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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th international conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential processing techniques. This paper proposes a Multi-Core parallel processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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Colony: parallel Functions as a Service on the Cloud-Edge Continuum 27th

Colony: Parallel Functions as a Service on the Cloud-Edge Co...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Lordan, Francesc Lezzi, Daniele Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual -Agent-, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models;thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs;thus, developers can efficiently code applications of any of the three envisaged computing scenarios - sense-process-actuate, streaming and batch processing - throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them.

关键词： Edge Fog Cloud Compute Continuum distributed systems Programming model Runtime system Serverless Function-as-a-Service Stream-processing Task-based workflow

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Low Error Floor QC-LDPC Codes Construction Using Modified Cole's Trapping Sets Enumerating

Low Error Floor QC-LDPC Codes Construction Using Modified Co...

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Digital Signal processing and its applications (DSPA), international conference on

作者： Vasiliy S. Usatyuk Research and Development Department T8 Moscow Russia

In order to count the number of trapping sets (TS), we changed Cole's Importance Sampling (IS) approach, which led to a message-passing decoder problem. Several concepts have been combined to suggest improvements to Cole's IS: utilizing short cycles and simultaneous TS impulse tree decomposition, un-wrapping of message passing iterations Tanner Graph/Forney's Normal Graph symmetry's Graph Authomorphism is a complex yet simple concept. Superior Velasquez-Subramani and Karimi-Banihashemi TS enumerating techniques were supported. The proposed technique under PEG (1008, 504) Mackay code for single thread implementation is 43 times quicker than the original Cole's method and 5027 times (71463 times, multi-thread) faster than the Velasquez-Subramani LP method. For the TS enumerating problem at (2640, 1320), Margulis's code is 37958 times quicker than the suggested Velasquez-Subramani LP approach for single thread implementation, 82 times faster than Karimi-Banihashemi, and 134 times faster than Cole's original solution. On the basis of the creation of QC-LDPC codes, we demonstrate the effects of improved EMD spectrum and increased hamming distance on the TS spectrum and BER/FER error-floor level.

关键词： Codes Monte Carlo methods Message passing Simulated annealing Digital signal processing parallel processing Decoding

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PQMPV: parallel and Quantified Dense Stereo Disparity Estimation Based on Multi-Path Viterbi

PQMPV: Parallel and Quantified Dense Stereo Disparity Estima...

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international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Rongqi Gu Tianhang Wang Tianpei Zou Bo Zhang Zhijun Li Haotian Zhang Guang Chen Department of Automotive Engineering Tongji University Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Automation University of Science and Technology of China Hefei China Department of Computer Science and Technology Tongji University Shanghai China

Stereo depth estimation plays a crucial role in enabling robots, autonomous driving and other applications. As an ill-posed problem, both deep learning based and traditional methods have been extensively studied in recent years. Traditional methods mainly focus on feature matching between multiple images to estimate depth, but often result in blank pixels with zero disparity and also require significant computation time. In this paper, we propose a new stereo disparity estimation algorithm based on the dynamic programming method known as Viterbi. Our approach optimize the range of disparity calculation through image pyramid, combined with Viterbi algorithm for cost aggregation, and accelerates several calculation steps through parallel computing techniques. Additionally, we improve data transmission and processing speed by quantifying data from float to integer. Experiments on the KITTI dataset and MiddEval benchmark demonstrate that our algorithm achieves significant improvements in average error rate and processing speed of disparity map, making it a promising solution for stereo depth estimation in practical applications.

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SimplePIM: A Software Framework for Productive and Efficient processing-in-Memory 23

SimplePIM: A Software Framework for Productive and Efficient...

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Proceedings of the 32nd international conference on parallel Architectures and Compilation techniques

作者： Jinfan Chen Juan Gómez-Luna Izzat El Hajj Yuxin Guo Onur Mutlu ETH Zürich American University of Beirut

ISBN: (纸本)9798350342543

Data movement between memory and processors is a major bottleneck in modern computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this bottleneck by performing computation inside memory chips. Real PIM hardware (e.g., the UPMEM system) is now available and has demonstrated potential in many applications. However, programming such real PIM hardware remains a challenge for many *** paper presents a new software framework, SimplePIM, to aid programming real PIM systems. The framework processes arrays of arbitrary elements on a PIM device by calling iterator functions from the host and provides primitives for communication among PIM cores and between PIM and the host system. We implement SimplePIM for the UPMEM PIM system and evaluate it on six major applications. Our results show that SimplePIM enables 66.5% to 83.1% reduction in lines of code in PIM programs. The resulting code leads to higher performance (between 10% and 37% speedup) than hand-optimized code in three applications and provides comparable performance in three others. SimplePIM is fully and freely available at https://***/CMU-SAFARI/SimplePIM.

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Medical Image Analysis using distributed Deep Learning Models

Medical Image Analysis using Distributed Deep Learning Model...

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Global conference for Advancement in Technology (GCAT)

作者： Raji Ramachandran Abhiram Prasad Ritika R Prasad Vysakh S Nair Lakshmi G Pillai Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Medical image analysis plays a crucial role in modern medicine, serving as an indispensable tool for clinicians to enhance diagnostic precision and establish personalized treatment programs. Our team has devised an innovative method for medical image analysis, employing distributed deep learning techniques for both COVID-19 and brain tumor datasets. Traditional approaches to medical image analysis often demand significant computational resources and extensive data, rendering their implementation challenging in real-world scenarios. In order to overcome these obstacles, we propose a distributed deep learning framework that enables parallel processing of medical images across multiple nodes. Our framework leverages convolutional neural networks and transfer learning to achieve exceptional accuracy in the detection of COVID-19 and brain tumors from inputted images, respectively. Through the evaluation of an extensive dataset of medical images, we have successfully demonstrated the efficiency of our approach in accurately detecting these conditions

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A Short Survey of AI-driven Load Distribution for Electronic Health Record Management with Recent Deployed techniques, Research Gaps and Challenges

A Short Survey of AI-driven Load Distribution for Electronic...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： S. Suryanarayanaraju M.Chandra Naik R.N. V Jagan Mohan Department of Computer Science & Engineering GIET University Gunupur Odisha Department of Computer Science & Engineering SRKR Engineering College (A) Chinaamiram

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

With the arrival of Artificial Intelligence (AI), the Internet of Things (IoT), machine learning, and deep learning methodologies, data-driven medical applications have appeared as a promising solution for implementing scalable and robust prognostic and diagnostic approaches with the utilization of medical data. These technologies attained a lot of interest from academia and industry and led to insightful advancements in healthcare systems. The AI-driven techniques related to medical applications still face several issues including privacy, security, and Quality-of-Service (QoS) standards. The complex machine learning models are effectively trained via Federated Learning (FL) in a distributed manner, this advanced technique has become an attractive research area, especially in processing healthcare data at the network edge in a decentralized way to address security concerns and preserve privacy of the information. The availability of FL in the medical domain has several benefits, but it has not been explored to its extent. Existing review works related to FL mainly concentrated on the importance of FL in diverse applications, but no brief or comprehensive survey exists on the distribution of Electronic Health Records (EHR). Thus, this review work focuses on various techniques of AI-driven load distribution for federated networks on EHR. For this purpose, a detailed literature survey is conducted on AI-driven load distribution framework for federated networks on EHR from the previous decades. The AI techniques employed for disease detection are analyzed and categorized. The dataset details, performance metrics, and simulation tools are gathered and listed for a better understanding of the process. Subsequently, the research gaps on the conventional AI-driven load distribution framework for federated networks on EHR are provided to facilitate future work.

关键词： Surveys Data privacy Reviews Medical services Quality of service Security Internet of Things Electronic medical records Medical diagnostic imaging Diseases

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Big Data-Driven Computational Aptamer Design Framework via parallel Monte Carlo Tree Search

Big Data-Driven Computational Aptamer Design Framework via P...

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IEEE international conference on Big Data

作者： Jinglin Jian Zhiheng Jiao Zihan Li Jin Chen School of Information Sciences University of Illinois Urbana-Champaign Champaign US Independent Researcher San Francisco US Cleveland Clinic Cleveland OH

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Computational aptamer design using Monte Carlo Tree Search (MCTS) shows promise in drug discovery but is limited by sequential computing and simulation, which hinders the exploration of vast nucleotide sequence spaces and the integration of high-quality molecular docking techniques. We present a big data-driven parallel MCTS framework that significantly accelerates aptamer discovery while maintaining high accuracy. Our framework addresses these computational bottlenecks by distributing tasks across multiple processing units, enabling simultaneous exploration of diverse sequence regions and efficient incorporation of RNA/ssDNA-protein docking simulations for candidate evaluation. Key designs include a distributed search architecture with molecular docking, a score penalty system for optimized exploration, and comprehensive RNA/ssDNA docking pipelines that combine structure prediction with protein docking simulations. Experimental results targeting human complement component protein (i.e., protein 2A73) demonstrate a 98-fold increase in daily successful candidate discovery and improved binding affinity compared to the baseline, with the successful ratio increasing from 1% to 7.59%. Our approach not only accelerates the aptamer design process but also enhances the quality of generated aptamers, providing a more efficient and effective tool for drug discovery.

关键词： Proteins Monte Carlo methods Accuracy Pipelines Production Transformers Stability analysis Drug discovery Computational efficiency Reliability

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Economical Electricity Supplement for Mobile Collectors in Large-Scale Multitask WSNs

Economical Electricity Supplement for Mobile Collectors in L...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Xin Zhai Lijie Xu Jialei Zhang Kun Wang Jia Xu Bei Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations.

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A Moving Target Defense Approach for the distributed Dynamic Network

A Moving Target Defense Approach for the Distributed Dynamic...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Lin Zhang Yunchuan Guo Siyuan Leng Zifu Li Fenghua Li Liang Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed Network Address Shuffling (DNAS), is proposed to thwart attackers’ network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural Network (FCNN) to recognize the attacker’s scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning.

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