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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Reddy, Reddyvari Venkateswara Monisha, G.S. Ali, Ahmed Mudassar Yamini Devi, Palivala Madhusudhan, M.V. Sucharitha, Yadala CMR College of Engineering & Technology CSE-Cyber Security Hyderabad India Panimalar Engineering College Computer Science and Engineering Chennai Poonamalle India S. A. Engineering College Inforormation Technology Chennai India Vignana Bharathi Institute of Technology Computer Science and Engineering Hyderabad India Presidency University Computer Science and Engineering Bengaluru India VNR Vignana Jyothi Institute of Engineering and Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350334012

Thyroid issues are quite general, effecting millions of people across the globe. The thyroid gland is susceptible to a wide range of illnesses, including hypothyroidism, hyperthyroidism, and malignancy. The symptoms of hypothyroidism are often very unpleasant. Timely identification of thyroid disorders is greatly aided by accurate categorization and machine learning. Treatment times for patients will be impacted by this rapid categorization. If we want to save time and have fewer mistakes made by radiologists, automatic and exact thyroid nodule recognition in ultrasound images is essential. The use of medical imaging has increased dramatically as a trustworthy and significant data source for the growth of machine learning (ML) strategies. In this study, we apply numerous diverse ML strategies to the dataset and accomplish a comparative examination of their efficiency so that we can more perfectly anticipate the disease utilizing the factors recognized by the dataset. In addition, the dataset has been tweaked to improve classification prediction accuracy. Both the sampled and non-sampled datasets were classified so that the two could be contrasted more precisely. With some data tweaking, we were able to boost the random forest algorithm's accuracy to 99 percent, with 97 percent specificity. © 2023 IEEE.

关键词： Forecasting

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Generalized Polarization-Spatial Modulation With Multi-Mode Transmission

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IEEE Internet of Things Journal 2025年第12期12卷 18570-18581页

作者： Li, Jun Li, Shuangyuan Dang, Shuping Chen, Xuan Chen, Chuanxi Peng, Yuyang Yu, Ling Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

Polarization-spatial modulation (PSM) has been recently proposed to improve the system performance and energy efficiency of the conventional polarization modulation (PM) by activating only a single dual-polarized (DP) antenna for signal transmission. However, the spectral efficiency (SE) of PSM is significantly degraded due to the single DP antenna transmission. To tackle this problem, we propose a generalized PSM (GPSM) scheme to enhance the SE of PSM through transmission using multiple DP antennas. In the GPSM scheme, the information bits are mapped to antenna activation patterns (AAPs), polarization matrix activation patterns (PAPs), and modulated symbols. To further enhance the SE of GPSM, we propose a more practical scheme termed multi-mode GPSM (MM-GPSM), which transmits information bits not only through the AAPs, PAPs, and modulated symbols but also via the constellation activation patterns (CAPs). To reduce the computational burden of the optimal maximum likelihood (ML) detector, we propose a low-complexity ML detector and a log-likelihood ratio (LLR) detector for both GPSM and MM-GPSM, with the negligible performance degradation. A closed-form upper bound on the bit error rate (BER) is derived to evaluate the performance of both GPSM and MM-GPSM. Simulation results demonstrate that GPSM and MM-GPSM outperform conventional PM and PSM schemes, particularly in the high signal-to-noise ratio (SNR) region, and validate the accuracy of the theoretical BER analysis. © 2014 IEEE.

关键词： Global positioning system

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EBAKE-SE: A novel ECC-based authenticated key exchange between industrial IoT devices using secure element

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Digital Communications and Networks 2023年第2期9卷 358-366页

作者： Chintan Patel Ali Kashif Bashir Ahmad Ali AlZubi Rutvij Jhaveri School of Artificial Intelligence Information Technology and Cyber SecurityRashtriya Raksha UniversityGujaratIndia Department of Computing and Mathematics Manchester Metropolitan UniversityUnited Kingdom Woxsen School of Business Woxsen UniversityIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Computer Science Department Community CollegeKing Saud UniversityP.O.Box 28095Riyadh11437Saudi Arabia Department of Computer Science and Engineering School of TechnologyPandit Deendayal Energy UniversityGujaratIndia

Industrial IoT(IIoT)aims to enhance services provided by various industries,such as manufacturing and product *** suffers from various challenges,and security is one of the key challenge among those *** and access control are two notable challenges for any IIoT based industrial *** IoT based Industry 4.0 enterprise designs networks between hundreds of tiny devices such as sensors,actuators,fog devices and ***,articulating a secure authentication protocol between sensing devices or a sensing device and user devices is an essential step in IoT *** this paper,first,we present cryptanalysis for the certificate-based scheme proposed for a similar environment by Das et *** prove that their scheme is vulnerable to various traditional attacks such as device anonymity,MITM,and *** then put forward an interdevice authentication scheme using an ECC(Elliptic Curve Cryptography)that is highly secure and lightweight compared to other existing schemes for a similar ***,we set forth a formal security analysis using the random oracle-based ROR model and informal security analysis over the Doleve-Yao *** this paper,we present comparison of the proposed scheme with existing schemes based on communication cost,computation cost and security index to prove that the proposed EBAKE-SE is highly efficient,reliable,and trustworthy compared to other existing schemes for an inter-device *** long last,we present an implementation for the proposed EBAKE-SE using MQTT protocol.

关键词： Internet of things Authentication Elliptic curve cryptography Secure key exchange Message Queuing telemetry transport

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Novel optimal region based robust watermarking using modified dragonfly optimization algorithm

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International Journal of Information Technology (Singapore) 2025年第2期17卷 851-858页

作者： Shamna, N.V. Kaur, Puneet Raghavendra Patil, G.E. Krishna, Nanditha P A College of Engineering Dept of CSE (IoT & amp Cyber Security including Blockchain Technology) Karnataka India EEE UIET Panjab University Chandigarh India Department of Computer Science and Engineering Faculty of Engineering and Technology Jain Deemed-to be University Karnataka Bengaluru 562112 India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

The growing concern about unauthorized access and tampering with digital images requires the development of robust and secure watermarking methods. Traditional approaches frequently struggle with achieving a balance between security, image quality, and watermark strength, especially in high-resolution images. This study addresses these issues by proposing a new watermarking technique that combines the Blowfish encryption algorithm, the Discrete Wavelet Transform (DWT), and the Modified dragonfly optimization algorithm (MDFOA). The proposed approach begins with the Blowfish algorithm encrypting the secret image, providing increased security even if the watermark is detected. High-frequency features are preserved by embedding the encrypted watermark within the LL subband of the cover image, which is generated using DWT decomposition. MDFOA is used to identify the best embedding places inside the LL subband, which improves the watermark's concealment and resilience. The same methodology is used to repeat the extraction process, providing reliable and secure data transmission throughout the process. The proposed method is a highly effective solution for secure and high-quality image watermarking. It outperforms existing techniques in terms of Peak Signal-to-Noise ratio (PSNR), Normalized correlation (NC), Bit error rate (BER), and Normalized absolute error (NAE), according to extensive experiments conducted on a variety of high-resolution images. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blowfish encryption algorithm Discrete wavelet transform Modified dragonfly optimization algorithm Watermarking technique

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense Network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Artificial Intelligent based Cost Optimized Smart Energy Distribution and Management 2

Artificial Intelligent based Cost Optimized Smart Energy Dis...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Anitha, R. Siva Prasad, G. Senthil, M. Sarfaraz Ahmed, A. Raja, Laxmi Vijayakumar, S.D. Government Engineering College Department of Electronics and Communication Engineering Kerala India QIS College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Cyber Security Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331534950

The ever-increasing need for energy, along with the requirement to cut emissions of greenhouse gases and maximize efficiency in energy use, has been a driving force behind the development of cutting-edge technology in the field of intelligent energy distribution and management. This research article proposes an Artificial Intelligence (AI)-based cost-optimized smart energy distribution and management system. The purpose of this research is to present a new solution to addressing these difficulties. In order to intelligently manage the distribution and consumption of energy resources in a manner that is both cost-effective and efficient, the system that has been developed makes use of several artificial intelligence approaches, such as machine learning, optimization algorithms, and data analytics. The system's goal is to maximize energy utilization while keeping costs to a minimum and achieve the best possible optimization of energy distribution. In order to forecast energy demand and supply, the AI algorithms analyses data from the past as well as data that is being collected in real time. They also take into account aspects such as the current weather, the current time of day, and the preferences of individual users. The system makes real-time adjustments to the distribution of energy based on these projections, taking into account issues such as load balancing, the integration of renewable energy sources, and grid stability. Additionally, the system supports demand response capabilities, which enables users to actively participate in the management of energy by modifying their patterns of consumption based on real-time pricing signals. This allows users to actively participate in the management of energy. This demand-side management function not only promotes energy efficiency but also gives customers more agency in terms of the decisions they make regarding their energy consumption. © 2024 IEEE.

关键词： Artificial Intelligence Cost Optimization Demand Response Energy Distribution Energy Management Machine Learning Optimization Renewable Energy Smart Energy

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ML-assisted security for Anomaly Detection in Industrial IoT (IIoT) Applications 2

ML-assisted Security for Anomaly Detection in Industrial IoT...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Konatham, Bharath Reedy Simra, Tabassum Ghimire, Ashutosh Amsaad, Fathi Ibrahem, Mohamed I. Jhanjhi, N.Z. Department of Computer Science and Engineering Wright State University DaytonOH United States Department of Cyber Security Engineering George Mason University FairfaxVA United States School of Computer Science SCS Taylor’s University Subang Jaya47500 Malaysia

ISBN: (纸本)9798350305418

The industrial Internet of Things (IIoT) uses intelligent sensors and actuators, etc., to facilitate the application of IoT in different industries. This paper presents a comparative analysis using several machine learning (ML)-assisted deep learning techniques to develop efficient cybersecurity anomaly detection within IoT applications. To accomplish that, several deep learning techniques are investigated and analyzed, including Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), their hybrid (CNN+GRU), and Long Short-Term Memory (LSTM). The performance of the developed deep learning models is evaluated in terms of accuracy, precision, recall, F1-score, and False Alarm Rate (FAR). Our experimental results show that the developed hybrid CNN+GRU model outperforms the others, achieving an accuracy of 94.94%, a recall of 92.29%, a precision of 98.49%, an F1-score of 95.24%, and a low false alarm rate of 0.001. However, it is essential to note that the hybrid model requires a longer convergence time, indicating a trade-off between performance and computational efficiency. Notably, individual CNN and GRU models also showcase strong performance as time-efficient alternatives. In conclusion, our adopted comprehensive dataset and rigorous evaluation prove that we have developed practical deep-learning approaches to obtain an accurate measure for an efficient IIoT anomaly detection framework. © 2023 IEEE.

关键词： Anomaly Detection CNN GRU Industrial Internet of Things (IIoT) LSTM ML-assisted security

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Traceable Attribute-Based Keyword Search Scheme for Distributed Data Storage under Fog Computing-Based Social Internet of Vehicles

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Gu, Ke Qiu, Juan Peng, Xin Li, Xiong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China University of Electronic Science and Technology of China Institute for Cyber Security School of Computer Science and Engineering Chengdu611731 China

In Social Internet of Vehicles (SIoVs), vehicles frequently need to obtain their required data, thus many secure data acquisition schemes were proposed in cloud computing-based SIoVs. However, the distributed data storage method under fog computing-based SIoVs can bring about more security issues, such as potential data leakage, unauthorized access and integrity concerns. To address these challenges, we propose a traceable attribute-based keyword search scheme for distributed data storage based on SIoVs under fog computing in this paper. According to the Shamir's secret-sharing scheme, our proposed scheme provides a distributed data block verification participated by multiple fog servers to check the effectiveness of distributed data storage. In our scheme, a legal data-owned vehicle can divide its encrypted data into many data blocks, and securely upload these data blocks and related keywords to multiple fog servers;a data-required vehicle can generate a trapdoor on the keywords of required data, and then decrypt the data blocks received from related fog servers. Further, the key generation center can trace the identity of a suspicious data-owned vehicle according to related data when it is confirmed that there is false traffic data uploaded by the suspicious data-owned vehicle. Under the proposed security models, our proposed scheme is proved to secure stored data and keyword search in fog computing-based SIoVs. Additionally, experimental results show that our scheme is effective. © 2015 IEEE.

关键词： Information leakage

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Deep Learning Cost-Effective Evaluation Metrics

Deep Learning Cost-Effective Evaluation Metrics

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2023 China Automation Congress, CAC 2023

作者： Zhao, Jian Zhao, Zisong Shi, Lijuan Kuang, Zhejun Wang, Runmin Li, Haixia College of Cyber Security Changchun University Changchun China College of Electronic Information Engineering Changchun University Changchun China College of Computer Science and Technology Changchun University Changchun China

ISBN: (纸本)9798350303759

Deep learning has been a driving force behind recent breakthroughs in artificial intelligence. In particular, neural networks have shown great promise in handling a wide range of tasks. With the improvement of performance, networks need computing power and space are also increasing day by day. However, in practical applications, platforms with low computing power, such as edge devices, have limited computing, it is necessary to select a high cost-effective model. Traditional methods require a lot of experience to select cost-effective models. Therefore, there is an urgent need for a deep learning cost-effective evaluation metrics to measure this performance. To address this, we propose a cost-effective evaluation metric called TALES, which takes a common approach to evaluating deep learning models in practical applications. Our metric objectively evaluates networks based on FLOPs, Params, and predicted latency, which are key factors in determining model performance. After conducting a statistical survey of nearly 300 networks, we conclude that the growth in accuracy and the growth in Euclidean distance computed by FLOPs, Params, and Latency have a very similar mathematical pattern to the Sigmoid function. We then fit the relationship curve using mathematical modeling. Our final results show that our designed cost-effective evaluation metric can effectively and objectively evaluate the relationship between accuracy and network complexity of each network. The results are consistent with objective cognition in the field of deep learning. Through the metric, we can effectively evaluate the performance of deep learning models in practical applications and reducing costs and improving efficiency. © 2023 IEEE.

关键词： Cost effectiveness

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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