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3rd international Conference on Electronics and Renewable systems, ICEARS 2025

作者： Mahaveerakannan, R. Kumar, T. Rajesh Balamanigandan, R. Vasarao, Parnandi Srinu Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Swarnandhra College of Engineering and Technology Department of Computer Science and Engineering West Godavari District Andhra Pradesh Narsapur India

ISBN: (纸本)9798331509675

The presence of numerous bidirectional communication devices connecting customers to the grid makes smart grid networks particularly vulnerable to network attacks. As an instance of a network attack that could impact the smart grid, a distributed denial of service (DDoS) attack occurs when a compromised grid communiqué devices or nodes send a flood of false data or requests to the smart grid network. This can cause problems with smart meters, data servers, and the state estimator, which in turn affects the services that end users receive. The problem of protecting the network from distributed denial of service (DDoS) attacks is best addressed by methods that rely on machine learning. One major drawback of using machine learning-based approaches is that models need to be retrained every time new types of attacks appear. Despite the fact that DDoS attack detection has long relied on Intrusion Detection systems (IDS), previous research has been limited due to a number of factors. Actually, it is highly discouraged to interfere with the regular functioning of the smart grid. to suggest using reconstructive deep learning methods to identify DDoS attacks efficiently and with minimal downtime in order to tackle this difficulty. In order to identify typical attacks on a communication network, this research introduces a shallow neural network with just 110 artificial neurones triggered by ReLU. to suggest a better attack-sharing loss function to handle unbalanced learning, which will allow for the training of such tiny neural networks. A Lungs Performance-based Optimisation (LPO) was devised using the suggested model to hone the parameters. In smart grid settings, our suggested approach improves the scalability and dependability of IDS and provides useful insights for building strong IDS based on machine learning. This enables organisations to strengthen their defences against harmful cyber-attacks and proactively reduce security risks. © 2025 IEEE.

关键词： Network intrusion

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On Intrinsic Cause and Defense of Adversarial Examples in Deep Neural networks 19th

On Intrinsic Cause and Defense of Adversarial Examples in...

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19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European symposium on Research in computer Security, ESORICS 2024

作者： Tasaki, Hajime Chao, Jinhui Department of Information and System Engineering Chuo University Bunkyo Tokyo1-13-27 Kasuga Japan

ISBN: (纸本)9783031823619

The existence of so-called adversarial examples has become a serious threat to Deep Neural networks (DNN) and their applications, especially security-sensitive ones. Explanations and defenses mainly focused on inside of the DNN, e.g. its generalization errors and training defects, have been proposed without much success. Recently, we have shown that the extrinsic cause of adversarial examples is due to embedding geometry of the training data manifold, and adversarial examples mainly exist in the orthogonal complementary spaces of the tangent spaces on the data manifold. In this paper, we prove the intrinsic cause of the adversarial examples, i.e. the existence of non-zero manifold-orthogonal components of every weight almost everywhere, as the counterpart of the extrinsic cause. Investigations showed that weights of the DNN always have nonzero and large manifold-orthogonal components at almost every data point, which explains why small adversarial perturbations can lead to misclassifications in well-trained DNN. Then, based on these mechanisms, we propose a new defense against adversarial examples by extracting the manifold structure of the training data and removing the components orthogonal to the tangent spaces of the manifolds from the input data, which successfully prevents misclassification of DNN for major adversarial example attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Designing a Rescue Strategy Emphasizing distributed Control in RoboCupRescue Simulation: AIT-Rescue: RoboCup 2024 Rescue Simulation League Champion

Designing a Rescue Strategy Emphasizing Distributed Control...

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27th RoboCup international symposium, 2024

作者： Ando, Keisuke Maeda, Ryoya Uehara, Haruki Fujisawa, Joe Matsunaga, Itsuki Suzuki, Ryosuke Kato, Kota Shimada, Yuki Fujii, Shuntarou Uchitane, Takeshi Iwata, Kazunori Ito, Nobuhiro Department of Information Science Aichi Institute of Technology Toyota Japan Department of Business Administration Aichi University Nagoya Japan

ISBN: (纸本)9783031858581

AIT-Rescue is the champion team in the RoboCup 2024 Rescue Simulation League that succeeded in proposing a rescue strategy focused on distributed control. RoboCupRescue Simulation is a competition that aims to develop rescue strategies to save more civilians in disaster rescue simulations. AIT-Rescue employs autonomous distributed decision-making and has a flexible rescue strategy that can be adopted for complex disaster situations. This paper explains the rescue strategy of AIT-Rescue and the implemented module design. The critical features in this system are the methods for determining priority roads and selecting sanctuaries. The processes that these methods consider for civilian rescue in disaster conditions are explained. The research results on multiagent systems are presented in relation to AIT-Rescue’s development. This research contributes to developing effective strategies for real-world disaster rescue by exploring multiagent systems and applying them to complex disaster scenarios. In the future, these results will be integrated into agents to obtain better strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： distributed parameter control systems

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Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2025年 162卷

作者： Chen, Shengbo Li, Shuai Wang, Guanghui Yu, Keping Henan Univ Sch Comp & Informat Engn Kaifeng Henan Peoples R China Henan Univ Sch Software Kaifeng Henan Peoples R China Henan Univ Henan Engn Res Ctr Intelligent Technol & Applicat Kaifeng Henan Peoples R China Henan Univ Henan Int Joint Lab Intelligent Network Theory & K Kaifeng Henan Peoples R China Hosei Univ Grad Sch Sci & Engn Tokyo Japan

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

关键词： Energy harvesting Linear wireless sensor networks Optimal energy allocation Throughput maximization

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8th international symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

8th International Symposium on Cyber Security, Cryptology, a...

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8th international symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

ISBN: (纸本)9783031769337

The proceedings contain 27 papers. The special focus in this conference is on Cyber Security, Cryptology, and Machine Learning. The topics include: Robust Secure Aggregation for Co-located IoT Devices with Corruption Localization;spoofing-Robust Speaker Verification Based on Time-Domain Embedding;provable Imbalanced Point Clustering;Mezzo TLS 1.3 Protocol, Suitable for Transmitting Already-Encrypted Data: – Short Paper –;LLMSecCode: Evaluating Large Language Models for Secure Coding;distributed Verifiable Random Function with Compact Proof;fiat-Shamir in the Wild;predicting the Degradation Rate of Technical systems at Early Stages of Development;reminisce for Securing Private-Keys in Public;minimally Intrusive Access Management to Content Delivery networks Based on Performance Models and Access Patterns;on the Effects of Neural Network-Based Output Prediction Attacks on the Design of Symmetric-Key Ciphers;entanglement-Based Mutual Quantum Distance Bounding;evaluation of Posits for Spectral Analysis Using a Software-Defined Dataflow Architecture;access Policy Prediction via User Behavior;a Probabilistic Model for Rounding Errors: A New Look at the Table-Maker’s Dilemma;on the Overflow and p-adic Theory Applied to Homomorphic Encryption;Beneath the Cream: Unveiling Relevant Information Points from CrimeBB with Its Ground Truth Labels;cybersecurity Enhancement for Wireless networks Using Aerial Reconfigurable Intelligent Surfaces;KNN+X;challenges in Timed-Cryptography: A Position Paper (Short Paper);Post Quantum Lightweight OWF Candidates: Based on Theoretically Secure Primitives: Xors, Error Detection Codes, Permutations, Polynomials, Interaction, and Nesting (Short Version);super-Teaching in Machine Learning;A Lattice Attack Against a Family of RSA-Like Cryptosystems;on the Security Related Properties of Randomly Constructed Combinatorial Structures;HBSS+: Simple Hash-Based Stateless Signatures Revisited: (Preliminary Version, Short Paper).

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Reinforcement Learning-Driven Adaptive Signal Processing for Low-Power Edge Devices in Dynamic Environments 2

Reinforcement Learning-Driven Adaptive Signal Processing for...

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2nd international Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Kashikar, Raj Virginia Polytechnic Institute and State University Department of Computer Science Blacksburg United States

ISBN: (纸本)9798331505745

Reinforcement learning (RL) offers a transformative approach to adaptive signal processing on low-power edge devices operating in dynamic environments. This work introduces an RL-driven framework utilizing Proximal Policy Optimization (PPO) in conjunction with Convolutional Neural networks (CNNs) and Long Short-Term Memory (LSTM) networks to autonomously enhance signal quality and reduce energy consumption. The model dynamically adjusts to fluctuating noise and interference by fine-tuning filtering techniques, optimizing compression, and adapting hardware configurations. Tests on platforms like NVIDIA Jetson Nano and Raspberry Pi show a 12% improvement in signal-to-noise ratio (SNR) and a 25% reduction in power consumption compared to baseline methods, showcasing RL's potential for real-time, energy-efficient edge processing. Future applications of this framework could extend to larger IoT networks, employing federated learning for distributed optimization across interconnected low-power devices. © 2025 IEEE.

关键词： Reinforcement learning

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Adaptive Cyber Defence: Leveraging GANs for Simulating and Mitigating Advanced Network Attacks in IoT Environments 11th

Adaptive Cyber Defence: Leveraging GANs for Simulating and ...

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11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Rao, P. Kiran Chatterjee, Subarna Prakash, P. Suman Ramana, K. Seshadri Rajeev Gandhi Memorial College of Engineering and Technology Andra Pradesh Nandyal518501 India Department of CSE MS Ramaiah University of Applied Science Karnataka Bengaluru India Department of CAI G Pullaiah College of Engineering and Technology Andra Pradesh Kurnool India Department of CAI Ravindra College of Engineering for Women Andra Pradesh Kurnoo518001 India

ISBN: (纸本)9789819797615

The progressive dissemination of the Internet of Things (IoT) and Wireless Sensor networks (WSNs) has ushered in a new era of connectivity, with vast applications spanning from medicare to smart city infrastructure. However, this expansion has been paralleled by a corresponding increase in the sophistication and variety of cyber threats targeting these networks. Traditional cyber security measures, designed for a less dynamic threat landscape, are proving increasingly insufficient in protecting against the innovative and varied attack methods now in commonplace. This study introduces an innovative application of Generative Adversarial networks (GANs) to address this challenge, presenting a novel framework for the simulation and mitigation of advanced network attacks, particularly focusing on distributed Denial of Service (DDoS) and spoofing attacks which pose significant threats in IoT environments. Generative Adversarial networks (GANs), comprising two neural networks-the generator and the discriminator compete in a game-theoretic scenario, facilitating a deep understanding of attack patterns through the generation of realistic, synthetic cyber-attack scenarios. This research exploits GANs to bridge the gap between the static nature of traditional security protocols and the dynamic, evolving landscape of cyber threats. By training on a comprehensive dataset of known attacks and normal network activities, our proposed model, the Dynamic Adaptive Threat Simulation GAN (DATS-GAN), is capable of producing varied and realistic attack scenarios. These simulations serve a dual purpose: they not only enhance the detection capabilities and responsiveness of current security systems but also provide a basis for the development of new, adaptive security mechanisms capable of dynamically responding to the ever-changing cyber threat landscape. The effectiveness of DATS-GAN is demonstrated through extensive empirical analysis, highlighting significant improvements in the detecti

关键词： Generative adversarial networks

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Architecting Space Microdatacenters: A System-level Approach 31

Architecting Space Microdatacenters: A System-level Approach

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31st IEEE international symposium on High Performance computer Architecture, HPCA 2025

作者： Bleier, Nathan Eason, Rick Lembeck, Michael Kumar, Rakesh University of Michigan Department of Electrical Engineering & Computer Science Ann ArborMI United States University of Illinois Department of Electrical and Computer Engineering UrbanaIL United States University of Illinois Department of Aerospace Engineering UrbanaIL United States

ISBN: (纸本)9798331506476

Server-based computing in space has been recently proposed due to potential benefits in terms of capability, latency, security, sustainability, and cost. Despite this, there has been no work asking the question: how should we architect systems for server-based computing in space when considering overall cost. This paper presents a Total Cost of Ownership (TCO)-based approach to architecture of server-based computing systems for space (Space Microdatacenters - SμDC) for processing data produced by low Earth orbit (LEO)-based Earth observation (EO) satellites. We show that power of compute is the primary factor in determining SμDC TCO, though the dependence is sublinear. Second, the impact of compute mass, monetary cost, and communication on TCO is relatively insignificant. Third, architectures with the highest \frac{\text { FLOPs }}{W} provide much higher performance per TCO {\} even if they have poor \frac{FLOPs}{\}. We leverage these insights to advocate extreme heterogeneity designs for SμDCs. These designs reduce SμDC TCO by 116× in spite of poor \frac{{FLOPs}}{\} characteristics. We also show that (a) collaborative compute constellations - constellations in which EO satellites are also equipped with compute hardware - further improve SμDC TCO by 1.31 to 1.74×, (b) a distributed architecture reduces TCO by 10% over a monolithic architecture, and (c) low monetary cost of compute can be leveraged to provide near zero cost compute overprovisioning which improves an SμDC's availability significantly and supports graceful degradation. Overall, this is the first paper on cost-aware architecture and optimization of a SμDC. © 2025 IEEE.

关键词： Digital storage

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Certified Quantization Strategy Synthesis for Neural networks 26th

Certified Quantization Strategy Synthesis for Neural Network...

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26th international symposium on Formal Methods (FM)

作者： Zhang, Yedi Chen, Guangke Song, Fu Sun, Jun Dong, Jin Song Natl Univ Singapore Singapore 117417 Singapore ShanghaiTech Univ Shanghai 201210 Peoples R China Chinese Acad Sci Key Lab Syst Software Beijing 100190 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Nanjing Inst Software Technol Nanjing 211135 Peoples R China Singapore Management Univ Singapore 178902 Singapore

ISBN: (纸本)9783031711619;9783031711626

Quantization plays an important role in deploying neural networks on embedded, real-time systems with limited computing and storage resources (e.g., edge devices). It significantly reduces the model storage cost and improves inference efficiency by using fewer bits to represent the parameters. However, it was recently shown that critical properties may be broken after quantization, such as robustness and backdoor-freeness. In this work, we introduce the first method for synthesizing quantization strategies that verifiably maintain desired properties after quantization, leveraging a key insight that quantization leads to a data distribution shift in each layer. We propose to compute the preimage for each layer based on which the preceding layer is quantized, ensuring that the quantized reachable region of the preceding layer remains within the preimage. To tackle the challenge of computing the exact preimage, we propose an MILP-based method to compute its under-approximation. We implement our method into a tool Quadapter and demonstrate its effectiveness and efficiency by providing certified quantization that successfully preserves model robustness and backdoor-freeness.

关键词： Embedded systems

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Comparison of the Effectiveness of Face Recognition Algorithms in Terms of Photo Distortion Level 11th

Comparison of the Effectiveness of Face Recognition Algorith...

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11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Grabowski, Daniel Saeed, Khalid Faculty of Computer Science Bialystok University of Technology Bialystok Poland Department of Computer Science and Electronics Universidad de la Costa—CUC Barranquilla Colombia

ISBN: (纸本)9789819797615

This research explores the impact of photo distortion on face recognition algorithms performance, focusing on convolutional neural networks (CNNs) due to their superior efficiency. Through a comprehensive review of current solutions and empirical experimentation with the LFW image database, this study investigates how compression, blurring, and noise affect algorithmic accuracy. Experiments utilized the Python-based DeepFace library for comparative analysis of photo similarity, optimizing algorithm parameters through density analysis and decision trees. The study confirms that while compression minimally affects performance, noise—especially at higher levels—significantly degrades accuracy. However, applying median filters to noisy images largely restores effectiveness. Blurring’s impact varies with direction, with vertical blurring severely impairing recognition capabilities. Among the algorithms evaluated, FaceNet512 emerged as the most resilient in handling various types of distortions. The findings underscore the importance of distortion type and parameter optimization in maintaining recognition accuracy, offering valuable insights for enhancing algorithmic resilience against photo quality degradation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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