi-attribute Seq2Seq
model detects anomalous raw TCP packets which are part of intrusions with
97\% accuracy. Also, it can detect selected intrusions in real-time with
100\% accuracy and outperforms existing algorithms based on recurrent
neural network models such as LSTM."
}Read a pcap file and send all packets to Apache Kafka:
python3 -m pcap_processor --sink kafka samples/cicids_2017.pcapRead a pcap file, map protocols and write them to a CSV file:
python3 -m pcap_processor --map protocol --sink csv samples/cicids_2017.pcapMappers and sinks have their own properties. Please modify them in the relevant plugins/<file>.py.
For example, to change the output CSV file location, modify the self.path = "packets.csv" in pcap_processor/plugins/csv_sink.py.