With the rapid growth of the internet of things (IoT) and the emergence of edge computing, new opportunities and challenges have arisen in the realm of cyber security. This work presents a comprehensive review of cyber security in IoT-based edge computing, aiming to shed light on the potential risks and vulnerabilities associated with this evolving paradigm. This work begins by highlighting the increasing integration of IoT devices and edge computing, emphasizing their combined potential to revolutionize various industries. However, this integration also introduces new attack surfaces and vulnerabilities, making robust cyber security measures imperative to safeguard critical systems and sensitive data. The work discusses an in-depth analysis of the unique security challenges that arise at the intersection of IoT and edge computing. It explores the vulnerabilities introduced by the distributed nature of edge computing, the resource-constrained nature of IoT devices, and the heterogeneity of the IoT ecosystem.
Top1. Introduction
The Internet of Things (IoT) has witnessed exponential growth in recent years, connecting billions of smart devices to the internet and enabling seamless data exchange and automation across various industries. Alongside this rapid expansion, edge computing has emerged as a transformative paradigm that complements the IoT by bringing computational capabilities closer to the data source, reducing latency and enhancing real-time processing efficiency (Mukherjee et al., 2019). The fusion of IoT and edge computing offers numerous benefits, such as improved response times, reduced data transmission costs, and enhanced scalability. However, the convergence of IoT and edge computing also introduces significant cyber security challenges (Liang et al., 2018). As the deployment of interconnected IoT devices proliferates, the attack surface for malicious actors widens, making these systems vulnerable to various cyber threats. Moreover, the decentralized and resource-constrained nature of edge computing introduces additional complexities for ensuring robust security measures. This work discusses into the realm of cyber security in IoT-based edge computing, aiming to shed light on the evolving threats and vulnerabilities associated with this amalgamation. By exploring the unique characteristics and functionalities of both IoT and edge computing, we aim to provide a comprehensive understanding of the security challenges that arise at their intersection.
1.1 Overview of IoT-Based Edge Computing
IoT-based edge computing refers to the integration of IoT devices and edge computing technologies to process and analyze data closer to the source, at the edge of the network. It combines the capabilities of IoT devices, such as sensors and actuators, with the computational power and storage capabilities of edge computing nodes or devices. Traditionally, IoT devices collect data and send it to centralized cloud servers for processing and analysis. However, this approach has limitations in terms of latency, bandwidth, and scalability, especially when dealing with large volumes of real-time data. Edge computing addresses these challenges by bringing computational resources closer to the devices generating the data, enabling faster response times, reduced network congestion, and improved privacy and security. In IoT-based edge computing, edge devices or gateways are deployed at the edge of the network, often in close proximity to IoT devices. These devices act as intermediaries between the IoT devices and the cloud (Dastjerdi et al., 2016; Fernández-Caramés & Fraga-Lamas, 2018; Suo et al., 2012), performing data preprocessing, filtering, and analysis tasks locally. This allows for real-time decision-making and immediate response to critical events without relying on a round-trip to the cloud.
1.2 Key Characteristics of IoT-Based Edge Computing
Few key characteristics of IoT-based edge computing are;
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Proximity: Edge devices are located close to IoT devices, reducing network latency and improving response times.
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Data processing and analytics: Edge devices perform data preprocessing, filtering, and analytics at the edge, enabling real-time information and immediate actions.
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Bandwidth optimization: By processing data locally, only relevant or summarized information is sent to the cloud, reducing bandwidth usage.
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Offline operation: Edge devices can continue to operate even when disconnected from the cloud, ensuring system functionality and data availability.
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Security and privacy: Edge computing can enhance security and privacy by keeping sensitive data local and reducing the exposure of data during transmission.
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Scalability: Edge computing allows for distributed processing and scalable deployments, accommodating the increasing number of IoT devices and their data (Alaba et al., 2017; Ray, 2018).
In summary, applications of IoT-based edge computing span various domains, including smart cities, industrial automation, healthcare, transportation, and agriculture. For instance, in a smart city context, edge devices can process sensor data from traffic cameras or environmental sensors in real-time to optimize traffic flow or detect environmental anomalies. In summary, IoT-based edge computing enables efficient data processing, real-time analytics, reduced latency, and improved scalability by bringing computational capabilities closer to IoT devices. It plays an important role in enabling the full potential of IoTs and supporting a wide range of applications that require low latency, real-time decision-making, and enhanced security and privacy.