Identification of Data Injection Attacks in Networked Control Systems Using Noise Impulse Integration The benefits of using Networked Control Systems (NCS) in the growing Industry 4.0 arenumerous, including better management and operational capabilities, as well as costs reduction.However, despite these benefits, the use of NCSs can also expose physical plants to new threatsoriginated in the cyber domainxe2x80x94such as data injection attacks in NCS links through which sensorsand controllers transmit signals. In this sense, this work proposes a link monitoring strategy toidentify linear timeinvariant (LTI) functions executed during controlled data injection attacksby a ManintheMiddle hosted in an NCS link. The countermeasure is based on a bioinspiredmetaheuristic, called Backtracking Search Optimization Algorithm (BSA), and uses white Gaussiannoise to excite the attack function. To increase the accuracy of this countermeasure, it is proposedthe Noise Impulse Integration (NII) technique, which is developed using the radar pulse integrationtechnique as inspiration. The results demonstrate that the proposed countermeasure is able toaccurately identify LTI attack functions, here executed to impair measurements transmitted bythe plant sensor, without interfering with the NCS behavior when the system is in its normaloperation. Moreover, the results indicate that the NII technique can increase the accuracy of the attackidentification.

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.