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Next-Gen SDN Security: Quantum Keying Meets AI Analytics

Next-Gen SDN Security: Quantum Keying Meets AI Analytics

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Virushabadoss, S. Anithaashri, T.P. Nivedha, M. Krishnan, S. Hari Karuppannan, Anand Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept. of Computer Science and Engineering Chennai India Arasu Engineering College Department of Electronics and Communication and Engineering Kumbakonam India Sanskrithi School of Engineering Department of Electronics and Communication and Engineering Puttaparthi India Gnanamani College of Technology Department of Electronics and Communication and Engineering Namakkal India

ISBN: (纸本)9798350375442

This paper suggests a complete security architecture that blends quantum key distribution (QKD) with cutting-edge machine learning methods to improve the Software-Defined Networking (SDN) control plane. By creating secure channels of communication through QKD, the framework ensures integrity and secrecy of control messages between the SDN controller and network devices. Furthermore, network traffic is continuously monitored and analyzed using machine learning techniques, enabling the detection of unusual behaviors that might be warning signs of upcoming security problems. Tests utilizing Mininet as the network interface and Open Daylight as the SDN controller proved the efficacy of the suggested method. The outcomes revealed a minimal computational overhead, exceptional detection accuracy up to 98.1%, and low false positive rates, substantiating the system's ability to effectively manage varied network conditions and traffic volumes. These results underscore the potential of integrating QKD with robust machine learning strategies to secure the SDN control plane against sophisticated threats. © 2024 IEEE.

关键词： Quantum cryptography

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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Smart Devices (ICSD), International Conference on

作者： Vivek Kumar Kamal Kumar Maheep Singh Dept. of Computer Science –and Engineering National Institute of Technology Pauri Uttarakhand India Department of Information Technology IGDTUW New Delhi India Dept. of Computer Science and Engineering Doon University Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical; therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes.

关键词： Training Threat modeling Performance evaluation Deep learning Training data Network intrusion detection Feature extraction Data models Smart devices

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Near-Threshold-at-Gate based Test for Stuck-on Fault in Scan-chain Testing

Near-Threshold-at-Gate based Test for Stuck-on Fault in Scan...

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International Conference on VLSI Design

作者： R S Haripriya Soumitro Vyapari Jaynarayan T Tudu Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science & Engineering Indian Institute of Technology Tirupati India

Scan integrity test has become inevitable for the design manufacturing that demands low DPPM. As the current technology has already moved towards nanometer realm, the manufacturing process begin to see newer defects at the level of cell. Such defect doesn’t seems to be detectable either by stuck-at or transition delay test alone. The newer kind of defects which are becoming prominent are rather subtle requiring a newer test methodology that would involve finer control of voltage and current at the transistor itself. In this work we have addressed the fault of kind which can be classified as Stuck-on fault in NMOS transistor. The specific problem that we attempt to solve is that how do we test the stuck-on fault in the NMOS transistor on the scan path? The solution we propose is to find out a newer scan integrity test methodology to ensure the correctness of scan chain before it can be used for design test. The proposed solution provides a scan integrity test technique that involves a careful control of gate voltage at near threshold value. This near threshold voltage at input sensitizes the Stuck-on fault which is then propagated to the scan-out by the process of flushing shift. The proposed methodology has been experimentally verified for the standard scan cell design using 16nm technology as well as at the logic level it has been implemented on ISCAS circuit to demonstrate the sensitization and propagation of fault effect. The proposed methodology does not requires any additional DFT area.

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Multi-View Brain Network Analysis with Cross-View Missing Network Generation

Multi-View Brain Network Analysis with Cross-View Missing Ne...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Luo, Gongxu Li, Chenyang Cui, Hejie Sun, Lichao He, Lifang Yang, Carl MBZUAI Department of Machine Learning Abu Dhabi United Arab Emirates University of Waterloo Dept. of Electrical and Computer Engineering WaterlooON Canada Emory University Department of Computer Science AtlantaGA United States Lehigh University Dept. of Computer Science and Engineering BethlehemPA United States

ISBN: (纸本)9781665468190

Parkinson's Disease (PD), one of the most common neurological disorders, has long been a challenge in public health clinical diagnosis as well as scientific understanding. Recently, there has been an upsurge of interest in brain network analysis which benefits the understanding of brain functions and early detection of neurological disorders extensively. Multi-view brain networks with different connectivity patterns among regions of interests (ROIs) can be constructed to reflect different and complementary perspectives of the brain connectivity profile. However, the extraction of such multi-view brain networks relies on the availability of multiple neuroimaging modalities and heavy data preprocessing, which often leads to severe missing data in either view. The cross-view missing issue hinders the pragmaticality of multi-view representation learning and downstream analysis. In this work, we formulate the novel problem of cross-view brain network generation and propose CroGen, a graph generative model that can generate the missing view when only one view is given. Specifically, GroGen leverages the potential correlation between diverse views of brain networks of the same individuals. Moreover, we design a pre-train schema to make full use of the labeled individuals with only single views of brain networks. Extensive experiments on real-life Parkinson's Progression Markers Initiative (PPMI) cohort demonstrate the supreme effectiveness of CroGen over baselines on both cross-view generation tasks and downstream PD classification. © 2022 IEEE.

关键词： Neuroimaging

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CPT Manager: An Open Environment for Constrained Path-Based Testing 18

CPT Manager: An Open Environment for Constrained Path-Based ...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Klima, Matej Bures, Miroslav Holotik, Daniel Herczeg, Maximilian Miltner, Marek Zanocco, Chad FEE Czech Technical University in Prague Dept. of Computer Science Prague Czech Republic Stanford University Civil and Environmental Engineering StanfordCA United States The Czech Technical University in Prague FEE Dept. of Economics Management and Humanities Prague Czech Republic

ISBN: (纸本)9798331534677

Path-based Testing is a common technique to test System Under Test (SUT) processes. Generally, a directed graph that models a system's workflow is input to the test path generation process, as well as the selected test coverage criterion. Several algorithms are proposed in the literature that traverse the graph and facilitate the generation of test cases for the selected coverage criterion. However, a plain directed graph used for modeling SUT processes does not allow for the capture of real-life dependencies and constraints between actions in the tested processes, which might pose a limit in application of this technique. Therefore, we defined an extended model that allows the specification of constraints upon the graph's elements and a set of algorithms that allow the generation of the set of test cases that satisfy the given constraints with the edge coverage. Considering the fact that in path-based testing, there is no platform in which engineers and researchers can share SUT models to be further assembled into open datasets to test performance of evolved path-based testing MBT algorithms, especially for the given problem of test paths generation with the constraints, this paper presents a summary of the problem and a novel management for creation and management of SUT models with constraints that allows the generation of test paths as well as to serve as a platform for creation of such benchmark datasets. © 2025 IEEE.

关键词： Data flow graphs

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Securing the Future of Electronic Health Records with Blockchain Integration

Securing the Future of Electronic Health Records with Blockc...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： Andleeb Khan Parma Nand Bharat Bhushan Department of Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida UP Dept. of Computer Science & Engineering Sharda School of Engineering &Technology Greater Noida UP

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

The use of blockchain technology is meant to revolutionize the way in which traditional systems work. This technology has paved way for more secure solutions thereby leveraging the concepts of data privacy and authentication. Electronic health records are digitally stored health data of the patients which includes their history of diagnosis along with their reports generated by the hospitals. But these records also face the concerns of data privacy and security as they are prone to tempering, leakage and duplication. To address these concerns researchers have come up with the use of blockchain in electronic health records. Since blockchain is a decentralized and immutable ledger-based platform, it offers more secure and private solutions to the healthcare sector. This paper presents a framework based on blockchain for EHRs in order to enhance their security. It also makes use of Interplanetary file system as a distributed data storage platform which is compatible with Web 3 based environments. This addresses the issue of scalability of the system. The proposed framework proves to be better in terms of privacy, scalability, security and cost when compared with the recent literatures. This system can further be extended by integrating multi-factor authentication as a future direction for research.

关键词： Data privacy Privacy Multi-factor authentication Hospitals Scalability Memory Market research Blockchains InterPlanetary File System Electronic medical records

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Outage Analysis for a HAPS-Mounted IRS-Assisted Multi-User Communication System

Outage Analysis for a HAPS-Mounted IRS-Assisted Multi-User C...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Ahmed M. Benaya Mohamed S. Hassan Mahmoud H. Ismail Taha Landolsi Mohamed I. AlHajri Dept. of Electrical Engineering American University of Sharjah Sharjah UAE Dept. of Computer Science and Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

In this paper, we propose a high altitude platform station (HAPS)-mounted intelligent reflecting surface (IRS)assisted multi-user communication system. Due to potential blockage or the fact that the users might be located outside of the coverage of the terrestrial base station (TBS), there might not be a direct service link from the TBS to the users. Hence, with the use of the reflected signals from the HAPS-mounted IRS, the TBS is able to provide services to its associated user equipment (UEs). With this proposed scenario, the coverage area can be substantially increased as the HAPS are usually located in high altitudes. We study the outage performance of the proposed system taking into account the UEs distribution in the service area. We derive an approximate closed-form expression for the outage probability under a Rician-faded TBS-HAPS link and a shadowed Rician-faded HAPS-UEs link. Moreover, we validate our derived expression through Monte Carlo simulations considering the effect of different system parameters on the outage probability. Results show that the proposed system can achieve outage probability in the order of 10 −4 up to a service area diameter of 30 km and in the order of 10 −2 for service areas in the range of 40 km in diameter.

关键词： Fading channels Monte Carlo methods Closed-form solutions Communication systems Simulation Rician channels Probability Power system reliability Surface treatment Shadow mapping

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Evaluation of spatial tasks in virtual acoustic environments by means of modeling individual localization performances 24

Evaluation of spatial tasks in virtual acoustic environments...

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24th International Congress on Acoustics, ICA 2022

作者： Barumerli, Roberto Majdak, Piotr Geronazzo, Michele Meijer, David Avanzini, Federico Baumgartner, Robert Acoustic Research Institute Austrian Academy of Sciences Austria Dept. of Management and Engineering University of Padova Italy Dept. of Computer Science University of Milano Italy

Virtual acoustic environments (VAEs) are an excellent tool in hearing research, especially in the context of investigating spatial-hearing abilities. On the one hand, the development of VAEs requires a solid evaluation, which can be simplified by applying auditory models. On the other hand, VAE research provides data, which can support the further improvement of auditory models. Here, we describe how Bayesian inference can predict listeners' behavior when estimating the spatial direction of a static sound source presented in a VAE experiment. We show which components of the behavioral process are reflected in the model structure. Importantly, we highlight which acoustic cues are important to obtain accurate model predictions of listeners' localization performance in VAE. Moreover, we describe the influence of spatial priors and sensorimotor noise on response behavior. To account for inter-individual differences, we further demonstrate the necessity of individual calibration of sensory noise parameters in addition to the individual acoustic properties captured in head-related transfer functions. © ICA *** rights reserved

关键词： binaural sounds probabilistic approach sound localization

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Early Detection Of Parkinson’s Disease Using Machine And Deep Learning Models

Early Detection Of Parkinson’s Disease Using Machine And De...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Afreen Sorathiya Jinal Mehta Hetangi Rathod Nilesh Marathe Dept. of Computer Science & Engineering (Data Science) SVKM’S Dwarkadas J Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Parkinson’s disease (PD) represents significant challenges in early detection, often relying on subjective clinical assessments and costly imaging procedures, leading to low accuracy rates, especially in detecting PD at its early stages. This research investigates the transformative potential of machine learning and deep learning algorithms in PD detection and diagnosis. Leveraging a diverse dataset comprising vocal fundamental frequency measurements, nonlinear dynamical complexity measures, and image data, we explore various Machine Learning (ML) and Deep Learning (DL) models. Our findings reveal promising accuracies across different models with DenseNet121 achieving a maximum accuracy of ${9 6 . 6 7 \%}$ for image data and hybrid architectures like 1D CNN-GRU achieving ${9 4 . 8 7 \%}$ accuracy for vocal data. Furthermore, the study identifies key limitations and challenges associated with each model, emphasizing the need for continued refinement and innovation. This research contributes to advancing PD diagnosis, offering potential improvements in patient outcomes and paving the way for more accessible and effective diagnostic solutions.

关键词： Deep learning Accuracy Computational modeling computer architecture Data models Vectors Frequency measurement

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