The rapid proliferation of mobile Internet of Things (IoT) devices with inadequate security measures has elevated security to a critical concern. Researchers have proposed various systems for vulnerability detection based on conventional frameworks. However, these approaches often face challenges, such as high computational costs, limited storage capacity, and slow response times. To ensure robust protection against cyberattacks, modern security solutions must continuously monitor and analyze historical data across the IoT network.
This paper introduces a distributed security framework for IoT networks, leveraging software-defined networking (SDN), blockchain, and edge computing to efficiently detect and mitigate IoT-based attacks. In the proposed framework, SDN facilitates network-wide data monitoring and analysis, enabling effective attack detection. Blockchain technology ensures decentralized and tamper-resistant attack identification, addressing potential vulnerabilities. Meanwhile, the edge computing paradigm enables real-time attack detection at the network edge, ensuring timely alerts.
An experimental evaluation of the proposed framework demonstrates its superiority over traditional approaches in terms of detection accuracy (98.7%), false positive rate (1.2%), and response time (101.1 ms), highlighting its effectiveness in securing IoT networks.
With the ability to enable autonomous operations and communications, IoT technology plays a vital role in facilitating and advancing new services in daily human lives. IoT technology has found widespread application across numerous industries for effective resource assessment and ubiquitous data sensation due to the proliferation of sensor technologies. An estimated 76.88 billion devices will be linked to the internet by 2025. As the number of connected devices continues to grow, the urgency of safeguarding them from malicious cyber activity grows significantly.
IoT security issues are significantly more severe than traditional networks because attackers can control and harm vital infrastructures, including critical sensors and moving vehicles. A compromised device or the entire IoT network might be subject to privacy breaches if misused by an attacker.
The proposed BSIN framework utilizes a multi-tiered architecture that consists of sensing, edge, and cloud layers. The sensing layer includes various smart devices and sensory nodes that monitor public infrastructures. Data collected in the sensing layer is then transmitted to the edge layer, where SDN-specific switches analyze it before feeding into the cloud-based controller.
By leveraging SDN’s capability for centralized control alongside edge computing’s low-latency processing, the BSIN framework significantly reduces response times to potential attacks. In this new approach, decentralized attack detection facilitates secure data exchanges between edge nodes, ensuring both privacy and reliability.
This distributed framework not only addresses the common weaknesses associated with centralized solutions but also enhances resilience and robustness in IoT network security. The experimental results reveal that the BSIN framework outperforms traditional centralized and distributed models when it comes to both detection accuracy and response efficiency.
In conclusion, the BSIN framework stands out as a promising approach for securing IoT networks against increasing cyber threats. By utilizing a combination of software-defined networking, edge computing, and blockchain technology, the research suggests a powerful defense mechanism capable of adapting to the rapidly evolving digital landscape.
