TabNet-SFO: An Intrusion Detection Model for Smart Water Management in Smart Cities

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wahid Rajeh, Majed M. Aborokbah, Manimurugan S., Tawfiq Alashoor, Karthikeyan P.
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引用次数: 0

Abstract

As Smart City (SC) infrastructures evolve rapidly, securing critical systems like smart water management (SWM) becomes paramount to protecting against cyber threats. Enhancing the security, sustainability and execution of conventional schemes is considered significant in developing smart environments. Intrusion detection systems (IDS) can be effectively leveraged to realise this security objective in an Internet of Things (IoT)-based smart environment. This research addresses this need by proposing a novel IDS model called TabNet architecture optimised using Sailfish Optimisation (SFO). The TabNet-SFO model was specifically developed for SWM in SC applications. The proposed IDS model includes data collection, preprocessing, feature selection and classification processes. For training the model, this research used the CIC-DDoS-2019 dataset, and for evaluation, real-time data collected using an IoT-based smart water metre are used. The preprocessing step eliminates unnecessary features, cleans the data, encodes labels and normalises the applied datasets. After preprocessing, the TabNet model selects significant features in the dataset. The TabNet architecture was optimised using the SFO algorithm, which allows hyperparameter tuning and model optimisation. The proposed model demonstrated improved detection accuracy and efficiency on both the simulated and real-time datasets. The model attained a 98.90% accuracy, a 98.85% recall, a 98.80% precision, a 98.82% specificity and a 98.78% f1 score on the CIC-DDoS dataset and a 99.21% accuracy, a 99.02% recall, a 99.05% precision, a 99.10% specificity and a 99.18% f1 score on real-time data. Compared to existing models, the TabNet-SFO model outperformed all existing models in terms of performance metrics and validated its efficiency in detecting attacks.

Abstract Image

TabNet-SFO:面向智慧城市智慧水管理的入侵检测模型
随着智慧城市(SC)基础设施的快速发展,保护智能水管理(SWM)等关键系统对于防范网络威胁至关重要。提高传统方案的安全性、可持续性和执行力被认为对发展智能环境具有重要意义。入侵检测系统(IDS)可以有效地在基于物联网(IoT)的智能环境中实现这一安全目标。本研究通过提出一种名为TabNet架构的新型IDS模型来解决这一需求,该模型使用旗鱼优化(SFO)进行优化。TabNet-SFO模型是专门为SC应用中的SWM开发的。该模型包括数据采集、预处理、特征选择和分类等过程。为了训练模型,本研究使用了CIC-DDoS-2019数据集,并使用基于物联网的智能水表收集的实时数据进行评估。预处理步骤消除不必要的特征,清理数据,编码标签和规范化应用的数据集。经过预处理后,TabNet模型选择数据集中的重要特征。TabNet架构使用SFO算法进行了优化,该算法允许超参数调整和模型优化。该模型在模拟和实时数据集上都证明了更高的检测精度和效率。该模型在CIC-DDoS数据集上的准确率为98.90%,召回率为98.85%,精确度为98.80%,特异性为98.82%,f1得分为98.78%;在实时数据集上的准确率为99.21%,召回率为99.02%,精确度为99.05%,特异性为99.10%,f1得分为99.18%。与现有模型相比,TabNet-SFO模型在性能指标上优于所有现有模型,验证了其检测攻击的效率。
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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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