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Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

Cybersecurity 2024年第1期7卷 1-15页

作者： Armita Kazeminajafabadi Mahdi Imani Department of Electrical and Computer Engineering Northeastern UniversityBostonMAUSA

Early attack detection is essential to ensure the security of complex networks,especially those in critical *** is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network *** attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the *** attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of *** paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of *** exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman *** adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined *** and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

关键词： Multi-stage attacks Bayesian attack graph Attack detection Optimal monitoring

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Capacitorless Solid-state Power Filter for Single-phase DC-AC Converters

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CES Transactions on electrical Machines and Systems 2024年第3期8卷 367-377页

作者： Haitham Kanakri Euzeli C.Dos Santos Purdue University Electrical and Computer Engineering DepartmentIndianapolisIN46202 USA

Converters rely on passive filtering as a crucial element due to the high-frequency operational characteristics of power *** filtering methods involve a dual inductor-capacitor(LC)cell or an inductor-capacitor-inductor(LCL)***,capacitors are susceptible to wear-out mechanisms and failure ***,the necessity for monitoring and regular replacement adds to an elevated cost of ownership for such *** utilization of an active output power filter can be used to diminish the dimensions of the LC filter and the electrolytic dc-link capacitor,even though the inclusion of capacitors remains an indispensable part of the *** paper introduces capacitorless solid-state power filter(SSPF)for single-phase dc-ac *** proposed configuration is capable of generating a sinusoidal ac voltage without relying on *** proposed filter,composed of a planar transformer and an H-bridge converter operating at high frequency,injects voltage harmonics to attain a sinusoidal output *** design parameters of the planar transformer are incorporated,and the impact of magnetizing and leakage inductances on the operation of the SSPF is *** analysis,supported by simulation and experimental results,are provided for a design example for a single-phase *** total harmonic distortion observed in the output voltage is well below the IEEE 519 *** system operation is experimentally tested under both steady-state and dynamic conditions.A comparison with existing technology is presented,demonstrating that the proposed topology reduces the passive components used for filtering.

关键词： Solid-state power filter(SSPF) Capacitorless topology Active output power filter(AOF) Planar transformer Electrolytic capacitor Lifetime Wear-out mechanisms

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Intelligent integrated sensing and communication:a survey

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Science China(Information sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

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computers, Materials & Continua 2024年第9期80卷 4643-4662页

作者： Zinah Khalid Jasim Jasim Sefer Kurnaz Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34000Turkey

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)*** steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least *** contemporary methods of steganography are at best a compromise between these *** this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic *** approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret *** approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale *** ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual *** evaporation is introduced through iterations to avoid stagnation in solution *** levels of pheromone are modified to reinforce successful pixel *** results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of *** approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

关键词： Steganography steganalysis capacity optimization ant colony algorithm

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Design and Develop Function for Research Based Application of Intelligent Internet-of-Vehicles Model Based on Fog Computing

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computers, Materials & Continua 2024年第12期81卷 3805-3824页

作者： Abduladheem Fadhil Khudhur Ayca Kurnaz Türkben Sefer Kurnaz Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34000Turkey

The fast growth in Internet-of-Vehicles(IoV)applications is rendering energy efficiency management of vehicular networks a highly important *** of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous *** on Large Energy-Aware Fog(LEAF)computing,this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network *** main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected *** proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous *** the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave,while different kinds of networking protocols-wired and wireless-can also be *** novelty of this work is threefold:for the first time,the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in *** existing fog computing simulators,data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and *** results of evaluation show that LEAF can cover up to 98.75%of the distance,with devices ranging between 1 and 1000,showing significant energy-saving potential through A wide-area network(WAN)usage *** findings indicate great promise for fog computing in the future-in particular,models like LEAF for planning energy-efficient IoV infrastructures.

关键词： Fog computing internet of vehicles LEAF segmentation distance power consumption cloud vehicle nodes wireless

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

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computers, Materials & Continua 2024年第7期80卷 819-841页

作者： Saad Abdalla Agaili Mohamed Sefer Kurnaz Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34000Turkey

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity ***,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork *** present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service *** compile a broad dataset of labeled VPN traffic flows from various apps and usage ***,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous *** effectively process and categorize encrypted packets,the neural network model has input,hidden,and output *** use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral *** also use cutting-edge optimizationmethods to optimize network characteristics and *** suggested ANN-based categorization method is extensively tested and *** show the model effectively classifies VPN traffic *** also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%*** study improves VPN security and protects against new *** VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security *** study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

关键词： VPN network traffic flow ANN classification intrusion detection data exfiltration encrypted traffic feature extraction network security

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Hybrid Local-global Power-sharing Scheme for Droop-free Controlled Microgrids

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1520-1534页

作者： Kunyu Zuo Lei Wu Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA

The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals,i.e.,sharing disturbance mitigation among all controllable assets to even their ***,limited by neighboring communication,the time-consuming peer-to-peer coordination of the droopfree control slows down the nodal convergence to global consensus,reducing the power-sharing efficiency as the number of nodes *** this end,this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes,in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed.A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes,which also enables the autonomous switching between local and global power-sharing modes according to the system *** guidance for key control parameter designs is derived via the optimal control methods,by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to *** system stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct ***,simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode ***,compared with the classical global power-sharing scheme,the proposed scheme presents promising benefits in convergence speed and scalability.

关键词： Droop-free control power-sharing optimal control Lyapunov stability

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Nanomechanical thermometry for probing sub-nW thermal transport

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Microsystems & Nanoengineering 2024年第5期10卷 287-303页

作者： Sangmin Oh Nehpal Singh Shekhawat Osama Jameel Amit Lal Chung Hoon Lee Electrical and Computer Engineering Department Marquette UniversityMilwaukeeWiUSA Polytec Inc. 16400 Bake PkwyIrvineCAUSA Electrical and Computer Engineering Department Cornell UniversityIthacaNYUSA

Accurate local temperature measurement at micro and nanoscales requires thermometry with high resolution because of ultra-low thermal *** the various methods for measuring temperature,optical techniques have shown the most precise temperature detection,with resolutions reaching(-10^(-9) K).In this work,we present a nanomechanical device with nano-Kelvin resolution(-10^(-9) K)at room temperature and 1 *** device uses a 20 nm thick silicon nitride(SiN)membrane,forming an air chamber as the sensing *** presented device has a temperature sensing area>1 mm^(2)for micro/nanoscale objects with reduced target placement constraints as the target can be placed anywhere on the>1 mm^(2)sensing *** temperature resolution of the SiN membrane device is determined by deflection at the center of the *** temperature resolution is inversely proportional to the membrane's stiffness,as detailed through analysis and measurements of stiffness and noise equivalent temperature(NET)in the pre-stressed SiN *** achievable heat flow resolution of the membrane device is 100 pW,making it suitable for examining thermal transport on micro and nanoscales.

关键词： transport stiffness precise

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