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Electronics and Sustainable Communication systems (ICESC), 2020 international conference on

作者： Arvind Kumar Tushar Vyas Saleem Ahmed Nitin Girdharwal E. Vijayakumar Arivanantham Thangavelu MRIIRS Faridabad Haryana India Department of CSE JECRC University Jaipur Rajasthan India Oryx Universal College Doha Qatar Prestige Institute of Management & Research (PIMR) Indore Madhya Pradesh India KIT- Kalaignar Karunanidhi Institute of Technology Coimbatore Tamilnadu India Computer Engineering Dr. D. Y. Patil College of Engineering and Innovation Varale Talegaon Pune Maharashtra India

Users may easily deal with data overload concerns while navigating Online Social networks (OSN) through suggestion engines. In addition, it increases a number of privacy-related issues, particularly for users who would rather remain anonymous on OSNs. Furthermore, certain vendors of services sell consumer data to other organizations without the consumer’s awareness as a result of the exploitation of online consumer data, which undermines user confidence in the vendors of services. Such issues demand a solution that gives consumers of technology the independence and authority they sorely need over their data. As blockchain technology develops, numerous research organizations are working with distributed techniques to find a solution to the privacy invasion and hacking problem that OSN users face. A blockchain-based hybrid framework for confidentiality maintenance is proposed, to overcome these restrictions. To guarantee the confidentiality and anonymity of user data, two smart contracts, SCSGI and SCSTI are created. While the SCSTI retains information, the SCSGI controls transfer of user information. To identify rebellious nodes, a Graph Attention Network (GAT) was incorporated into the blockchain-enabled platform. The research and trial findings reveal that the suggested approach exemplifies efficacy and confidentiality of consumers in the system.

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Detection and Replay of distributed Denial of Service Attacks In Smart Cities Using a Hybrid Deep Learning Approach

Detection and Replay of Distributed Denial of Service Attack...

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Business Analytics for Technology and security (ICBATS), international conference on

作者： Muhammad Waseem Iqbal Ghassan F. Issa Muhammad Yousif Muhammad Atif Department of Software Engineering Superior University Lahore Pakistan School of Information Technology Skyline University College Sharjah UAE Department of Computer Science NCBAE Lahore. Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science NCBAE Lahore Pakistan

Infrastructure for smart cities is presently being built, mostly because of the (IoT) platform, which connects a wide variety of things by using the website's underlying infrastructure. As a result, it is possible to employ a uniform platform to automate the services that are delivered. Smart city infrastructure, on the other hand, is susceptible to cyberattacks owing to security flaws in IoT networks. Distributing denial of service (DDoS) and replay assaults, for example, violate the requirements for certification in smart cities. The lack of citations in this part backs up many of the statements it makes. Who needs a citation? Both assaults have the potential to substantially damage smart city infrastructure, which might also cost money and even result in human fatalities. This paper covers the creation of a blended deep- learning algorithm for replay and DDoS intrusion prevention on a growth-oriented smart city platform. This is made possible by combining proven machine-learning approaches with more recent advances in artificial intelligence. We simulate scattered denial of service and replay attacks on three datasets acquired from real-world smart cities to assess the efficacy of the proposed hybrid strategy on environmental, smart river, and smart soil dataset. The suggested model has shown excellent rates of accuracy. The ecosystem collection has an accuracy level of 98.37%, the smart riverbed dataset must have an accuracy of 98.13%, and the smart mud dataset must have an accuracy of 99.51%. The findings demonstrated that the proposed model outperformed past instances of evolutionary computation and machine learning techniques employed in the corpus of academic literature.

关键词： Analytical models Machine learning algorithms Smart cities Computational modeling Soil Probability distribution Real-time systems

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distributed Novelty Detection at the Edge for IoT Network security 21st

Distributed Novelty Detection at the Edge for IoT Network Se...

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21st international conference on Computational Science and Its Applications (ICCSA)

作者： Puhl, Luis Cassales, Guilherme Weigert Guardia, Helio Crestana Senger, Hermes Univ Fed Sao Carlos Sao Carlos Brazil

ISBN: (纸本)9783030869700;9783030869694

The ongoing implementation of the Internet of Things (IoT) is sharply increasing the number and variety of small devices on edge networks. Likewise, the attack opportunities for hostile agents also increases, requiring more effort from network administrators and strategies to detect and react to those threats. For a network security system to operate in the context of edge and IoT, it has to comply with processing, storage, and energy requirements alongside traditional requirements for stream and network analysis like accuracy and scalability. Using a previously defined architecture (IDSA-IoT), we address the construction and evaluation of a support mechanism for distributed Network Intrusion Detection systems based on the MINAS Data Stream Novelty Detection algorithm. We discuss the algorithm steps, how it can be deployed in a distributed environment, the impacts on the accuracy and evaluate performance and scalability using a cluster of constrained devices commonly found in IoT scenarios. The obtained results show a negligible accuracy loss in the distributed version but also a small reduction in the execution time using low profile devices. Although not efficient, the parallel version showed to be viable as the proposed granularity provides equivalent accuracy and viable response times.

关键词： Novelty detection Intrusion detection Data streams distributed system Edge computing Internet of Things

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IoT and Blockchain combined: For decentralized security 18

IoT and Blockchain combined: For decentralized security

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18th international conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th international conference on Future networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information Technology, SEIT 2021

作者： Abbassi, Younes Benlahmer, Habib Computer Sciences Department Hassan 2 University Casablanca Morocco

Blockchain technology, a version of distributed ledger technology, has been grabbing a huge amount of attention in fields beyond its roots in crypto-currencies: blockchain and finance, blockchain and logistics, blockchain and the Internet of Things (IoT), blockchain and cybersecurity, and the list goes on. Blockchain (there are actually many blockchains, and it's not limited to the technology that powers bitcoin), has been mentioned by virtually every research study firm as a rapidly accelerating evolution, and it's not just about financial services companies, the domain in which we first discussed blockchain outside of its cryptographic roots (i.e., FinTech or financial technologies). The converging of blockchain and the Internet of Things is actually on the table for many companies and there are existing deployments, solutions and initiatives in several areas outside of IoT and financial services as well. Combine blockchain and IoT, and you have two bigger-than-Internet businesses that need each other for many reasons that we explain in this IoT blockchain overview. © 2021 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Whale optimization algorithm with exploratory move for wireless sensor networks localization 19th

Whale optimization algorithm with exploratory move for wirel...

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19th international conference on Hybrid Intelligent systems, HIS 2019, and the 14th international conference on Information Assurance and security, IAS 2019

作者： Bacanin, Nebojsa Tuba, Eva Zivkovic, Miodrag Strumberger, Ivana Tuba, Milan Singidunum University Belgrade11000 Serbia

ISBN: (纸本)9783030493356

In the modern era, with the development of new technologies, such as cloud computing and the internet of things, there is a greater focus on wireless distributed sensors, distributed data processing and remote operations. Low price and miniaturization of sensor nodes have led to a large number of applications, such as military, forest fire detection, remote surveillance, volcano monitoring, etc. The localization problem is among the greatest challenges in the area of wireless sensor networks, as routing and energy efficiency depend heavily on the positions of the nodes. By performing a survey of computer science literature, it can be observed that in the wireless sensor networks localization domain, swarm intelligence metaheuristics have generated compelling results. In the research proposed in this paper, a modified/improved whale optimization swarm intelligence algorithm, that incorporates exploratory move operator from Hooke-Jeeves local search method, applied to solve localization in wireless networks, is presented. Moreover, we have compared the proposed improved whale optimization algorithm with its original version, as well as with some other algorithms that were tested on the same model and data sets, in order to evaluate its performance. Simulation results demonstrate that our presented hybridized approach manages to accomplish more accurate and consistent unknown nodes locations in the wireless networks topology, than other algorithms included in comparative analysis. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Swarm intelligence

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Blockchain 3.0 applications survey

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JOURNAL OF PARALLEL AND distributed COMPUTING 2020年第0期138卷 99-114页

作者： Maesa, Damiano Di Francesco Mori, Paolo Univ Cambridge Dept Comp Sci & Technol Cambridge England CNR Ist Informat & Telemat Pisa Italy

In this paper we survey a number of interesting applications of blockchain technology not related to cryptocurrencies. As a matter of fact, after an initial period of application to cryptocurrencies and to the financial world, blockchain technology has been successfully exploited in many other different scenarios, where its unique features allowed the definition of innovative and sometimes disruptive solutions. In particular, this paper takes into account the following application scenarios: end-to-end verifiable electronic voting, healthcare records management, identity management systems, access control systems, decentralized notary (with a focus on intellectual property protection) and supply chain management. For each of these, we firstly analyse the problem, the related requirements and the advantages the adoption of blockchain technology might bring. Then, we present a number of relevant solutions proposed in the literature both by academia and companies. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Blockchain DLT Smart contracts distributed Applications

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DadGAN: DDOS Anomaly Detection using Generative Adversarial Network 5

DadGAN: DDOS Anomaly Detection using Generative Adversarial ...

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5th IEEE international conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2021

作者： Girish, L. Rao, Sridhar Kn Renukananda, T. Vidyashree, K. Hemashree, R. Channabasaveshwara Institute of Technology Open Source Club Department of Computer Science and Engineering Karnataka Gubbi India

ISBN: (纸本)9781665406109

Software Defined networks (SDN) were designed to simplify network management by allowing users to monitor and administer the entire network from a single location. In many data centre network environments today, SDN is widely *** the other side, emerging technologies can result in a slew of vulnerabilities and threats, which manufacturers are currently dealing with. As the number of devices grows, access and backbone layer control becomes more difficult, as a consequence, it raises security worries about network assaults like distributed Denial of Service (DDoS). DDoS has been a substantial threat to the Internet, because DDoS traffic seems to be normal traffic, it can cause enormous financial loss to businesses and governments. This paper strongly encourages the use of dadgan, a supervised anomaly detection approach built on Generative Adversarial networks (GANs). DadGAN is trained with generator and discriminator loss to allow for effective anomaly detection in software-defined networks. To demonstrate the effectiveness and generalisation of our technique, various effective performance indicators such as confusion metrics, precision, recall, and F1 score are used. According to the results, our methodology is capable of detecting anomalies, particularly point anomalies. © 2021 IEEE.

关键词： Generative adversarial networks

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Research on Routing Algorithm of mobile wireless packet switching computer network based on deep learning 4

Research on Routing Algorithm of mobile wireless packet swit...

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4th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2021

作者： Guan, Zhicong Guangdong Business and Technology University Zhaoqing526020 China

ISBN: (纸本)9781665428392

With the rapid development of wireless sensor networks in various fields, its security has become the focus of attention. The application of computer network technology is becoming more and more common, not only in production, but also gradually into people's daily life with the improvement of living standards. The development of computer network also brings new opportunities and challenges to the development of network routing. Network routing is responsible for the exchange of information. Therefore, it is very important to improve the utilization of network resources. Focusing on improving the high-efficiency utilization of energy, optimize cluster head election and routing algorithm to build routing cake to maximize the time of network life cycle. distributed computer systems, routers, switches and other fields. Wireless sensor network is a wireless network system composed of a large number of micro sensor nodes. It has become a research hotspot in the information field because of its advantages of self-organization, flexible network layout, wireless multi hop connection between nodes, and collaborative monitoring of dynamic data. This paper mainly studies the load balancing routing algorithm of direct connected networks, and proposes a new load balancing routing algorithm. Through the simulation of the algorithm, it is found that the performance of the algorithm is better than the traditional routing algorithm in the same network simulation environment. © 2021 IEEE.

关键词： Routing algorithms

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Internet of Things-Based Middleware Against Cyber-Attacks on Smart Homes using Software-Defined Networking and Deep Learning 2

Internet of Things-Based Middleware Against Cyber-Attacks on...

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2nd international conference on Computational Methods in Science and Technology, ICCMST 2021

作者： Tawfik, Mohammed Al-Zidi, Nasser M. Alsellami, Belal Al-Hejri, Aymen M. Nimbhore, Sunil Dr. Babasaheb Ambedkar Marathwada University Department of Computer Science & It Aurangabad India Albaydha University Faculty of Administrative and Computer Sciences Albaydha Yemen

ISBN: (纸本)9781665458665

Internet of Things (IoT) devices are expected to number about 3.5 billion by 2023.;a tremendous amount of Internet of Things (IoT) data that is generating (IoT) devices is estimated to exceed 79.4 zettabytes by 2025. security challenges will become an increasingly significant issue, especially in smart homes that depend entirely on IoT devices. Due to the weak infrastructure of the IoT, it is vulnerable to various types of cyber-attacks. The most common IoT attacks are distributed denial of service (DDoS). Most traditional security solutions, like intrusion detection systems (IDS), cannot detect most attacks. Complexity is being hidden by a new paradigm that recently arose, called the software-defined system that is brings a significant change to the networking industry, a great solution for mitigation of attacks that can adopt deep learning technique to encounter cyber-attacks based on the attack behavior and by filtering normal and attack traffic by using well-defined rules. This paper proposed a system by suggesting middleware that can help mitigate or prevent various attacks on IoT on smart home environment. Machine learning has included in the middleware to provide automatic protection against cyber-attacks on IoT networks. A promising approach to protecting real-time, highly accurate attacks on SDN-managed IoT networks has been proposed. This middleware allows IoT devices to efficiently handle evolving security threats dynamically and adaptively without impacting the IoT devices. © 2021 IEEE.

关键词： Internet of things

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A Robust Federated Learning Approach for Combating Attacks Against IoT systems Under Non-IID Challenges

A Robust Federated Learning Approach for Combating Attacks A...

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international conference on Smart Communications and Networking (SmartNets)

作者： Eyad Gad Zubair Md Fadlullah Mostafa M. Fouda Department of Computer Science University of Western Ontario London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complex-ities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly, this is evident within resource-constrained and security-sensitive environments such as those encountered in networks associated with the Internet of Things (IoT), Federated Learning has emerged as a promising remedy to these challenges by decentralizing model training to edge devices or parties, effectively addressing privacy concerns and resource limitations. Nevertheless, the presence of statistical heterogeneity in non-Independently and Identically distributed (non-IID) data across different parties poses a significant hurdle to the effectiveness of FL. Many FL approaches have been proposed to enhance learning effectiveness under statistical heterogeneity. However, prior studies have uncovered a gap in the existing research land-scape, particularly in the absence of a comprehensive comparison between federated methods addressing statistical heterogeneity in detecting loT attacks. In this research endeavor, we delve into the exploration of FL algorithms, specifically FedAvg, FedProx, and Scaffold, under different data distributions. Our focus is on achieving a comprehensive understanding of and addressing the challenges posed by statistical heterogeneity. In this study, We classify large-scale loT attacks by utilizing the CICloT2023 dataset. Through meticulous analysis and experimentation, our objective is to illuminate the performance nuances of these FL methods, providing valuable insights for researchers and practitioners in the domain.

关键词： Training Accuracy Federated learning Scalability Computational modeling Robustness Internet of Things

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