Utilising Flow Aggregation to Classify Benign Imitating Attacks

This work aims to introduce new features based on a higher level of abstraction of network traffic, called Flow Aggregation features. In this repository, two features are extracted: Number of flows, Source ports delta.

Citation

To be updated.

General Parameters

Argument	Usage	Default	Values and Notes
--normal_path	CICIDS2017 Dataset Monday File Path	csv_files/biflow_Monday-WorkingHours_Fixed_Hour_0.csv
--attack_paths	CICIDS2017 Dataset Attack Paths	csv_files/biflow_Wednesday-WorkingHours_Slowhttptest.csv	Comma separated
--drop_aggregation	Whether or not to drop flow aggregation features	0	0/1
--slice_normal	Whether or not to use a portion of normal file	0	0/1
--slice_attacks	Whether or not to use portion(s) of attack file(s)	0	0/1 (comma separated)
--slice_normal_percent	Percentage of the portion to use from normal file	0
--slice_attacks_percent	Percentage of the portion(s) to use from attack file(s)	0	Comma separated
--normal_slice_no	Slice number to use when slicing the normal file	0
--slice_attacks_number	Slice number(s) to use when slicing the attack files	0	Comma separated
--slice_attacks_number	Slice number(s) to use when slicing the attack files	0	Comma separated
--number_of_features	Number of RFE features to print	5	Only used in print_features script
--output	The output file name	result.csv
--choose_features	Whether or not to run RFE	0	0/1 (check selected_features argument)
--selected_features	The features to use during training	'fwd_mean_pkt_len, bwd_mean_pkt_len, fwd_min_pkt_len, bwd_min_pkt_len, fwd_max_pkt_len,num_src_flows, src_ip_dst_prt_delta'

How to Run the repository:

Clone this repository.
run pcap_parser.py [pcap file path] run print_features [specify the parameters as required] run flow_aggregation.py [specify the parameters as required]

• The output of pcap_parser will be saved as 'pcap_file_name.csv'.
• Print features script will display the RFE ranked features in order (slicing in parameters is only used if set, default is to use the whole files).
• Flow aggregation script will output the classification accuracy and the confusion matrices for a 5-fold cross validation.