Security in edge-assisted Internet of Things: challenges and solutions

Abstract

The flourish of 5th generation wireless systems (5G) network has brought numerous benefits to the Internet of Things (IoT) with universal connectivity, improved data rate, and decreased latency. The development of IoT extends the computation-intensive applications from centralized servers to the edge of the network, promoting the paradigm of edge computing. Edge computing brings great assistance to IoT architecture by efficiently accomplishing tasks with lower latency, less energy consumption and reduced network bandwidth. Edge-assisted IoT emerges to provide location-aware services and offload computational tasks to edge nodes near the IoT devices. However, a series of security challenges still exist in edge-assisted IoT owing to the inherent vulnerabilities of edge nodes and sensitive nature of the collected data. Among the emerging and existing security schemes in IoT applications, security and energy consumption are two overriding yet conflicting requirements. The tradeoff between security and energy is critical to the sustainability of the IoT ecosystem but still lacks sufficient discussion. In this article, we investigate the security challenges in edge-assisted IoT and how the constraints in energy consumption can affect the design of security schemes. Specifically, we firstly present the architecture of edge-assisted IoT and its unique characteristics. Secondly, we identify the security threats in the edge-assisted IoT applications and the security-energy tradeoff during implementation. Thirdly, in a case study of distributed denial of service (DDoS) and malware injection attack, we propose a preliminary solution to address the conflict between security and energy requirements. Finally, we discuss some open issues and identify future research directions for security and energy efficiency in edge-assisted IoT.

Malicious Code Classification Method Based on Deep Residual Network and Hybrid Attention Mechanism for Edge Security

“…Many universities were infected and severely spreads to large public service areas such as airports, customs, and public safety networks [4]. In view of the weak security protection mechanism and limited computing resources of edge computing nodes, the detection and prevention of malicious code in the entire life cycle of edge computing is of great significance [5]. Malicious code classification is the key to preventing malicious code from running and improving information security and provides an important basis for malicious code detection, control, and removal.…”

Privacy security in control systems

“…Privacy issues in control systems are getting more and more attention from both industry and research. In recent years, information technology and artificial intelligence technology are being increasingly employed in emerging applications such as the Internet-of-Things [1], cloud-based control systems, smart buildings, autonomous vehicles, and 5G networks. The ubiquitous employment of such technologies provides more ways for an adversary to access sensitive information (e.g., eavesdropping on a communication channel, hacking into an information processing center, or colluding with participants in a system); therefore, the risk of privacy leakage is rapidly increasing.…”

Secure physical layer short-packet communication for 5G

“…另一方面, 未来物联网应用将涉及更多具有更高安全级别的用户隐私信息, 使其面临着比传统通 信系统更严峻的安全挑战 [10] . 如电子医疗保健、智能家居及智能交通等物联网服务会收集和管理用 户的私人信息, 遭受着被恶意攻击者窃听和篡改的风险 [11] , 一旦发生用户信息泄露将导致严重的后果. 以往通信系统主要依靠上层加密机制为信息传输提供安全保障, 然而在 IoT 系统中应用上层加密机制 将面临不可忽视的客观挑战.…”

“…首先, 未来 IoT 应用部署的海量终端将形成高度动态的异构网络, 给统 一的密钥管理、协商、分发和更新带来困难 [12] . 其次, 传统保密机制需要进行频繁的信令交互, 对于 以短促频发为特征的短包通信而言传输开销过大, 无法适应 IoT 节点资源受限的现状 [11,13,14] . 最后, IoT 节点通常使用轻量级保密协议, 以牺牲系统的可靠性甚至安全性为代价交换较低的资源需求 [15] .…”