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Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

computer Systems science & engineering 2023年第12期47卷 3145-3163页

作者： Shahina Anwarul Sunil Kumar Ashok Bhansali Hammam Alshazly Hany S.Hussein School of Computer Science University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science Graphic Era Hill UniversityDehradunUttarakhand248001India Department of Computer Engineering and Applications GLA UniversityMathuraIndia Faculty of Computers and Information South Valley UniversityQena83523Egypt Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha62529Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Security systems are the need of the hour to protect data from unauthorized *** dissemination of confidential information over the public network requires a high level of *** security approach such as steganography ensures confidentiality,authentication,integrity,and *** helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and ***,we present an efficient hybrid security model that provides multifold security *** this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high *** modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret *** enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)*** experiments are conducted on the images of the USC-SIPI standard image *** experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

关键词： Cryptography steganography digital signature rectified AES encryption

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User Profiling Attack Using Windows Registry Data 14

User Profiling Attack Using Windows Registry Data

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Amoruso, Edward L. Zou, Cliff C. Leinecker, Richard University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States University of Central Florida Department of Computer Science OrlandoFL32816 United States

ISBN: (纸本)9798350304138

The Windows registry stores a glut of information containing settings and data utilized by the Microsoft operating system (OS) and other applications. For example, information such as user credentials, installed programs, recently used applications and documents, accessed resources such as local, remote, and removable devices can all be found in this database. More revealingly, the registry also has time and date stamps that can help build a timeline of user activities. The Windows registry can be easily queried by either malicious or benign applications. This is possible through the Windows Application Program Interface (API) and other OS built-in utilities. In this paper, we develop and demonstrate a program able to collect and infer a user's rich activities by accessing the Windows registry alone. This information, also referred to as the user's digital footprint, can be used to devise an exploit or create a privacy threat. Our custom developed application will demonstrate how a user's digital footprint can be acquired by a malicious application from a Windows registry, without alerting security software. In addition, this information can be exported to a set of comma delimited files, making it easy to import them into other analysis applications. © 2023 IEEE.

关键词： Application programs

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On the Performance of Multidimensional Constellation Shaping for Linear and Nonlinear Optical Fiber Channel 49

On the Performance of Multidimensional Constellation Shaping...

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49th European Conference on Optical Communications, ECOC 2023

作者： Chen, Bin Liang, Zhiwei Li, Shen Lei, Yi Liga, Gabriele Alvarado, Alex School of Computer Science and Information Engineering Hefei University of Technology China Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical Engineering Eindhoven University of Technology Netherlands

ISBN: (纸本)9781839539954

Multidimensional constellation shaping of up to 32 dimensions with different spectral efficiencies are compared through AWGN and fiber-optic s imulations. The results show that no constellation is universal and the balance of required and effective SNRs should be jointly considered for the specific optical transmission scenario. © The Institution of engineering & Technology 2023.

关键词： Optical fibers

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Transfer Learning Enhanced Deep Reinforcement Learning for Volt-Var Control in Smart Grids

Transfer Learning Enhanced Deep Reinforcement Learning for V...

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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Kundan Kumar Gelli Ravikumar Department of Computer Science and Electrical and Computer Engineering Iowa State University IA United States

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources during training. The control policies developed using DRL are specific to a single grid and require retraining from scratch for other grids. Training the DRL model from scratch is computationally expensive. This paper proposes a novel TL with a DRL framework to optimize VV C across different grids. This framework significantly reduces training time and improves VVC control performance by fine-tuning pre-trained DRL models for various grids. We developed a policy reuse classifier that transfers the knowledge from the IEEE-123 Bus system to the IEEE-13 Bus system. We performed an impact analysis to determine the effectiveness of TL. Our results show that TL improves the VVC control policy by 69.51 %, achieves faster convergence, and reduces the training time by 98.14%.

关键词： Training Renewable energy sources Energy resources Computational modeling Power system management Transfer learning Robustness Topology Smart grids Optimization

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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers 21

Network Traffic Prediction with Decomposition and Multi-Scal...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Meijia Yuan, Haitao Kuang, Zhenwei Ma, Hanbo Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models. © 2024 IEEE.

关键词： Autocorrelation

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Multi-Context Recommendation Systems (CARS) in Autonomous Driving and Other Applications

Multi-Context Recommendation Systems (CARS) in Autonomous Dr...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Ali, Abdullahi Kashef, Rasha Ibrahim, Ahmed Fakhry Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science TorontoON Canada Boston University Computer Science Department United States

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Recommendation systems (RS) are important for their instantaneous ability to suggest to users their desired items and for ensuring a smooth and worthwhile user experience. To optimize the user's experience with the RS, it can be programmed to be aware of the various contexts the user has while interacting with the system. Context-aware recommendation systems (CARS) aim to add more nuance to the process of predicting items by considering different factors such as location and time. This paper presents the foundations of recommendation systems, such as categories, evaluation metrics, datasets, and challenges. In this paper, we show the effectiveness of multi-contexts in autonomous driving. Three CARS models are developed and tested. Experimental results show that adding multi-contexts provides more personalized options for drivers, allowing intelligent decisions. © 2022 IEEE.

关键词： Recommender systems

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The Role of Artificial Intelligence (AI) in the Accounting and Auditing Professions 10

The Role of Artificial Intelligence (AI) in the Accounting a...

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10th IEEE Uttar Pradesh Section International Conference on electrical, Electronics and computer engineering, UPCON 2023

作者： Kaur, Harshdeep Arora, Geeta Salaria, Archana Singh, Arun Rakhra, Manik Dhiman, Gaurav Lovely Professional University Department of Mathematics Phagwara India University Institute of Engineering and Technology Department of Computer Science and Engineering Kathua India School of Computer Science and Engineering Lovely Professional University Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lebanese American University Department of Electrical and Computer Engineering Lebanon School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India

ISBN: (纸本)9798350382471

The fast growth of business, science, and technology has ushered in the age of artificial intelligence, which has had far-reaching effects in every sphere of human existence. This article explains how AI may be used to detect and prevent accounting fraud and enhance the reliability of financial statements. Accounting and auditing firms may benefit or suffer from the advancement and use of artificial intelligence (AI). This article explains how AI can be used to reduce accounting fraud and boost the credibility of financial statements. The use of AI in these fields is not without its merits and drawbacks. Change is inevitable, and the accounting and auditing industry will undergo significant change in the near future. The report concludes by stressing the importance, in the broader context of AI, of accounting staff members developing their own expertise and proficiency. This research highlights the possible impacts of artificial intelligence (AI) on the accounting industry and the steps that need to be taken to prepare for a workforce in the future where AI plays a larger role. © 2023 IEEE.

关键词： component formatting insert style styling

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SyREC: A Symbolic-Regression-Based Ensemble Combiner 36

SyREC: A Symbolic-Regression-Based Ensemble Combiner

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Fong, Kei Sen Motani, Mehul Department of Electrical and Computer Engineering Singapore College of Design and Engineering Singapore Institute of Data Science Singapore N.1 Institute for Health Singapore National University of Singapore Singapore

ISBN: (纸本)9798331527235

Symbolic Regression (SR) is the task of finding a concise white-box mathematical expression that fulfills a given machine learning (ML) objective. In this work, we introduce the first-of-its-kind SR-based ensemble combiner (SyREC) for ML which utilizes the quasi-arithmetic mean (parameterized by a function f) as an ensemble combiner and discovers f via SR. Our Sy REC demonstrates advantages over existing ensemble combiners, i.e., averaging methods and meta-learners. Compared to averaging methods, Sy REC allows for increased combiner complexity via exploring a large function class and provides a learning process to tune the ensemble combiner to the specific dataset. Compared to meta-learners, SyREC preserves important ensemble properties, like idempotency, monotonicity and boundedness, which produce predictions more consistent with the base models. We note SyREC produces a white-box ensemble combiner that lies in the intersection of both categories of existing combiners, addressing the weaknesses while capturing the strengths of each category. To evaluate Sy REC, we present experiments on 16 open-source PMLB datasets and commonly used base models, including a large variety of experiments and analyses across different settings: (i) regression and classification tasks, (ii) equally weighted combiners and custom-weighted combiners, (iii) homogeneous and heterogeneous ensembles, (iv) sequential and parallel ensembles. We find that in all these settings, SyREC shows consistent improvement over existing ensembling approaches. This indicates that SyREC is a robust ensemble combiner, which produces explainable and consistent ensemble predictions. © 2024 IEEE.

关键词： Contrastive Learning

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Channel-Adaptive Resource Allocation Architecture for Resource-Efficient RAN Slicing 13

Channel-Adaptive Resource Allocation Architecture for Resour...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Serae Jin, Sunghyun Kim, Junseon Lee, Kyunghan Seoul National University Electrical and Computer Engineering Seoul Korea Republic of Computer Science and Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Network slicing is one of the key techniques for realizing future services with diverse requirements. In radio access network(RAN), dynamic slice management for guaranteeing quality-of-service(QoS) of each user is especially challenging due to highly fluctuated radio channel for each user. To cope with such channel changes, it is inevitable to allocate more radio resources than are actually used(i.e., over-provisioning). To handle this resource inefficiency, there have been RAN slicing studies for adaptively allocating resources according to channel changes. However, their approaches, which focus on ensuring average performance over time and users, make it difficult to provide guaranteed performance for each user. In this paper, we propose channel-adaptive resource allocation architecture that enables resource-efficient per-user performance guarantees. The proposed architecture consists of two main components: 1) inter-slice allocation which tries to secure resources to satisfy each slice's service-level agreements and 2) intra-slice allocation for per-user resource allocation for satisfying performance requirements of each service. This paper concludes with a discussion on open issues to practical usage of the proposed architecture. © 2022 IEEE.

关键词： Reinforcement learning

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Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

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computer Modeling in engineering & sciences 2023年第12期137卷 2163-2192页

作者： Ali S.Alghamdi Mohana Alanazi Abdulaziz Alanazi Yazeed Qasaymeh Muhammad Zubair Ahmed Bilal Awan M.G.B.Ashiq Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka72341Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityAr’Ar73222Saudi Arabia Automation and Control Department College of Engineering TechnologyUniversity of Doha for Science and TechnologyDoha24449Qatar Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjman20550United Arab Emirates Department of Physics College of ScienceImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried *** has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the *** standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight *** proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub ***,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

关键词： Stochastic energy hub scheduling energy profit uncertainty Hong’s two-point estimate method improved artificial rabbits optimization

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