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 ...
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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 ...
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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...
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作者:
Chen, ShengboLi, ShuaiWang, GuanghuiYu, KepingHenan 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 perf...
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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.
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 ...
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).
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 Po...
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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. H...
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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
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 s...
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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 i...
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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.
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 cu...
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