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DDOS Detection

Libraries Used

tshark [sudo apt-get install wireshark] pandas os numpy sklearn pickle sys time

Pipeline for training

  1. feature_extraction [python script] a. We use the tool tshark to extract required features from the .pcap files. b. TCP and UDP protocol packets are extracted into separate CSV files and combined at a later stage in the pipeline.

  2. feature_engineering [python script] a. The tcp and udp packets of the same session (each pcap file) are combined back into their original structure using the frame.number attribute to restore packet order integrity. The combined data is stored in a pandas data frame b. This data is then split into batches (batch size = 100) to analyze the packets in the form of clusters. c. To ensure proper variability of data in each cluster bootstrap sampling is applied to each batch. d. Summary statistics (such as mean, median etc) are calculated on the features of each cluster to summarise that cluster’s network profile.

  3. ml [python script] a. All the csv files containing batches of data are combined into one dataframe. b. Some features are dropped before running the dataframe through the machine learning model. c. RandomForestClassifier is used to train the data. d. The trained model is saved using pickle in batch_network_traffic_classifier.sav

Pipeline for Testing

ddosdetect [python script]

  1. Features are extracted.
  2. Feature engineering is done.
  3. Batches are formed.
  4. Classification of clusters is done.
  5. The benign and ddos_attack traffic is outputted in FINAL_OUTPUT.txt.

Coding Conventions

  • ddosdetect.py creates the folder TEMPORARY_FILES to store temporary .csv files, text files which contain the path to pcap files etc

  • feature_extraction.py creates the folders CSV_FILES/BENIGN and CSV_FILES/MALWARE to store the data extracted from the pcap files using tshark

  • feature_engineering.py creates the folders CSV_FILES_BATCH, CSV_FILES_PATH_LIST, PCAP_CSV_FILES to store temporary files as needed.

  • ml.py stores the trained Machine Learning model in batch_network_traffic_classifier.sav

Feature Extraction

  • In this work, the goal is to identify characteristics in network traffic that are able to distinguish the normal network behavior from DoS attacks.
  • For feature extraction we used the command line tool tshark which helps parse pcap files with ease and form CSVs of required fields. We extracted the following
    • Frame number- Helps mark the specific packets that have been marked as malicious in network traffic.
    • Source IP address - Helps monitor the packets that have been sent to and from the host. This is essential in the output file.
    • Destination IP address
    • Protocol used- Since a large number of DDoS attacks are carried over protocols such as ICMP and UDP. The protocol used is a worthy feature to use in the classifier.
    • TCP_SourcePort
    • TCP_DestPort
    • TCP_Flags
    • UDP_SourcePort
    • USP_DestPort
    • UDP_Flags

Feature Engineering

Summary statistics on some features were calculated to ensure the importance of features is correctly measured.

  • IP Protocol
  • IP source - Mean, Median, Var, Standard Deviation, Cross Variance, Rate of change
  • IP Destination - Mean, Median, Var, Standard Deviation, Cross Variance, Rate of change
  • Source Port - Mean, Median, Var, Standard Deviation, Cross Variance, Rate of change
  • Destination Port - Mean, Median, Var, Standard Deviation, Cross Variance, Rate of change
  • TCP Flags - Mean, Median, Var, Standard Deviation, Cross Variance, Rate of change