Snap4City Platform to Speed Up Policies

Increased attention is being given to Internet of things (IoT) network security due to attempts to exploit vulnerabilities. Security techniques protecting availability, confidentiality, and information integrity have intensified as IoT devices are viewed as gateways to larger networks by malicious actors. As an additional factor, the microservices‐based platforms have overtaken the deployment of applications that support smart cities; however, the distributed nature of these architectures heightens susceptibility to malicious network infrastructure use. These risks can result in disruptions to system functioning or data compromise. Proposed strategies to mitigate these risks include developing intrusion detection systems and utilizing machine learning to differentiate between normal and anomalous network traffic, indicating potential attacks. This article outlines the development and implementation of an intrusion detection system (IDS) using machine learning to detect online anomalies in network traffic. Comprising a traffic extractor and anomaly detector, the system employs supervised learning with various datasets to train models. The results demonstrate the effectiveness of the decision tree model in detecting traditional denial of service (DoS) attacks, achieving high scores across multiple metrics: an F1‐score of 98.08%, precision of 99.25%, recall of 96.96%, and accuracy of 99.62%. The random forest model excels in identifying slow‐rate DoS attacks, attaining an F1‐score of 99.85%, precision of 99.91%, recall of 99.80%, and accuracy of 99.88%.

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A Value Stream Mapping Approach to the Identification of Lean Management Opportunities for Off-Site Construction Production: A Case of Reinforced Concrete Slabs

Chen,

Guo,

Xue

2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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Off-site construction (OSC), including prefabrication and Modular-integrated Construction (MiC), is gaining popularity as a school of sustainable construction methods that can improve productivity, quality, and waste reduction. However, OSC projects face challenges related to technical integration, collaboration among stakeholders, and dynamic uncertainties. As a result, high-quality standards throughout the manufacturing process of OSC products are difficult to ensure, leading to costly rework, delays, and safety issues. This paper applies a value stream mapping (VSM) approach based on lean principles to OSC production for identifying lean management opportunities for off-site construction production. The case studied in this paper is reinforced concrete slabs. First, we employed a combination of field investigations and interviews to formalize the flow of materials and information for the case. Then, VSM processed the flow for the current state map, which highlights twelve opportunities to prioritize for slab production, e.g., the adoption of digital technology (VR, BIM) in information flows. The findings in value-added activities improvement of the opportunities demonstrate the potential of lean management in the slab production case. Furthermore, the VSM approach in this paper can identify the ‘wastes’ in lean theory, which are the control points of OSC production, for enhancing quality, efficiency, and resource utilization. The findings contribute to the existing body of knowledge by providing empirical evidence of the VSM approach to the identification of lean management opportunities for OSC production

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Snap4City Platform to Speed Up Policies

Cited by 4 publications

References 16 publications

Machine learning based models for detecting attacks in IoT systems

Machine learning based models for detecting attacks in IoT systems

Securing Microservices‐Based IoT Networks: Real‐Time Anomaly Detection Using Machine Learning

A Value Stream Mapping Approach to the Identification of Lean Management Opportunities for Off-Site Construction Production: A Case of Reinforced Concrete Slabs

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