Skip to content

Implementations of machine learning for anomaly detection in industrial IoT networks.

Notifications You must be signed in to change notification settings

jackw99/Anomaly-Detection-with-ML

Repository files navigation

Anomaly-Detection-with-ML

Implementations of machine learning for detection of common attacks in industrial IoT networks. Offline Classification of common attacks.

Attack 1: False-Data Injection Attacks

  • Attacker will inject false data (e.g. tampering with readings of sensors in a network) to try and damage the network
  • this causes down time and costs for the owner of the network to find and rectify the issue

Attack 2: Denial of Service Attacks (DoS)

  • Attacker tries to prevent normal operating functions of a network by dsirupting the normal flow of the network
  • this can be done by sending numerous false values into the network to throw off sensors

Attack 3: Replay Attack

  • Attacker chooses a valid data transmission to continously send through the network
  • Attempts to increase network congestion and render network useless

About

Implementations of machine learning for anomaly detection in industrial IoT networks.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages