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Detection of A Novel Dual Attack in Named Data Networking

Detection of A Novel Dual Attack in Named Data Networking

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

作者： Liang Liu Silin Peng College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Electronic Information and Automation Civil Aviation University of China Tianjin China

distributed Denial of Service (DDoS) attacks in Named Data Networking (NDN), such as Cache Pollution Attacks (CPA) and Improved Collusive Interest Flooding Attacks (I-CIFA), can significantly threaten the NDN network. However, most of the previous research has focused on the detection of a single attack by using Machine Learning algorithms and threshold-based methods, so the previous methods are not efficient enough for detecting dual attacks. This paper first proposes a dual attack by combining I-CIFA and CPA, and extracts the network traffic features, including the number of CacheMisses and the number of PIT entries. After analyzing the severe impact of the dual attack, a detection scheme BO-CatBoost is proposed based on Bayesian optimization and CatBoost. Finally, the experiment results show that the proposed detection scheme is robust in detecting the dual attack.

关键词： Pollution Machine learning algorithms distributed databases Telecommunication traffic Feature extraction Denial-of-service attack Bayes methods

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video processing

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： M. Ashok Kumar Laith H. Jasim Sudhakar K R. Archana Reddy N. Naga Saranya Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%).

关键词： Geometry Deformable models Visualization Three-dimensional displays Sensitivity Event detection Feature extraction

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Influence of optimization techniques on software performance for subsequent generations of CUDA architecture

Influence of optimization techniques on software performance...

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

作者： Jacek Gambrych Warsaw University of Technology Institute of Electronic Systems Warsaw Poland

From the very beginning of the CUDA technology, it was essential to apply state-of-the-art optimization techniques. Only then was it possible to fully utilize the enormous computational power of graphic processing units. However, with the development of the CUDA architecture, the impact of typical optimization techniques on software performance has changed significantly. This article shows how the impact of several optimization techniques on the performance of the image filtering algorithm has changed for the subsequent generations of CUDA architecture. Then, based on the results obtained, it attempts to answer whether tedious and time-consuming optimization of the CUDA software is still necessary.

关键词： Software algorithms Memory management Graphics processing units Software performance Image filtering Kernel Optimization

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Combining OFDM-MFSK and Chirp Spread Spectrum Modulation for applications in Low-Power Wide-Area Networks 15

Combining OFDM-MFSK and Chirp Spread Spectrum Modulation for...

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15th international conference on Signal processing and Communication Systems (ICSPCS)

作者： Thiasiriphet, Thanawat Teich, Werner G. Chiang Mai Univ Dept Elect Engn Fac Engn Ctr Mechatron Syst & Innovat Chiang Mai Thailand Ulm Univ Inst Commun Engn D-89069 Ulm Germany

ISBN: (纸本)9781665436991

Chirp spread spectrum (CSS) has attracted a lot of interest in the last few years, especially in the field of low-power wide-area networks (LPWAN). One of the most commonly employed LPWANs solution is LoRaWAN, which has LoRa as its physical layer. The concept of LoRa is to use the combination of CSS and M-ary frequency shift keying (M-FSK) for transmission. LoRa is known to be highly robust and well-suited for low-complexity implementation but the weak aspect is in its low spectral efficiency. Orthogonal frequency division multiplexing M-FSK (OFDM-MFSK) is a technique which combine M-FSK with OFDM. For this method, the transmission bandwidth is divided to several parallel M-FSK transmission, i.e. more than one sub-carrier can be active at a time, for improving the spectral efficiency. The reception of OFDM-MFSK can be done non-coherently, therefore it holds many similar design principles as LoRa. The problematic scenarios for OFDM-MFSK are for the case with multipath-induced frequency selective channel and for the channel with presence of strong narrowband interference. We propose the combination of OFDM-MFSK and CSS in this paper. The weakness and strength of the two techniques complement each other well. The implementation of this combination is simple, since CSS can be viewed as special case of direct sequence spread spectrum and only chirp signal multiplication at the transmitter and the receiver are needed. Simulation results show that the proposed method provide improvement compared to OFDM-MFSK in both multipath-induced frequency selective channel and for the channel with presence of strong narrowband interference.

关键词： OFDM-MFSK chirp spread spectrum LPWAN IoT

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OpenCLTM Implementation of Rapid Image Restoration Kernels Based on Blind/Non-blind Deconvolution techniques for Heterogeneous parallel Systems 5th

OpenCLTM Implementation of Rapid Image Restoration Kernels B...

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5th international conference on Microelectronics, Computing and Communication Systems, MCCS 2020

作者： Satapathy, Ashutosh Livingston, L. M. Jenila SCOPE Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9789811602740

Over the years, image enhancement is one of the research areas in the field of image processing and computer vision. In the real world, images or videos captured and transmitted by different sensors are not fit for further processing. Image restoration is the sub-branch of image enhancement used to reconstruct the original image from blur images using restoration models. It plays an immense role in many real-time applications such as traffic monitoring, vigilance system, human identification, and recognition, while it also requires faster implementation. In this paper, we have discussed different image degradation models and the most well-known restoration techniques such as Inverse, Pseudo Inverse filter, Wiener filter, Constraint Least Square Error filter, and Lucy Richardson filter are used to reestablish an original image from a distorted one. Apart from that, we have developed OpenCL kernels for those above filters for faster image reconstruction, which are also easily deployed on multiple heterogeneous parallel systems. At last, we have done a performance analysis of both OpenCL CPU and GPU implementation of the above restoration techniques in terms of accuracy and time consumption. Different performance metrics like Root Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, and Structural Similarity Index have used to evaluate the accuracy of the above techniques. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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Fault Tolerance and Error Handling techniques in Apache Kafka 23

Fault Tolerance and Error Handling Techniques in Apache Kafk...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Shubham Vyas Rajesh Kumar Tyagi Shashank Sahu Information Technology Amity Institute of Information Technology India Computer Science and Engineering Amity School of Engineering and Technology Amity University Haryana India Computer Science and Engineering Ajay Kumar Garg Engineering College India

ISBN: (纸本)9798400709418

Real Time streaming framework enables the availability of data for users in near real time. For achieving the high throughput, low latency and to avoid loss of any events at runtime, real time streaming frameworks has their own methods to accomplish this task of message handling, but there are scenarios when due to huge volume of data at the source or due to other environment or Infrastructure related issues, some of the critical data might get lost in the process. This article evaluates methods to handle data rejections in conjunction with a fault-tolerant error handling recommendations for real time data streaming. There are many frameworks available for streaming the events in real time, in this article Apache Kafka is used to examine most frequent situations when an error can occur. This article will evaluate the key performance factors in terms of capability of the framework to recover when downtime occurs. Apache Kafka [15] is a distributed real time data streaming platform; it was developed by Linked inn and currently used by many applications across the industry. It works in producer consumer fashion and with correct configuration and set up, high Throughput with low latency can be achieved. This article will explain the configurations provided with the framework that can be utilized to design and develop optimized event streaming applications. This article is explaining the different parameters that can be used to design and develop the stream processing applications and what are the available options to handle the errors during the streaming to avoid any negative impact to the application.

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An Optimized Scheduling Algorithm for the Multi-channel Hyperledger Fabric

An Optimized Scheduling Algorithm for the Multi-channel Hype...

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

作者： Ou Wu Shanshan Li He Zhang Liwen Liu Yanze Wang Haoming Li State Key Laboratory of Novel Software Technology Nanjing University China Software Institute Nanjing University China

Hyperledger Fabric (Fabric for short), a typical consortium blockchain platform, has released many versions that support different consensus protocols to ensure that the system can withstand faults (such as crashes, network partitions, or network shutdowns). However, if there is a malicious node in the system, Fabric cannot guarantee any behavior of the malicious node. Therefore, when a certain number of nodes are attacked simultaneously, it directly affects the security and efficiency of the entire system. In the face of multi-channel Fabric network architecture, the parallel processing of transactions by node groups linked on different channels significantly improves the throughput of the Fabric network. However, it may also cause system congestion and safety problems. To solve this problem, this paper designs a Lyapunov-based scheduling algorithm for the performance optimization of Fabric. When considering the system's security probability and queue accumulation, we give the maximum consensus verification rate under the minimum security probability guarantee. Finally, we installed the latest stable version v2.0 of Fabric on the cloud server to verify the algorithm's effectiveness.

关键词： Scheduling algorithms distributed ledger Network architecture Throughput Fabrics Scheduling Peer-to-peer computing

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Comparative study on skyline query processing techniques on big data 4

Comparative study on skyline query processing techniques on ...

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4th international conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

作者： Kumar Sadineni, Praveen Mahantech Corporation CharlestonWV United States

ISBN: (纸本)9781728154640

In this digital era, humans are intended to access huge amount of data to fulfill their needs. The so called big data has been generated across the globe from various sources such as e-commerce, social networking sites, banking applications etc. and makes it difficult to capture, store, access and manage the huge amount of data resources. To overcome this disadvantage, a new technique is proposed to access the useful insights from a pool of data to deliver better *** query is one such efficient and popular decision support approaches, which remains as an enhanced version of database queries that help to extract the desired data from large databases and recognizes the objects that present along with the optimum combination of the features of the dataset in an efficient manner. This paper deals with various skyline query processing techniques such as basic distributed skyline (BDS), improved distributed skyline (IDS), progressive distributed skyline (PDS), mobile ad-hoc network (MANET), SKYPEERISKYPEER+, parallel distributed skyline (PaDSkyline), feedback based distributed Skyline (FDS), distributed skyline (DSL), skyline space partitioning (SSP), SKYFRAME, iSky which can be used on Big Data to extract the required information and summarizes its comparison. © 2020 IEEE.

关键词： Large dataset

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Prompt: Dynamic Data-Partitioning for distributed Micro-batch Stream processing Systems 20

Prompt: Dynamic Data-Partitioning for Distributed Micro-batc...

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ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Abdelhamid, Ahmed S. Mahmood, Ahmed R. Daghistani, Anas Aref, Walid G. Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781450367356

Advances in real-world applications require high-throughput processing over large data streams. Micro-batching has been proposed to support the needs of these applications. In micro-batching, the processing and batching of the data are interleaved, where the incoming data tuples are first buffered as data blocks, and then are processed collectively using parallel function constructs (e.g., Map-Reduce). The size of a micro-batch is set to guarantee a certain response-time latency that is to conform to the application's service-level agreement. In contrast to tuple-at-a-time data stream processing, micro-batching has the potential to sustain higher data rates. However, existing micro-batch stream processing systems use basic data-partitioning techniques that do not account for data skew and variable data rates. Load-awareness is necessary to maintain performance and to enhance resource utilization. A new data partitioning scheme, termed Prompt is presented that leverages the characteristics of the micro-batch processing model. In the batching phase, a frequency-aware buffering mechanism is introduced that progressively maintains run-time statistics, and provides online key-based sorting as data tuples arrive. Because achieving optimal data partitioning is NP-Hard in this context, a workload-aware greedy algorithm is introduced that partitions the buffered data tuples efficiently for the Map stage. In the processing phase, a load-aware distribution mechanism is presented that balances the size of the input to the Reduce stage without incurring inter-task communication overhead. Moreover, Prompt elastically adapts resource consumption according to workload changes. Experimental results using real and synthetic data sets demonstrate that Prompt is robust against fluctuations in data distribution and arrival rates. Furthermore, Prompt achieves up to 200% improvement in system throughput over state-of-the-art techniques without degradation in latency.

关键词： distributed Data processing Micro-batch Stream processing Data Partitioning Elastic Stream processing

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