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LabStor: AModular and Extensible Platform for Developing High-Performance, Customized I/O Stacks in Userspace

LabStor: AModular and Extensible Platform for Developing Hig...

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international conference for High Performance computing, networking, Storage and Analysis (HPC)

作者： Logan, Luke Garcia, Jaime Cernuda Lofstead, Jay Sun, Xian-He Kougkas, Anthony Illinois Inst Technol Chicago IL 60616 USA Sandia Natl Labs Albequerque NM USA

ISBN: (纸本)9781665454445

Traditionally, I/O systems have been developed within the confines of a centralized OS kernel. This led to monolithic and rigid storage systems that are limited by low development speed, expressiveness, and performance. Various assumptions are imposed including reliance on the UNIX-file abstraction, the POSIX standard, and a narrow set of I/O policies. However, this monolithic design philosophy makes it difficult to develop and deploy new I/O approaches to satisfy the rapidly-evolving I/O requirements of modern scientific applications. To this end, we propose LabStor: a modular and extensible platform for developing high-performance, customized I/O stacks. Single-purpose I/O modules (e.g, I/O schedulers) can be developed in the comfort of userspace and released as plug-ins, while end-users can compose these modules to form workload- and hardware-specific I/O stacks. Evaluations show that by switching to a fully modular design, tailored I/O stacks can yield performance improvements of up to 60% in various applications.

关键词： Clouds and distributed computing Programming Frameworks and System Software

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Detection and Prevention of Advanced Persistent Threat (APT) Activities in Heterogeneous Networks using SIEM and Deep Learning 14

Detection and Prevention of Advanced Persistent Threat (APT)...

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14th international conference on computing Communication and networking Technologies, ICCCNT 2023

作者： Tharunika, V.S. Shridhar, T. Veeresh, K. Thangavel, Senthil Kumar Srinivasan, Karthik Vajipayajula, Sulakshan Tibrewal, Anjali Amrita Vishwa Vidyapeetham Computer Science and Engineering Coimbatore India India Pvt. Ltd. Ibm Security International Business Machines India

ISBN: (纸本)9798350335095

distributed Denial of Service (DDOS) in IOT based systems has become a vital consideration especially since the quintessential development of IOT based gadgets including small Personal Digital Assistants (PDA) to large computing systems. The stupendous and prolonged presence of such innovations have attracted potential attackers, encouraging them to carry out cyber-attacks and data theft. The key objective of this research is to detect and mitigate botnet-based distributed denial of service (DDoS) attacks in IoT networks. Our proposed model addresses the issue concerning threats from bots. Multiple machine learning models such as K-Nearest Neighbour (KNN), K-Means clustering, Fuzzy clustering and Deep learning models such as Conventional Neural Network (CNN), Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) were used to arrive at the model, wherein the model is trained by Bot-Iot dataset. Various preprocessing methods were equipped and Performance-based selection was done to choose algorithms from a collection using the reference point, (i.e) accuracy percentage. Feature selection and Synthetic minority oversampling technique (SMOTE) were also incorporated alongside Machine Learning (ML) algorithms. The results of the model indicates that the proposed architecture can effectively detect botnet-based attacks and also can be extended with corresponding architectures for unfolding attacks. © 2023 IEEE.

关键词： Network architecture

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 international conference on Power, Communication, computing and networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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Reducing Cloud Workload Costs in Geographically distributed Data Centers with GeoSched 14

Reducing Cloud Workload Costs in Geographically Distributed ...

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14th international conference on computing Communication and networking Technologies, ICCCNT 2023

作者： Sai, Vishnuvajjhula Pranav Raj, Harsh University Electronic and Communications Engineering Telangana Hyderabad India SRM Institute of Science and Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350335095

In this research paper, we present GeoSched, a job provisioning framework for geo-distributed data centers. Our approach takes into account parameters such as temperature and electricity prices to intelligently schedule jobs across different data centers. Through extensive simulations, we evaluate the performance of GeoSched and observe significant improvements in energy efficiency. Specifically, our meta-scheduler reduces energy consumption by 8.2% and decreases the cost of operation by 11.7%. These results highlight the effectiveness of our approach in minimizing data center operating costs while meeting workload demands and maintaining Service Level Agreements (SLAs). For future work, we plan to investigate additional factors that impact energy consumption, including the availability of renewable energy sources and the energy required for data transfer. We also aim to develop more comprehensive SLAs that can capture the complex and diverse requirements of cloud workloads, such as response time, availability, throughput, and data consistency. Furthermore, we recognize the importance of considering workload diversity in our meta-scheduling approach. Different types of workloads have varying requirements and priorities, and accommodating them is crucial for optimizing resource allocation and job provisioning. Additionally, we intend to explore the scalability of our framework for larger and more intricate data center networks. This would involve developing more sophisticated resource management strategies and algorithms to handle the complexities of scaling up. By addressing these areas of future research, we aim to further enhance the energy efficiency and performance of geo-distributed data centers while providing robust SLA adherence. © 2023 IEEE.

关键词： Energy efficiency

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DDoS Attack Detection Based on Information Entropy Feature Extraction in Software Defined Networks

DDoS Attack Detection Based on Information Entropy Feature E...

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2023 international conference on networking and Network Applications, NaNA 2023

作者： Ji, Wenjiang Yang, Yixin Zhang, Yaling Wang, Yichuan Tian, Mengjie Qiu, Yuan Xi'an University of Technology School of Computer Science and Engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9798350327380

distributed Denial of Service(DDoS) attacks target the forwarding-control separation feature of Software-Defined networking(SDN) to launch attacks, causing network disruptions. Therefore security against DDoS attack detection for SDN controllers is the focus of current research. This paper proposes an Extreme Gradient Boosting (XGBoost) DDoS attack detection algorithm based on a combination of information gain and recursive feature elimination algorithms. We evaluated the performance of the method using the CICDDoS2019 dataset. This method works well in multi-classification model for attack detection with an accuracy of 93.41%. © 2023 IEEE.

关键词： Software defined networking

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2023 5th international conference on Blockchain computing and Applications, BCCA 2023

2023 5th International Conference on Blockchain Computing an...

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5th international conference on Blockchain computing and Applications, BCCA 2023

ISBN: (纸本)9798350339239

The proceedings contain 89 papers. The topics discussed include: automating and decentralizing satellite-based emergency mapping;scalable blockchain system for load balancing DNS servers using heuristic sharding;blockchain-based data sharing platform customization with on/off-chain data balancing;metaverse through blockchain and intelligent networking: a comprehensive survey;blockchain-based federated learning for decentralized energy management systems;deep learning/PUF-based item identification for supply chain management in a distributed ledger framework;routing fee adjustment strategies in payment channels;towards privacy-aware federated learning for user-sensitive data;Qwallet: a hybrid cryptocurrency wallet using quantum RNG;and blockchain technology in IoT and IIoT environments.

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Data Security Storage Scheme For UAV Cluster Based On distributed Storage

Data Security Storage Scheme For UAV Cluster Based On Distri...

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2023 international conference on networking and Network Applications, NaNA 2023

作者： Wu, Kaijun Tian, Bo Wang, Xue Science and Technology on Communication Security Laboratory Chengdu610041 China No.30 Research Institute of China Electronics Technology Group Corporation Chengdu610041 China China Electronics Technology Cyber Security Co. Ltd Chengdu610041 China

ISBN: (纸本)9798350327380

UAVs have been widely used in various fields such as emergency rescue and disaster relief, weather detection, scientific research measurement, aerial surveying and mapping, and military reconnaissance. The concept of unmanned aerial vehicle (UAV) swarm is proposed and used, which solves the disadvantages of a single unmanned aerial vehicle with small load and weak information processing and storage capabilities. UAV swarms integrate computing resources and storage resources through the interconnection of individual UAVs, effectively improving the load capacity and information storage capabilities of unmanned swarms. However, there are many uncertain privacy issues in the use of UAV clusters. UAVs are at risk of being damaged, lost and captured, and the data stored on UAVs will be leaked. Therefore, it is necessary to build a safe storage scheme for unmanned swarm data to ensure that the data of unmanned swarms can be stored and used safely. This paper proposes a UAV cluster data security storage scheme based on distributed storage. Each UAV acts as a storage node to build an interstellar file system for distributed storage and shared files. UAV clusters use this system to encrypt and protect data, redundant fragmentation and distributed storage to realize the concealment and confidentiality of distributed storage and sharing of UAV cluster data. Ensure the integrity of data and enhance the invulnerability of safe storage of UAV cluster data. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Detecting distributed Denial Of Service Attacks In Software Defined Network Controllers: Proposed Research 7

Detecting Distributed Denial Of Service Attacks In Software ...

引用

7th international conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Kunna, Etidal Altom Omar, Mohd Nizam Zolkipli, Mohamad Fadli bin InterNetworks Lab School of Computing Universiti Utara Malaysia UUM Sintok Malaysia

ISBN: (纸本)9798331519513

This paper addresses the security vulnerabilities in Software Defined networking (SDN), which separates the network control plane from the data plane. A significant concern is the vulnerability of the SDN controller to distributed Denial of Service (DDoS) attacks, which can disrupt or collapse the entire network. To tackle this issue, a hybrid detection is proposed to detect and mitigate DDoS attacks in two phases. The effectiveness of proposed hybrid detection will be evaluated using the Mininet simulator, with a focus on detection rate and false positive rates and compared to existing DDoS detection techniques. Following the Design Research Methodology (DRM) approach, the research will develop and implement hybrid to enhance SDN network security. The findings are expected to demonstrate the mechanism's ability to protect the SDN controller from DDoS attacks, contributing to the overall resilience of SDN architectures. As mentioned in the title, this paper only focused on the proposed research that to be conducted based on the explained literature review. © 2024 IEEE.

关键词： Denial-of-service attack

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Reputation Systems for Supply Chains: The Challenge of Achieving Privacy Preservation 20th

Reputation Systems for Supply Chains: The Challenge of Achie...

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20th EAI international conference on Mobile and Ubiquitous Systems - computing networking and Services (EAI MobiQuitous)

作者： Bader, Lennart Pennekamp, Jan Thevaraj, Emildeon Spiss, Maria Kanhere, Salil S. Wehrle, Klaus Fraunhofer FKIE Cyber Anal & Def Wachtberg Germany Rhein Westfal TH Aachen Commun & Distributed Syst Aachen Germany Rhein Westfal TH Aachen Inst Ind Management Aachen Germany Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9783031639883;9783031639890

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs.

关键词： SCM confidentiality anonymity voter votee FHE

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Multi-UAV Cooperative Search in Multi-layered Aerial computing Networks: A Multi-Agent Deep Reinforcement Learning Approach 59

Multi-UAV Cooperative Search in Multi-layered Aerial Computi...

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59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Wu, Jiaqi Luo, Jingjing Jiang, Changkun Gao, Lin Harbin Inst Technol Sch Elect & Informat Engn Shenzhen Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen Peoples R China Shenzhen Univ Dept Comp Engn Shenzhen Peoples R China

ISBN: (纸本)9781728190549

Multi-UAV Cooperative Search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a Multi-layered Aerial computing Network (MACN) scenario, which consists of a Low-Altitude Platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a High-Altitude Platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of Search Probability Map (SPM). The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach based on Parameter Sharing and Action Mask (PSAM), called PSAMMA, where the State-Action-Reward-State-Action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that the proposed PSAMMA algorithm outperforms existing methods in terms of the average SPM uncertainty, the target discovery rate, and the coverage rate.

关键词： Resource allocation

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