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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhingra, Lovish Vinithra Banu, T. Goyal, Shobhit Hannah Jessie Rani, R. Suruchi, G. Hemelatha, S. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Electrical and Electronics Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India

ISBN: (纸本)9798350370249

software program-described networking (SDN) is an emerging technology that promises to revolutionize the manner networks are controlled, controlled and monitored. SDN lets in for the integration of various network elements and enables to automatism the community control system, resulting in an agile and flexible community. This paper explores the capability of SDN in cell networks, highlighting the advantages of SDN, in addition to the demanding situations that want to be addressed. We gift effects from our cell network experiments which illustrate the electricity of SDN in mobile networks;in particular, it's potential to streamline the network configuration method. Finally, we speak the results of enforcing SDN in cell networks, as well as destiny research instructions in this region. © 2024 IEEE.

关键词： Community Configuration Elements Enforcing Explores Method Networks Speak Various

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Security in the World of Internet of Things (IoT): Overcoming Challenges 4

Security in the World of Internet of Things (IoT): Overcomin...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gannavaram, Venkat Tulasi Krishna Desu, Sravya Jujjuru, Venkata Rohith Renikunta, Sri Vatsa Venkata Tejaswi Gajula, Arun Kumar Reddy, Ganta Raghotham School of Electrical Computer and Energy Engineering Arizona State University Tempe United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Kakatiya Institute of Technology and Science Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350360165

Internet of Things (IoT) is turning out to be more prominent in our everyday existence, with smart home appliances and industrial control systems serving as examples. But the proliferation of IoT devices and internet connectivity increased the potential of security breaches. Botnet attacks, injection attacks, Domain Name Systems (DNS) spoofing attacks, firmware assaults, zero-day attacks, brute- force attacks, and cryptographic attacks may be used against IoT devices. These assaults may result in the loss of data, illegal access, and even physical damage to infrastructure and people. There are various potential courses of action. Machine Learning subpart of Artificial Intelligence have the capacity for finding and prevent dangers in real time. With blockchain technology, an IoT network can have a secure and unbreakable a record of every exchange and data transfers. Hardware-based security techniques, such as secure boot and trusted execution environments, may protect IoT devices against attacks that seek to compromise the underlying hardware or firmware. Standardizing security procedures may ensure that all devices can work together safely and achieve a given degree of security. The use of privacy-enhancing technology, IoT data may be kept confidential and sensitive information disguised. © 2024 IEEE.

关键词： Firmware

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Comparison of Handover Latency Using Mobility Protocols for Wi-MAX Networks 15

Comparison of Handover Latency Using Mobility Protocols for ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Ravivarman, G. Reddy, B. Karthika, K. Tusha Vinay Kumar, S.B. Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Quantum University Research Center Quantum University India Karnataka Bangalore India

ISBN: (纸本)9798350370249

Due to the growing call for for wireless facts offerings, handover latency should be minimized a good way to ensure consumer revel in. so as to reduce handover latency, one-of-a-kind mobility protocols are to be had for We-MAX networks, including fast Base Station Switching (FBSS), Dynamic pleasant of carrier (Duos), and greater dispensed Channel get admission to (EDCA). This paper offers a comparative study of handover latencies for these three mobility protocols. The research was carried out the usage of a Web-MAX network simulator to simulate numerous scenarios. results show that EDCA was discovered to be the first-class in phrases of handover latency, with an average latency of 1.18 ms, at the same time as fast Base Station Switching and Duos had a latency of 6.02 ms and 13.43 ms respectively. Additionally, the effects showed that EDCA decreased the handover latency by using 14.4%, as compared to FBSS and 39.2% in comparison to Duos. The look at additionally suggests that EDCA is extra suitable for handover in We-MAX networks, due to its low latency and high performance. ©2024 IEEE.

关键词： Wimax

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： computer viruses

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Rethinking Federated Learning: An Adversarial Perspective on Global vs. Local Learning for Load Forecasting

Rethinking Federated Learning: An Adversarial Perspective on...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Manzoor, Habib Ullah Shabiar, Attia Nguyen, Dinh C. Mohjazi, Lina Kaushik, Aryan Zoha, Ahmed University of Glasgow James Watt School of Engineering United Kingdom Ghulam Ishaq Khan Institute Topi Pakistan University of Alabama in Huntsville Department of Electrical and Computer Engineering United States Manchester Metropolitan University Department of Computing & Mathematics United Kingdom

ISBN: (纸本)9798331507428

Resilient federated learning (FL) systems are essential for accurate load forecasting, especially when under adversarial attacks. Since these systems aggregate decentralized data from various sources, they are particularly vulnerable to attacks that can undermine forecast accuracy and reliability. To enhance robustness in load forecasting, our study investigates methods for strengthening FL systems by optimizing the balance between global and local learning processes. This paper explores the trade-offs between global and local learning in federated load forecasting under adversarial conditions. We develop a neural network framework tailored for federated short-term load forecasting and assess its performance against model poisoning attacks. Our experiments demonstrate that increasing the number of local training epochs while reducing global communication rounds can significantly enhance model robustness. Specifically, when local epochs are increased from 1 to 10 and global epochs are decreased from 1000 to 100, the average client Mean Absolute Percentage Error (MAPE) decreases from 92.3 % to 4.3 % under attack conditions. This improvement stems from a reduced attack surface and the concept of catastrophic forgetting, where local models gradually mitigate adversarial effects through extended training on authentic data, providing valuable insights for the design of secure and efficient distributed energy forecasting systems. © 2024 IEEE.

关键词： Cyber attack Cyber security Distributed learning Load forecasting

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Circuit Lower Bounds for Low-Energy States of quantum Code Hamiltonians 13

Circuit Lower Bounds for Low-Energy States of Quantum Code H...

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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Anshu, Anurag Nirkhe, Chinmay Simons Institute for the Theory of Computing BerkeleyCA United States Challenge Institute for Quantum Computation University of California BerkeleyCA United States Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

ISBN: (纸本)9783959772174

The No Low-energy Trivial States (NLTS) conjecture of Freedman and Hastings [27] - which posits the existence of a local Hamiltonian with a super-constant quantum circuit lower bound on the complexity of all low-energy states - identifies a fundamental obstacle to the resolution of the quantum PCP conjecture. In this work, we provide new techniques, based on entropic and local indistinguishability arguments, that prove circuit lower bounds for all the low-energy states of local Hamiltonians arising from quantum error-correcting codes. For local Hamiltonians arising from nearly linear-rate or nearly linear-distance LDPC stabilizer codes, we prove super-constant circuit lower bounds for the complexity of all states of energy o(n). Such codes are known to exist and are not necessarily locally-testable, a property previously suspected to be essential for the NLTS conjecture. Curiously, such codes can also be constructed on a two-dimensional lattice, showing that low-depth states cannot accurately approximate the ground-energy even in physically relevant systems. © Anurag Anshu and Chinmay Nirkhe;licensed under Creative Commons License CC-BY 4.0

关键词： Hamiltonians

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Concise and Efficient quantum Algorithms for Distribution Closeness Testing

arXiv

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arXiv 2023年

作者： Li, Lvzhou Luo, Jingquan Institute of Quantum Computing and Computer Theory School of Computer and Engineering Sun Yat-sen University China

We study the impact of quantum computation on the fundamental problem of testing the property of distributions. In particular, we focus on testing whether two unknown classical distributions are close or far enough, and propose the currently best quantum algorithms for this problem under the metrics of l1-distance and l2-distance. Compared with the latest results given in [9] which relied on the technique of quantum singular value transformation (QSVT), our algorithms not only have lower complexity, but also are more concise. © 2023, CC BY.

关键词： quantum theory

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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Proposed Statistical Framework for Signal Modulation and Processing 15

Proposed Statistical Framework for Signal Modulation and Pro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajendran, Naveen Kumar Mokashi, Mandar Krishnarao Singh, K. Ranjith Singla, Abhishek Chandrakala, P. Goyal, Samaksh Jain School of Engineering and Technology Department of Aerospace Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Quantum University Research Quantum University India

ISBN: (纸本)9798350370249

This paper proposes a new statistical framework to facilitate sign modulation and processing. The proposed framework models every signal as a combination of different distributions and makes use of advanced statistical inference techniques to perceive the parameters of each issue. additionally, the framework takes into account the temporal correlations between person samples of the sign and uses superior gadget gaining knowledge of techniques to lessen interference from noise, allowing for the a hit transmission of alerts over noisy channels. The proposed framework is designed to lessen the computational value of signal processing and for that reason enhance the transmission price of signals. The overall performance of the proposed framework is evaluated through both simulated and actual-world experiments. Consequences show that the proposed framework considerably improves the sign-to-noise ratio of transmitted indicators and also reduces the computational cost of sign processing. © 2024 IEEE.

关键词： Signal modulation

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Improved quantum linear system solver via quantum phase discrimination

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The European Physical Journal Special Topics 2025年 1-11页

作者： Liu, Zihao Li, Guanzhong Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen China

The problem of solving linear systems is of great significance in both theory and practice, for which quantum solvers have been shown to provide an exponential speedup over the best-known classical solvers. Recently, a quantum linear system solver has been developed with the state-of-the-art complexity [1], which combines two techniques, namely, discrete adiabatic evolution and eigenstate filtering. However, the parameters of the quantum circuit for eigenstate filtering need to be precomputed classically via the discrete Fourier transform, which introduces additional computational overhead. In this paper, we improve the solver by presenting a new eigenstate filtering process termed quantum phase discrimination in which the circuit parameters are given directly in a concise and analytical form with negligible classical overhead, while maintaining the same quantum complexity.

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