article · Scientific Reports
Protecting smart city networks and Internet of Things environments from cyber threats requires reliable and efficient detection systems. Existing machine learning and deep learning approaches often suffer from high computational costs, high time complexity, and reduced security performance. To address these issues, an integrated cyborg intelligence security framework has been introduced. The system first applies a Quantized Identical Data Imputation mechanism for data preprocessing and normalisation. Next, it deploys a Conjugate Self-Organising Migration algorithm to achieve feature optimisation and dimensionality reduction, thereby increasing learning speed, search efficiency, and classification accuracy. Finally, a Reconciliate Multi-Agent Markov Learning algorithm predicts and categorises network intrusions into their specific classes. Tested against various cyber-threat datasets, the proposed architecture improves attack detection performance and overall efficiency relative to existing state-of-the-art models.
Smart cities rely on extensive networks of connected devices that are increasingly exposed to disruptive digital attacks. Standard detection tools often demand excessive computing power and time. Developing faster and more accurate intrusion detection mechanisms helps protect critical urban infrastructure, civic services, and public data integrity without overloading resource-constrained Internet of Things hardware.
This framework could support threat detection software for smart city infrastructure operators, municipal network administrators, and connected device providers. The technology currently appears to be at an applied research stage, validated on static cyber-threat datasets rather than tested in live operational environments. Commercial adoption would require extensive testing on real-time network traffic and integration into commercial network monitoring platforms.
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Ensuring the privacy and trustworthiness of smart city-Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.
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DOI: 10.1038/s41598-023-42257-0
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