Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods
نویسندگان
چکیده
Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with hardware unit, allowing for computing processes to interact the physical world. However, this increased usage has also led security concerns them, as they allow potential attack vendors exploit possibilities of committing misconduct own benefit. It paramount importance these systems comprehensive mechanisms mitigate threats. typical vector malicious data supplied by compromised sensors are part CPSs. To combat vector, many secured through fault tolerance, including methods such checkpointing recover system. Looking at diverse nature attacks ever complexities, traditional approaches may not counter them efficiently, which creates vacuum be filled sophisticated state-of-the-art techniques. In paper, Deep Learning autoencoders, Support Vector Machines proposed secure CPSs against attacks. The networks applied trained normal profile devoid any data. Data collected from system’s specified intervals used form series input neural networks. compare analyze new detect anomalies, if there any. presence anomalous data, generate corrective action(s) states recording. Through detection effective improved addition providing protection sensors. Moreover, method securing opens up possibility further research showcasing
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ژورنال
عنوان ژورنال: Proceedings of the ... international conference on information warfare and security
سال: 2022
ISSN: ['2048-9870', '2048-9889', '2048-9897']
DOI: https://doi.org/10.34190/iccws.17.1.74