With the exponential growth in the number of connected devices, in recent years there has been a paradigm shift toward mobile edge computing. As a promising edge technology, it pushes mobile computing, network control, and storage to the network edges so as to provide better support to computation-intensive Internet of Things (IoT) applications. Although it enables offloading latency-sensitive applications at the resource-limited mobile devices, decentralized architectures and diversified deployment environments bring new security and privacy challenges. This is due to the fact that, with wireless communications, the medium can be accessed by both legitimate users and adversaries. Though cloud computing has helped in substantial transformation of global business, it falls short in provisioning distributed services, namely, security of IoT systems. Thus, the ever-evolving IoT applications require robust cyber-security measures particularly at the network's edge, for widespread adoption of IoT applications. In this vein, the classic machine learning models devised during the last decade, fall short in terms of low accuracy and reduced scalability for real-time attack detection across widely dispersed edge nodes. Thus, the advances in areas of deep learning, federated learning, and transfer learning could mark the evolution of more sophisticated models that can detect cyberattacks in heterogeneous IoT-driven edge networks without human intervention. We provide a SecEdge-Learn Architecture that uses deep learning and transfer learning approaches to provided a secure MEC environment. Moreover, we utilized blockchain to store the knowledge gained from the MEC clusters and thereby realizing the transfer learning approach to utilize the knowledge for handling different attack scenarios. Finally, we discuss the industry relevance of the MEC environment.

中文翻译：

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物联网驱动的移动边缘计算的安全性：新范式、挑战和机遇


随着连接设备数量的指数级增长，近年来出现了向移动边缘计算的范式转变。作为一种前景广阔的边缘技术，它将移动计算、网络控制和存储推向网络边缘，为计算密集型物联网应用提供更好的支持。尽管它能够在资源有限的移动设备上卸载对延迟敏感的应用程序，但分散的架构和多样化的部署环境带来了新的安全和隐私挑战。这是因为，通过无线通信，合法用户和对手都可以访问该介质。尽管云计算帮助全球业务发生了重大变革，但它在提供分布式服务（即物联网系统的安全性）方面存在不足。因此，不断发展的物联网应用需要强大的网络安全措施，特别是在网络边缘，以广泛采用物联网应用。在这方面，过去十年中设计的经典机器学习模型在广泛分散的边缘节点上进行实时攻击检测时，准确性低且可扩展性降低。因此，深度学习、联邦学习和迁移学习领域的进步可能标志着更复杂模型的发展，这些模型可以在无需人工干预的情况下检测异构物联网驱动的边缘网络中的网络攻击。我们提供了 SecEdge-Learn 架构，该架构使用深度学习和迁移学习方法来提供安全的 MEC 环境。 此外，我们利用区块链来存储从MEC集群获得的知识，从而实现迁移学习方法，利用这些知识来处理不同的攻击场景。最后，我们讨论 MEC 环境的行业相关性。