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Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.
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A Machine Learning Approach for Combating Cyber Attacks in Self-Driving Vehicles
Self-driving vehicles are very susceptible to cyber attacks. This paper aims to utilize a machine learning approach in combating cyber attacks on self-driving vehicles. We focus on detecting incorrect data that are injected into the data bus of vehicles. We will utilize the extreme gradient boosting approach, as a promising example of machine learning, to classify such incorrect information. We will discuss in details the research methodology, which includes acquiring the driving data, preprocessing it, artificially inserting incorrect information, and finally classifying it. Our results show that the considered algorithm achieve accuracy of up to 92% in detecting the abnormal behavior on the car data bus.
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- Award ID(s):
- 1816112
- PAR ID:
- 10227657
- Date Published:
- Journal Name:
- Proceedings of IEEE Southeastcon
- ISSN:
- 1558-058X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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