Insights into security and privacy towards fog computing evolution

Nowadays, the development and innovation of tennis have basically been integrated with the Internet, and the penetration of big data is accelerating the development of tennis. Collect data and statistics about tennis matches, analyze the key winning factors of the game, assign players’ gains and losses, and assign points. Fully understand the game’s tactics and various key technical functions of the player and enhance the influence of the game’s attention. Therefore, as the era of the Internet of Things is about to come, the development of sports events in my country should seize the opportunity of innovation in the application of Internet of Things technology, accelerate the process of upgrading informatization and intelligence, and achieve leapfrog development. This article is aimed at studying the use of the Internet of Things security technology, research, and design a monitoring system for the key technical characteristics of male tennis players. This paper proposes to develop a novel real-time video semantic analysis prototype system for tennis matches, taking the video from the monitoring perspective of tennis matches as the analysis object. The experimental results in this article show that from Wimbledon to the Australian Open, the Australian Open is very slow, and finally, to the French Open, the speed of the ball slows down and improves the accuracy of the tennis crisis system. The average level and the correct answer rate are 61.37%. Eventually, it rose from the highest 86.13% to 86.92%, and the highest was the lowest. It seems that a monitoring system for the basic technical characteristics of male tennis players is more feasible.

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Monitoring System of Key Technical Features of Male Tennis Players Based on Internet of Things Security Technology

Internet of Things (IoT) protection refers to the software field related to securing the Internet of Things and associated linked devices and systems. The IoT is a system of interconnected computers, sensors, actuators, or people on the World Wide Web (WWW). All these different devices have a unique identity in the IoT and must convey data across the network automatically. If computers are not adequately secured, allowing them to connect to the Internet exposes them to a range of serious vulnerabilities. Because the consequences of IoT failures are severe, it is necessary to observe and analyze security issues related to IoT. The prime goal of IoT security is to protect personal safety, while also guaranteeing and ensuring accessibility. In the context of IoT technology, the present study conducts a systematic literature review that analyzes the security problems associated with commercial and educational applications of home automation and details the technical possibilities of IoT with respect to the network layer. In this systematic review, we discuss how current contexts result in the inability of designers of IoT devices to enhance their cyber-security initiatives. Typically, application developers are responsible for training themselves to understand recent security advancements. As a result, active participation on the ridge scale with passive improvement can be achieved. A comparative analysis of the literature was conducted. The main objective of this research is to provide an overview of current IoT security research in home automation, particularly those using authentication methods in different devices, and related technologies in radio frequency identification (RFID) on network layers. IoT security issues are addressed, and various security problems in each layer are analyzed. We describe cross-layer heterogeneous integration as a domain of IoT and demonstrate how it can provide some promising solutions.

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Home Automation and RFID-Based Internet of Things Security: Challenges and Issues

The widespread significance of Android IoT devices is due to its flexibility and hardware support features which revolutionized the digital world by introducing exciting applications almost in all walks of daily life, such as healthcare, smart cities, smart environments, safety, remote sensing, and many more. Such versatile applicability gives incentive for more malware attacks. In this paper, we propose a framework which continuously aggregates multiple user trained models on non-overlapping data into single model. Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform. The LNN model analyzes various static and dynamic features of both malware and benign whereas the smart contract verifies the malicious applications both for uploading and downloading processes in the network using stored aggregated features of local models. In this way, the proposed model not only improves malware detection accuracy using decentralized model network but also model efficacy with blockchain. We evaluate our approach with three state-of-the-art models and performed deep analyses of extracted features of the relative model.

Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain

Although the Internet of Things (IoT) incorporates millions of heterogeneous devices to provide advanced intelligent services and has greatly impacted our lives over time, it has a huge blind spot since its design favors connectivity over security. Myriad efforts have been made to secure it, but it is still one of the most lucrative and often an easy target for attackers. IoT devices remain at higher risk of attack due to their intrinsic properties which include but are not limited to extreme heterogeneity, mostly plug-and-play nature, computational limitations, improper patch management, unnecessary open ports, default or no security credentials, and extensive use of reusable open-source software. To address these security concerns we need to thoroughly understand IoT devices’ vulnerabilities, associated attacks, and how criminal services can abuse these devices. In this article, we present recent advances in IoT security vulnerabilities, criminal services by empirically identifying major vulnerable IoT devices and cyber attacks exploiting them by cyber criminals. Additionally, we present mapping of vulnerabilities, criminal services, attacks, and potential solutions against such vulnerabilities and attacks. We have also presented different approaches in a tabular form for side-by-side comparison.

Advances in IoT Security: Vulnerabilities, Enabled Criminal Services, Attacks, and Countermeasures

The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

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Jungwoo Lee

Papers

Current Research Trends in IoT Security: A Systematic Mapping Study