Hybrid computing framework security in dynamic offloading for IoT …

Securing IoT big data in smart homes with advanced cryptography and anomaly detection

The rapid growth of the Internet of Things (IoT) has revolutionized the way we interact with our living spaces, giving rise to the concept of smart homes. These intelligent ecosystems, powered by interconnected devices and sensors, offer unprecedented convenience, energy efficiency, and enhanced security. However, the integration of IoT with cloud computing has introduced new challenges, particularly in the realm of data security and privacy.

In the dynamic landscape of smart home technology, ensuring robust data security, privacy, and efficient processing capabilities has become paramount. Traditional security measures often fall short in addressing the unique requirements and constraints of IoT devices, necessitating the development of innovative solutions that can effectively safeguard sensitive information while optimizing system performance.

To address these pressing concerns, we present the Trusted IoT Big Data Analytics (TIBDA) framework – a comprehensive solution that seamlessly integrates edge, fog, and cloud computing to provide secure and efficient data processing for IoT-enabled smart homes. At the core of TIBDA is a robust hybrid cryptosystem that combines the strengths of Elliptic Curve Cryptography (ECC), Post-Quantum Cryptography (PQC), and Blockchain Technology (BCT) to fortify data confidentiality, integrity, and user privacy.

Recognizing the importance of anomaly detection in maintaining system security, TIBDA employs a thorough comparative analysis of prominent machine learning algorithms, including Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope. Our findings reveal that the Isolation Forest algorithm outperforms its counterparts, achieving an exceptional accuracy of 99.30% in detecting anomalies within the smart home environment.

Furthermore, TIBDA incorporates an innovative Artificial Neural Network (ANN)-based dynamic offloading algorithm that intelligently allocates computational tasks across the Edge, Fog, and Cloud components. This dynamic approach ensures optimal resource utilization, reduced response times, and enhanced system performance, ultimately delivering a seamless and secure user experience in smart home applications.

Challenges in IoT-enabled smart home systems

The integration of IoT-based home automation systems offers numerous benefits, but it also introduces several challenges that must be addressed to ensure successful implementation in real-world environments. These challenges include:

1. Resource management: IoT-powered home automation applications require substantial processing power, memory, and energy resources, leading to rapid battery depletion and reduced performance of other applications on the device. Effective resource management strategies are needed to optimize the use of these resources.

2. Security and privacy: IoT devices are inherently vulnerable to security breaches due to their limited computational resources and the open nature of their communication protocols. Ensuring data security and user privacy is paramount, requiring the implementation of robust encryption methods, secure authentication protocols, and regular software updates.

3. Network reliability: The seamless operation of home automation systems depends on reliable network connectivity. Network outages, latency, and bandwidth limitations can disrupt the communication between devices, leading to potential system failures. Addressing this challenge involves implementing redundant communication pathways and leveraging edge and fog computing to reduce dependency on centralized cloud services.

4. Interoperability: Real-world smart home environments often consist of diverse IoT devices from various manufacturers, each with its own communication protocols and standards. Ensuring interoperability among these devices is a significant challenge that requires the development and adoption of universal standards and protocols.

5. Cost and scalability: The initial cost of setting up a comprehensive IoT-based home automation system can be high, potentially limiting its accessibility to a broader audience. Additionally, as the number of connected devices increases, the system must be scalable to handle the growing data and processing demands.

6. Environmental impact: The deployment of numerous IoT devices in smart homes can contribute to electronic waste and energy consumption. Developing energy-efficient devices and promoting sustainable practices in manufacturing and disposal are crucial to mitigate the environmental footprint of home automation systems.

To address these challenges and ensure the successful implementation of secure and efficient IoT-powered smart home systems, a comprehensive framework that combines advanced cryptographic techniques, robust anomaly detection, and dynamic resource allocation is essential. The Trusted IoT Big Data Analytics (TIBDA) framework aims to provide a holistic solution to these pressing concerns.

The TIBDA framework: Securing IoT big data in smart homes

The Trusted IoT Big Data Analytics (TIBDA) framework is designed to create a scalable, secure, and efficient hybrid computing architecture that can manage massive amounts of data in smart home systems and enable multi-user access while maintaining network performance and security. At the core of TIBDA is a hybrid cryptosystem that integrates Elliptic Curve Cryptography (ECC), Post-Quantum Cryptography (PQC), and Blockchain Technology (BCT) to ensure user information privacy and confidentiality.

1. Hybrid Cryptosystem: TIBDA’s comprehensive security approach combines the strengths of various cryptographic techniques:

a. Elliptic Curve Cryptography (ECC): ECC is a public-key cryptography technique that provides efficient and robust encryption, ensuring secure communication and data exchange between IoT devices and the hybrid computing infrastructure.

b. Post-Quantum Cryptography (PQC): PQC algorithms are designed to be resistant to attacks by quantum computers, safeguarding sensitive data against emerging threats. TIBDA’s hybrid cryptosystem integrates PQC to future-proof the system’s security.

c. Blockchain Technology (BCT): Blockchain’s decentralized and immutable nature is leveraged by TIBDA to securely store and manage sensitive data and device identities, enhancing the overall security and integrity of the system.

1. Anomaly Detection: TIBDA employs a comprehensive comparative analysis of prominent anomaly detection algorithms, including Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope. Our research findings reveal that the Isolation Forest algorithm outperforms the others, achieving an exceptional accuracy of 99.30% in detecting anomalies within the smart home environment.

2. Malicious Activity Detection: In addition to anomaly detection, TIBDA utilizes machine learning classification algorithms, such as random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis, to effectively distinguish between malicious and non-malicious activities within the smart home system. The random forest algorithm exhibited the best performance, with an accuracy of 94.70%.

3. Dynamic Offloading: TIBDA incorporates an innovative Artificial Neural Network (ANN)-based dynamic offloading algorithm that intelligently allocates computational tasks across the Edge, Fog, and Cloud components. This dynamic approach ensures optimal resource utilization, reduced response times, and enhanced system performance, delivering a seamless and secure user experience in smart home applications.

The integration of these advanced security measures, anomaly detection techniques, and dynamic offloading strategies collectively form the TIBDA framework, which aims to address the pressing challenges faced by IoT-enabled smart home systems.

TIBDA: Enhancing security and performance in smart home systems

The TIBDA framework stands out in its ability to provide a comprehensive and effective solution for securing IoT big data in smart home environments. By leveraging the strengths of various cryptographic techniques, anomaly detection algorithms, and dynamic offloading strategies, TIBDA offers a robust and adaptable approach to safeguarding sensitive data and optimizing system performance.

1. Hybrid Cryptosystem: TIBDA’s integration of ECC, PQC, and BCT creates a multilayered security solution that ensures the confidentiality, integrity, and availability of data within the smart home ecosystem. The hybrid cryptosystem provides secure communication, encryption, and decryption processes, shielding sensitive information from unauthorized access and emerging threats, such as quantum attacks.

2. Anomaly Detection: The superior performance of the Isolation Forest algorithm in TIBDA’s anomaly detection capabilities enables the framework to swiftly identify and mitigate potential security threats, such as unauthorized access attempts or malicious activities. This robust anomaly detection mechanism serves as an essential safeguard, helping to maintain the overall integrity and reliability of the smart home system.

3. Malicious Activity Detection: TIBDA’s use of machine learning classification algorithms, with the random forest algorithm exhibiting the highest accuracy, empowers the system to effectively distinguish between malicious and non-malicious activities. This capability enhances the framework’s ability to detect and respond to security breaches, ensuring the smart home environment remains secure and resilient.

4. Dynamic Offloading: The ANN-based dynamic offloading algorithm within TIBDA optimizes the distribution of computational tasks across the Edge, Fog, and Cloud components, providing several key benefits:

a. Resource Utilization: The dynamic offloading strategy ensures efficient allocation of resources, minimizing waste and maximizing the overall utilization of the hybrid computing infrastructure.

b. Response Time: By intelligently offloading tasks to the appropriate computing layer, TIBDA achieves reduced response times, enhancing the system’s responsiveness and user experience.

c. Scalability: TIBDA’s dynamic offloading capabilities enable the framework to seamlessly scale to accommodate growing data volumes and user demands, ensuring the smart home system remains efficient and adaptable.

The combination of these innovative features sets TIBDA apart from other existing solutions in the smart home security landscape. By seamlessly integrating cutting-edge cryptography, advanced anomaly detection, and dynamic resource allocation, TIBDA delivers a robust and reliable framework that effectively addresses the security and performance challenges faced by IoT-powered smart home systems.

Comparative analysis and performance evaluation

To assess the efficacy of the TIBDA framework, we conducted a comprehensive comparative analysis against several prominent security approaches, including SAS-Cloud, SHCEF, AILBSM, MHE-IS-CPM, and PA. The evaluation focused on various performance metrics, such as response time, security, and system reliability.

1. Response Time: The TIBDA framework consistently outperformed the competing systems in terms of response time, demonstrating a reduction of 10-20% across varying user loads, device counts, and transaction volumes. This superior performance can be attributed to TIBDA’s efficient resource management strategies and its dynamic offloading capabilities, which enable the system to adapt to changing workloads and optimize resource utilization.

2. Security Performance: TIBDA’s security measures, which integrate ECC, PQC, and blockchain technology, resulted in consistently higher AUC (Area Under the Curve) values, indicating a 5-15% improvement in security performance compared to the other systems. This enhanced security posture can be attributed to TIBDA’s robust threat detection mechanisms and its ability to mitigate a wide range of security threats.

3. System Reliability: TIBDA’s uptime percentage, a measure of its trustworthiness, was found to be 10-12% greater than its competitors. This demonstrates TIBDA’s ability to maintain operational continuity and reliability, ensuring that the smart home system remains available and functional even in the face of potential disruptions or security incidents.

4. Anomaly Detection: The Isolation Forest algorithm employed by TIBDA achieved an exceptional accuracy of 99.30% in detecting anomalies within the smart home environment, significantly outperforming the other anomaly detection techniques, such as Local Outlier Factor, One-Class SVM, and Elliptic Envelope.

5. Malicious Activity Detection: TIBDA’s use of the random forest algorithm for distinguishing between malicious and non-malicious activities resulted in an accuracy of 94.70%, surpassing the performance of other classification algorithms, including k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis.

6. Dynamic Offloading: The ANN-based dynamic offloading model employed by TIBDA achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance compared to traditional static offloading approaches.

The results of this comprehensive analysis confirm TIBDA’s superiority in addressing the security, performance, and reliability challenges faced by IoT-powered smart home systems. By seamlessly integrating advanced cryptographic techniques, robust anomaly detection, and dynamic offloading strategies, TIBDA emerges as a compelling choice for securing and optimizing the operation of smart home environments.

Conclusion and future directions

The Trusted IoT Big Data Analytics (TIBDA) framework presented in this article offers a comprehensive solution for securing IoT big data in smart home systems. By combining the strengths of Elliptic Curve Cryptography (ECC), Post-Quantum Cryptography (PQC), and Blockchain Technology (BCT), TIBDA ensures the confidentiality, integrity, and availability of sensitive data within the smart home ecosystem.

The framework’s exceptional performance in anomaly detection, with the Isolation Forest algorithm achieving an accuracy of 99.30%, and its ability to effectively distinguish between malicious and non-malicious activities, with the random forest algorithm exhibiting a 94.70% accuracy, further reinforce TIBDA’s effectiveness in maintaining the overall security and reliability of the smart home system.

Moreover, the integration of an innovative Artificial Neural Network (ANN)-based dynamic offloading algorithm within TIBDA optimizes resource utilization, reduces response times, and enhances system scalability. This dynamic approach to workload allocation across the Edge, Fog, and Cloud components enables TIBDA to adapt to changing demands and deliver a seamless user experience.

Comparative analysis reveals that TIBDA consistently outperforms other prominent security approaches, such as SAS-Cloud, SHCEF, AILBSM, MHE-IS-CPM, and PA, in terms of response time, security performance, and system reliability. These findings underscore TIBDA’s potential to become a leading solution for securing and optimizing the operation of IoT-powered smart home systems.

As the IoT ecosystem continues to evolve, there are several avenues for future research and enhancements to the TIBDA framework:

1. Dataset Diversity: Expanding the range of datasets used in the evaluation process can further enhance the generalizability and robustness of the TIBDA framework, ensuring its effectiveness in diverse smart home scenarios.

2. Advanced Cryptographic Techniques: Exploring the integration of emerging cryptographic methods, such as homomorphic encryption and secure multiparty computation, can strengthen TIBDA’s data protection capabilities and align with evolving security standards.

3. Privacy-Preserving Techniques: Incorporating privacy-preserving techniques, such as differential privacy and federated learning, can help ensure the protection of user data and compliance with regulatory requirements.

4. Interoperability and Standardization: Addressing the challenge of interoperability by aligning TIBDA with industry standards and protocols can promote its widespread adoption and seamless integration within heterogeneous smart home ecosystems.

5. Real-World Deployment and Validation: Collaborating with industry partners to deploy and validate the TIBDA framework in real-world smart home environments can provide valuable insights, feedback, and opportunities for further refinement and optimization.

By continuously exploring these avenues for improvement, the TIBDA framework can remain at the forefront of secure and efficient IoT-powered smart home solutions, contributing to the advancement of sustainable, intelligent, and privacy-preserving living environments.