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Machine learning for intrusion detection in industrial control systems:challenges and lessons from experimental evaluation

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Cybersecurity 2021年第1期4卷 415-426页

作者： Gauthama Raman M.R. chuadhr. mujeeb ahmed Aditya Mathur iTrust Singapore University of Technology and Design(SUTD)8 Somapah RdSingapore 487372Singapore University of Strathclyde 16 Richmond StGlasgow G11XQUnited Kingdom

Gradual increase in the number of successful attacks against Industrial Control Systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process *** this end,a class of anomaly detectors,created using data-centric approaches,are gaining *** machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an *** use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and *** the advantages,there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale *** this work,we enumerate and discuss such *** presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.

关键词： Industrial control systems ICS security Machine learning Intrusion detection Testbed and experimental Study

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AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

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Computers, Materials & Continua 2024年第9期80卷 4085-4110页

作者： Syed Kamran Haider Abbas ahmed Noman mujeeb Khan Ali Nauman Sung Won Kim College of Internet of Things(IoT)Engineering Hohai UniversityChangzhou213001China Department of Electrical and Electronics Engineering Beaconhouse International CollegeIslamabad44000Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongsan38428Republic of Korea

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

关键词： UAV trajectory clustering next-generation wireless sensor network(NGWSN) energy efficiency mobile sink

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