jackw99 / Anomaly-Detection-with-ML

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

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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

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


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