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Dear Colleagues,

In recent years, research on smart cities has received a great deal of attention from the research community, interested in improving efficiency across a variety of services. The development and deployment of novel and existing data mining and machine learning techniques can have huge benefits for smart cities, especially for the further improvement of operational efficiency. Data mining and machine learning techniques play an important role in the development of various data-driven intelligent technologies for smart cities. The main goal of this Special Issue is to explore novel and existing data-driven intelligent technologies for smart cities. Papers within this Special Issue should focus on challenges, limitations, opportunities and potential impact for smart cities from practical, experimental or theoretical perspectives. Papers describing mitigation strategies concerning challenges and limitations related to smart cities are also for submission.

Dr. Md Anisur Rahman
Prof. Dr. Loveleen Gaur
Dr. Saurav Mallik
Guest Editors

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Keywords

smart city
data mining
machine learning
intelligent system
data-driven decision
knowledge discovery

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16 pages, 753 KiB

Open AccessArticle

Cybersecurity in Smart Cities: Detection of Opposing Decisions on Anomalies in the Computer Network Behavior

by Danijela Protic, Loveleen Gaur, Miomir Stankovic and Md Anisur Rahman

Electronics 2022, 11(22), 3718; https://doi.org/10.3390/electronics11223718 - 13 Nov 2022

Cited by 6 | Viewed by 1638

Abstract

The increased use of urban technologies in smart cities brings new challenges and issues. Cyber security has become increasingly important as many critical components of information and communication systems depend on it, including various applications and civic infrastructures that use data-driven technologies and computer networks. Intrusion detection systems monitor computer networks for malicious activity. Signature-based intrusion detection systems compare the network traffic pattern to a set of known attack signatures and cannot identify unknown attacks. Anomaly-based intrusion detection systems monitor network traffic to detect changes in network behavior and identify unknown attacks. The biggest obstacle to anomaly detection is building a statistical normality model, which is difficult because a large amount of data is required to estimate the model. Supervised machine learning-based binary classifiers are excellent tools for classifying data as normal or abnormal. Feature selection and feature scaling are performed to eliminate redundant and irrelevant data. Of the 24 features of the Kyoto 2006+ dataset, nine numerical features are considered essential for model training. Min-Max normalization in the range [0,1] and [−1,1], Z-score standardization, and new hyperbolic tangent normalization are used for scaling. A hyperbolic tangent normalization is based on the Levenberg-Marquardt damping strategy and linearization of the hyperbolic tangent function with a narrow slope gradient around zero. Due to proven classification ability, in this study we used a feedforward neural network, decision tree, support vector machine, k-nearest neighbor, and weighted k-nearest neighbor models Overall accuracy decreased by less than 0.1 per cent, while processing time was reduced by more than a two-fold reduction. The results show a clear benefit of the TH scaling regarding processing time. Regardless of how accurate the classifiers are, their decisions can sometimes differ. Our study describes a conflicting decision detector based on an XOR operation performed on the outputs of two classifiers, the fastest feedforward neural network, and the more accurate but slower weighted k-nearest neighbor model. The results show that up to 6% of different decisions are detected. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Technologies for Smart Cities)

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