With the exponential growth in the number of connected devices, recent years have seen a paradigm shift towards 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 the 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 classical 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 utilised blockchain to store the knowledge gained from the MEC clusters and thereby realising the transfer learning approach to utilise the knowledge for handling different attack scenarios. Finally, we discuss the Industry relevance of the MEC environment.
Mobile web browsing signifies accessing the content on web pages using a mobile device. It is common for Internet search engines to use keyword searching in which rank is assigned to each page based on several features. But it is an arduous task for a user to inscribe a keyword in such a delicate small mobile screen. A challenging research goal is the development of advanced web-based applications for mobile that can offer some amount of interactivity and adaptivity in order to support operators and users. This paper briefly introduces Automatic Adaptive System (AAS), a substantial adaptive system on the WWW for extracting the details related to the search. The proposed system builds an adaptive architecture for user specific mobile services. It intelligently processes the available web contents and accomplishes the user's demand related to the selected query. It delivers the specific results in a completely menu driven environment.
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