{"title":"Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context","authors":"Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu","doi":"10.1109/TMC.2025.3525463","DOIUrl":null,"url":null,"abstract":"In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks’ operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3764-3778"},"PeriodicalIF":7.7000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10824696/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks’ operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.
期刊介绍:
IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.