Development of Injection Attacks Toolbox in MATLABSimulink for Attacks Simulation in Industrial Control System Applications Modern Industrial Control Systems (ICS) introduce the terms such and Industry 4.0 or Industrial Internet of Things (IIOT) that led to interconnectivity of several smart devices in an industry ranging from simple sensors to complex machines. With the advantages of interconnectivity, there is an equivalent rise in exposure to threats or attacks. Such attacks on ICS are inevitable and there exists several examples. In order to protect ICS from such attacks, a proper securing mechanism is necessary. This security mechanism needs to get trained for all possible attacks on the industrial network. Relaying of real data for training is almost impossible as we canxe2x80x99t get the attack data to train and test the security mechanisms. For this purpose, we provide an injection attack toolbox developed in MATLABSimulink to insert manipulated data values into the plant data so that the security mechanism can understand the different behaviors of the possible attacks on ICS. The developed toolbox is freely available in GitHub and can be used for research purpose.

Framework for Detecting Control Command Injection Attacks on Industrial Control Systems (ICS) This paper focuses on the design and development of attack models on the sensory channels and an Intrusion Detection system (IDS) to protect the system from these types of attacks. The encodingdecoding formulas are defined to inject a bit of data into the sensory channel. In addition, a signal sampling technique is utilized for feature extraction. Further, an IDS framework is proposed to reside on the devices that are connected to the sensory channels to actively monitor the signals for anomaly detection. The results obtained based on our experiments have shown that the oneclass SVM paired with Fourier transformation was able to detect new or Zeroday attacks.