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README.md

AEIDS (Unsupervised Approach for Detecting Low Rate Attacks on Network Traffic with Autoencoder)

AEIDS is a prototype of anomaly-based intrusion detection system which works by remembering the pattern of legitimate network traffic using Autoencoder. The full paper of this approach (Unsupervised Approach for Detecting Low Rate Attacks on Network Traffic with Autoencoder) is available here

Dependencies:

  • Python 2.7
  • Pcapy
  • Keras
  • psycopg2 (for database access)
  • PostgreSQL 9.5

Installation:

  1. Clone this repository and install all necessary libraries and programs
  2. Create a database in PostgreSQL and import the schema in aeids.sql`
  3. Modify aeids.conf, put the location of your PCAP file in theroot\_directory variable. Put the name of the PCAP file in the training_filenamealong with the number of TCP connections to the server using this formatfilename:num_connections. See the examples config provided. Use wireshark or the counting` phase in AEIDS to get the number of TCP connections.
  4. Modify the database connection configuration in aeids.py. Find the open_conn() function.

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AEIDS is a prototype of anomaly-based intrusion detection system which works by remembering the pattern of legitimate network traffic using Autoencoder.

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