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Resource investment for DDoS attack resistant SDN: a practical assessment

science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

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Journal of computer science & Technology 2024年第2期39卷 401-420页

作者：龚成卢冶代素蓉邓倩杜承昆李涛 College of Software Nankai UniversityTianjin 300350China College of Computer Science Nankai UniversityTianjin 300350China State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and *** exploration implies heavy workloads for domain experts,and an automatic compression method is ***,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real *** this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human *** can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the *** is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our *** optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory *** is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network *** have implemented AutoQNN and integrated it into *** experiments demonstrate that AutoQNN can consistently outperform state-of-the-art *** 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art ***,c

关键词： automatic quantization mixed precision quantizing scheme search quantizing precision learning quan-tized architecture generation

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Out-Of-Distribution Detection with Diversification (Provably) 38

Out-Of-Distribution Detection with Diversification (Provably...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yao, Haiyun Han, Zongbo Fu, Huazhu Peng, Xi Hu, Qinghua Zhang, Changqing College of Intelligence and Computing Tianjin University China Institute of High Performance Computing A*STAR Singapore College of Computer Science Sichuan University China

Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers (e.g., data from the web or other datasets) in training. However, we experimentally reveal that these methods still struggle to generalize their detection capabilities to unknown OOD data, due to the limited diversity of the auxiliary outliers collected. Therefore, we thoroughly examine this problem from the generalization perspective and demonstrate that a more diverse set of auxiliary outliers is essential for enhancing the detection capabilities. However, in practice, it is difficult and costly to collect sufficiently diverse auxiliary outlier data. Therefore, we propose a simple yet practical approach with a theoretical guarantee, termed Diversity-induced Mixup for OOD detection (diverseMix), which enhances the diversity of the auxiliary outlier set for training in an efficient way. Extensive experiments show that diverseMix achieves superior performance on commonly used and recent challenging large-scale benchmarks, which further confirm the importance of the diversity of auxiliary outliers. Our code is available at https://***/HaiyunYao/diverseMix. © 2024 Neural information processing systems foundation. All rights reserved.

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Simulating a Chern Insulator with C=±2 on Synthetic Floquet Lattice

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Chinese Physics Letters 2024年第9期41卷 1-8页

作者： Ling-Xiao Lei Wei-Chen Wang Guang-Yao Huang Shun Hu Xi Cao Xin-Fang Zhang Ming-Tang Deng Ping-Xing Chen Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha 410073China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Greatwall Quantum Laboratory Changsha 410006China Hefei National Laboratory Hefei 230088China

The synthetic Floquet lattice,generated by multiple strong drives with mutually incommensurate frequencies,provides a powerful platform for quantum simulation of topological *** this study,we propose a 4-band tight-binding model of the Chern insulator with a Chern number C=±2 by coupling two layers of the half Bernevig–Hughes–Zhang lattice and subsequently mapping it onto the Floquet lattice to simulate its topological *** determine the Chern number of our Floquet-version model,we extend the energy pumping method proposed by Martin et al.[2017 ***.X 7041008]and the topological oscillation method introduced by Boyers et al.[2020 ***.125160505],followed by numerical simulations for both *** simulation results demonstrate the successful extraction of the Chern number using either of these methods,providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half Bernevig–Hughes–Zhang ***,we briefly discuss a potential experimental implementation for our *** work demonstrates significant potential for simulating complex topological matter using quantum computing platforms,thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena.

关键词： properties topological quantum

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An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT 7

An Interpretation of the Challenges and Solutions for Agricu...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Surya, M. Manohar, S. Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamilnadu India

ISBN: (纸本)9781665464086

Healthcare, the smart grid, and supply chain management are some of the industries where IoT(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, and the people around them. Internet of things achieves this by using wearable technology, embedded sensors, and automated systems. The number, types, and sizes of IoT devices are increasing. There are issues with security, authenticity, reliability, and scalability. Because of the expected growth in IoT, IoT systems must be reliable and maintainable. Blockchain has helped solve IoT network problems. Blockchain improves trust, immutability, transparency, and release of agricultural data collection, processing, and sharing. Blockchain and IOT will streamline precision farming and transform normal farms into the Internet of Smart Farms. This study describes the IoT systems have issues, and blockchain-based solutions are suggested. Additionally, it discusses current research on the degree to which blockchain technology and Internet of Things networks. IoT integration inside the blockchain issues are also covered. This article proposes to double connect IoT and blockchain using recent computing techniques. This research work intends to make data processing and storage decentralized, secure and anonymous, and seamless and fast using blockchain technology and services. © 2023 IEEE.

关键词： Cloud computing architecture

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Quantum Polynomial Hierarchies: Karp-Lipton, Error Reduction, and Lower Bounds 49

Quantum Polynomial Hierarchies: Karp-Lipton, Error Reduction...

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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Agarwal, Avantika Gharibian, Sevag Koppula, Venkata Rudolph, Dorian David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo Canada Paderborn University Germany Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9783959773355

The Polynomial-Time Hierarchy (PH) is a staple of classical complexity theory, with applications spanning randomized computation to circuit lower bounds to "quantum advantage" analyses for near-term quantum computers. Quantumly, however, despite the fact that at least four definitions of quantum PH exist, it has been challenging to prove analogues for these of even basic facts from PH. This work studies three quantum-verifier based generalizations of PH, two of which are from [Gharibian, Santha, Sikora, Sundaram, Yirka, 2022] and use classical strings (QCPH) and quantum mixed states (QPH) as proofs, and one of which is new to this work, utilizing quantum pure states (pureQPH) as proofs. We first resolve several open problems from [GSSSY22], including a collapse theorem and a Karp-Lipton theorem for QCPH. Then, for our new class pureQPH, we show one-sided error reduction pureQPH, as well as the first bounds relating these quantum variants of PH, namely QCPH ⊆ pureQPH ⊆ EXPPP © Avantika Agarwal, Sevag Gharibian, Venkata Koppula, and Dorian Rudolph.

关键词： Quantum computers

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Advanced Optimizing Network Security in Manets: A Comprehensive Multilevel-Optimization and Monitoring Mechanism (Multi-Omm)

Advanced Optimizing Network Security in Manets: A Comprehens...

引用

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Aravind, A. Poongodi, A. School of Computing Sciences Vels Institute of Science Technology & Advance Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

With the proliferation of the Internet of Things (IoT), mobile ad hoc networks (MANET) have become an essential component of contemporary wireless communications. These networks, which rely on multi-hop data transmission, offer significant flexibility and adaptability. However, their open and dynamic nature makes them vulnerable to various internal and external security threats. Among these, Black hole attacks by malicious nodes present serious challenges. Traditional security measures, such as firewalls and encryption, often fall short in addressing these threats. Therefore, there is an urgent need for advanced intrusion detection systems tailored for these environments. Additionally, node selfishness-where nodes conserve their resources at the expense of others-further disrupts network performance and violates protocols. To address these issues, we propose the Multilevel Optimization and Monitoring Mechanism (Multi-OMM), a novel algorithm designed for real-time detection and prevention of malicious activities. Multi-OMM employs a multi-phase approach to enhance network security through traffic pattern analysis, dynamic topology inspection, and targeted attack detection. By implementing and evaluating Multi-OMM through comprehensive experiments, we aim to demonstrate its effectiveness in identifying and mitigating both selfish and malicious behaviors, thereby improving overall network security and performance. © 2024 IEEE.

关键词： Internet of things

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Enhancing Heart Disease Prediction Through a Heterogeneous Ensemble DL Models 4th

Enhancing Heart Disease Prediction Through a Heterogeneous E...

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4th EAI International Conference on Cognitive computing and Cyber Physical Systems, IC4S 2023

作者： Naidu, J. N. S. S. Janardhana Varma, Mudunuri Aniketh Madhuri, P. Shyamala Shankar, D. Matta, Durga Satish Ramya, Singaraju Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031488870

Accurate and highly effective forecasting approaches for timely detection and management are imperative given that cardiovascular illness is one of the biggest causes of death world wide. Machine learning (ML) and Deep learning (DL) methodologies have produced promising outcomes. In particular, ensemble DL models have gained attention for their ability in order to capitalize on the advantages of many models to enhance predictive performance. This study focuses on applying a cardiovascular disease prognosis with a DL ensemble. The model combines the predictions of multiple DL models, networks of neurons, such as supervised DL Models (CNN and RNN), to magnify accuracy and robustness. In this research, the effectiveness of an ensemble model is measured using an ample dataset, comparing it with individual DL models. The model’s prediction skills are evaluated by utilizing a verity of Evaluation metrics. The findings highlight the effectiveness of cardiovascular disease ensemble DL models prediction, showcasing their potential for enhancing diagnostic accuracy in clinical settings and aiding healthcare professionals in making informed decisions for patient care. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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Integration of IoT-enabled UAVs in Smart Agriculture: Types, Applications, Trends, challenges 7

Integration of IoT-enabled UAVs in Smart Agriculture: Types,...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Prathyusha, Gajawada Krishnan, M.B.Mukesh Deepak, S.Prem Kumar SRM Institute of Science and Technology School of Computing Department of Networking and Communications India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for various applications, including the military, smart agriculture, rescue operations, entertainment, monitoring, and medicine delivery. In the current era, UAVs extensively employ a wide range of technologies to transmit data to base stations. The collected data undergoes processing, producing remarkable outcomes through the integration of machine learning and deep learning technologies. This paper reviews disease identification and object detection techniques and their associated applications & challenges. Additionally, the research explores recent trends and technologies in the field of smart agriculture. This research emphasizes the use of sensors in unmanned aerial vehicles (UAVs) by analyzing the methods utilized for recognizing diseased areas and things while identifying insects and sick crops. This paper also addresses the challenges encountered by existing methodologies and proposes directions for future research and development. Furthermore, this review delves into suitable techniques for heterogeneous UAVs and examines IoT-related strategies. © 2024 IEEE.

关键词： Smart agriculture

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ZeTFRi - A Zero Trust-Based Free Rider Detection Framework for Next Generation Federated Learning Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1938-1953页

作者： Edirimannage, Shehan Khalil, Ibrahim Elvitigala, Charitha Daluwatta, Wathsara Wijesekera, Primal Zomaya, Albert Y. RMIT University School of Computing Technologies MelbourneVIC3000 Australia International Computer Science Institute University of California BerkeleyCA94704 United States University of Sydney Centre for Distributed and High-Performance Computing SydneyNSW2006 Australia

With the rapid expansion of next-generation networking, Internet of Things (IoT) devices have become central components of federated learning (FL) networks. FL offers a paradigm for distributed training machine learning models while preserving user data privacy. However, existing network security measures often struggle to identify legitimate contributors from opportunistic free riders within these networks. The Free Rider (FR) problem arises when participants seek to benefit from the FL processes without contributing. In particular, free riders are known to exist within or outside of the network, whereas outside free riders can hardly be identified. The Zero Trust model proposes an environment where no entity, including the network itself, is inherently trusted, providing a foundation to counter external threats seeking to exploit the network. This study proposes a novel framework strengthened by the Zero Trust model to identify external free riders in FL networks. Leveraging a Deep Autoencoding Gaussian Mixture Model (DAGMM)-based technique for internal free rider detection, our framework demonstrates superior performance in identifying free riders across various FR scenarios compared to current state-of-the-art solutions. Through our proposed framework and the principles of Zero Trust, we establish a robust security guarantee for FL networks, ensuring the integrity of the learning process. © 1983-2012 IEEE.

关键词： Federated learning

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