Cybersecurity in Smart Cities using CCPOA-HDLM
Smart cities integrate advanced technologies to enhance urban living, but they also introduce severe cybersecurity challenges due to interconnected systems and large-scale data flows. Conventional security models are often inadequate in identifying multistep and evolving attacks, leading to high false alarms and compromised resilience. To address these issues, advanced artificial intelligence (AI) and machine learning (ML) solutions are increasingly being employed to secure smart environments. This research introduces the Cybersecurity using Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM), a novel approach designed to enhance detection, classification, and defense capabilities in smart city networks.
Cybersecurity Challenges in Smart Cities
Smart cities rely on interconnected infrastructures, including transportation, healthcare, energy grids, and communication systems. These networks are highly vulnerable to cyber threats such as intrusion, data breaches, and denial-of-service attacks. The traditional attack-defense frameworks fail to provide sufficient adaptability for modern multistep assaults, leaving systems prone to advanced persistent threats. Moreover, the limited recognition rules and high rates of false positives undermine the efficiency of current models, thereby demanding innovative solutions.
Machine Learning in Cybersecurity Detection
Machine learning has emerged as a promising technique for intrusion detection in complex environments. By identifying hidden patterns and anomalies in large datasets, ML methods help administrators recognize and mitigate threats more effectively than static rule-based systems. The integration of ML with hybrid deep learning models, such as CNNs and RNNs, enhances the capability to capture both spatial and temporal features of attacks, ensuring robust classification of cybersecurity events.
CCPOA-HDLM Methodology
The CCPOA-HDLM framework introduces a multi-stage process for robust cybersecurity. It begins with min-max normalization to prepare raw data for efficient processing. An Improved Salp Swarm Algorithm (ISSA) is then employed for optimal feature selection, reducing dimensionality while retaining critical attack characteristics. A hybrid Multi-Channel Convolutional Neural Network and Recurrent Neural Network (MCNN-RNN) is utilized for accurate detection and classification, while the Crested Porcupine Optimizer (CPO) tunes hyperparameters to enhance overall system performance.
Experimental Validation on UNSW-NB15 Dataset
To evaluate the effectiveness of the CCPOA-HDLM method, experiments were conducted using the UNSW-NB15 dataset, which contains a diverse set of modern attack types. The proposed model demonstrated a superior accuracy of 99.04%, outperforming existing approaches across multiple metrics. These results validate the robustness of the CCPOA-HDLM framework and highlight its potential in mitigating cyber threats within smart city environments.
Future Scope and Research Directions
While CCPOA-HDLM achieves state-of-the-art performance, further research could extend its applicability to real-time monitoring and large-scale deployments in smart cities. Exploring adaptive learning mechanisms, cross-domain attack detection, and integration with blockchain-based security solutions may enhance resilience against sophisticated cyberattacks. Additionally, addressing computational efficiency and scalability will be crucial to ensure its seamless integration into critical smart city infrastructures.
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