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An Abnormal Behavior Detection Method Based on User Behavior Correlation Feature Sequence Modeling 9

An Abnormal Behavior Detection Method Based on User Behavior...

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9th IEEE international conference on Data Science in Cyberspace, DSC 2024

作者： Zhou, Yuting Sun, Lijuan Wu, Jingchen Gao, Yutong Wu, Xu Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service China Beijing University of Posts and Telecommunications School of Cyberspace Security BUPT Beijing China Beihang University Intellectual Property Information Service Center BHU Beijing China Minzu University of China School of Information Engineering MUC Beijing China

ISBN: (纸本)9798350391367

the healthcare industry is one of the industries most affected by insider threats. At present, the mainstream abnormal behavior detection of internal threats has the problems of relatively single reference dimension of user behavior data and lack of correlation analysis between data of different dimensions. therefore, this paper proposes an abnormal behavior detection method that integrates user behavior correlation feature sequence modeling (UBCFSM). this method fully considers the multidimension and correlation of user behavior data, and uses the time convolutional network to learn behavior sequence features and the bidirectional long and short term memory network to learn behavior habit features of the convolutional attention mechanism. In addition, the deep neural network is introduced to fuse the deviations between the predicted results of different models and the actual results, detect abnormal results, and improve the generalization ability and detection accuracy of the model. Finally, experiments were carried out on the CERT public data set, and the AUC score of 97.1% was obtained. Compared with other abnormal behavior detection models, the proposed model has achieved significant improvement, showing its effectiveness and competitiveness. © 2024 IEEE.

关键词： Convolutional neural networks

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Using Virtualization Approaches to Solve Deep Learning Problems in Voluntary distributed computing Projects 9th

Using Virtualization Approaches to Solve Deep Learning Probl...

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9th Russian Supercomputing Days international conference, RuSCDays 2023

作者： Kurochkin, Ilya Papanov, Valeriy Institute for Information Transmission Problems of Russian Academy of Sciences Moscow Russia The National University of Science and Technology MISIS Moscow Russia

ISBN: (纸本)9783031494345

the task of training deep neural networks on a large amount of data requires a lot of resources. the solution of such a problem is often impossible to carry out on one computing device in an adequate time. distributed computing systems can be used to solve deep learning problems. Such systems may consist of heterogeneous computing nodes with different computing power. To implement deep learning on a distributed heterogeneous system, it is necessary to solve the problem of utilization of all available resources. the solution to this problem is to configure the task delivery system of a distributed system. And to expand the number of computing nodes involved, it is necessary to use virtualization. the article discusses two types of virtualization for grid systems when solving deep learning problems. the features of the implementation of computational applications for training deep neural networks for solving the problem of image classification are discussed. the results of distributed deep learning on a public grid system are discussed. A comparative analysis of two virtualization approaches is given. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtualization

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ParaFuzz: Grey-Box Fuzzing Based on distributed Parallelization 9

ParaFuzz: Grey-Box Fuzzing Based on Distributed Parallelizat...

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9th international conference on Computer and Communications, ICCC 2023

作者： Fang, Enbo Ye, Genzuo Jia, Peng Co. Ltd Chengdu China Sichuan University School of Cyber Science and Engineering Chengdu China

ISBN: (纸本)9798350317251

In recent years, fuzzing has become the most popular and effective vulnerability mining technique due to high degree of automation and versatility. In order to improve the characteristics of fuzz testing such as blindness and inefficiency, a large number of studies have been conducted to optimize the design of each step. Since fuzzing is typically a computationally intensive process, and the performance improved by algorithm optimization is always limited on a single machine, parallel computing to improve the performance of fuzzing is of great research value. However, parallelization of fuzzing must face and overcome challenges such as task conflicts, scalability in distributed environments, data synchronization overhead and workload imbalance. In this paper, we set out to solve each challenge in parallelized fuzzing and propose ParaFuzz, a new parallelized fuzzing tool. ParaFuzz manage and distribute seeds centrally in a client/server architecture to avoid task conflicts, solve workload balancing problems through a request/response model. A unique global sharing mechanism is designed for different information characteristics, and a variant strategy selection mechanism is proposed to improve the efficiency of fuzzing at the task scheduling level. Results from ParaFuzz tests on the LAVA-M test set and two real-world applications show up to 66% improvement in code path discovery compared to AFL native parallel mode at 8-node parallel scale. © 2023 IEEE.

关键词： Balancing

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SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Distribution Structure 25

SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Di...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xu, Jinwei Jiang, Jingfei Xu, Shiyao Gao, Lei National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Hunan Changsha China

ISBN: (纸本)9798350330014

Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To speedup RNNs inference, previous research proposed model sparse pruning techniques. However, the pruning rate of existing sparse pruning algorithms will be affected by pruning granularity and hardware friendliness. In order to approximate nonstructured pruning algorithm, this paper proposes Large Region Balanced Sparse (LRBS) pruning method, which does not limit sub-matrix shape and effectively improves pruning rate. Furthermore, we propose Sparse Matrix Vector Multiplication Accelerator for RNNs (SMVAR), which adopt non-blocking data distribution structure to solve the problem of efficient execution of large region irreg-ular matrix multiplication. To further improve the accelerator performance, SMVAR fine-grained adjusts the pipeline between macro-operations to reduce the idle of compute components. In addition, according to the coarse-grained block characteristics of LRBS algorithm, we develop the coarse-grained parallelism of accelerator with multiply compute units(CUs) structure. Experiments show that the pruning rate of our proposed LRBS is 1.25x-2.5x higher than that of the existing pruning algorithms. Compared with the existing work, the execution efficiency is improved by more than 2.02x-35.9x in the same application scenario. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Comparison of Multinomial Naive Bayes and XG Boost for Sentiment Analysis and Bias Detection in Tweets 9

A Comparison of Multinomial Naive Bayes and XG Boost for Sen...

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9th international conference on Wireless Communications, Signal Processing and networking, WiSPNET 2024

作者： Devadath, R. Alex, Emeric C Sreeja, K. Abhishek, S. Anjali, T. Engineering Amrita School of Computing Department of Computer Science Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350350845

In the context of social platforms, this study explores Twitter sentiment analysis, providing practical insights and directions for future research. In previous days, people conveyed their feelings directly, but now platforms like Twitter have gained popularity for expressing individual and organizational opinions. Understanding these expressions can be challenging. this research focuses on sentiment analysis techniques for Twitter data, a subfield of Natural Language Processing (NLP) where machine learning plays a key role in detecting public emotions. the paper covers methods, tools, pre-processing, sentiment classification techniques, evaluation matrixes, and machine learning algorithms like Naive Bayes. the analysis, employing various keywords and hashtags, evaluates using metrics such as confusion matrix, F1 score, precision, and recall. the study emphasizes the responsible and ethical use of sentiment analysis tools for informed decision-making, promoting inclusivity and empathy online. through systematic analysis, a million tweets were categorized into positive and negative sentiments, contributing to the growing body of knowledge in sentiment analysis. © 2024 IEEE.

关键词： Sentiment analysis

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Addressing Catastrophic Forgetting in Federated Learning on Resource-Constrained Devices: A Feature Replay Approach 20th

Addressing Catastrophic Forgetting in Federated Learning on ...

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20th international conference on Intelligent computing (ICIC)

作者： Gao, Zhipeng Zhang, Junyao Yu, Xinlei Beijing Univ Posts & Telecommun State Key Lab Switching & Networking Technol Beijing Peoples R China

ISBN: (纸本)9789819756742;9789819756759

Federated learning is a new type of privacy-preserving distributed machine learning method. Despite its advantages, Federated learning suffers from catastrophic forgetting due to the temporal variability of data on participating devices. While some methods have been proposed to address this issue in federated learning, they often come at the expense of storage space, which is not a practical solution. this paper presents a federated incremental learning framework designed specifically for resource-constrained devices to address catastrophic forgetting. Leveraging a feature generator, knowledge distillation, and dynamic adaptive weight allocation, the framework addresses catastrophic forgetting and accelerates model convergence. Our framework effectively addresses the issue of catastrophic forgetting, even in the context of resource-constrained devices with limited storage. the results demonstrate significant improvements in our framework compared to existing baselines on the CIFAR-10 and CIFAR-100 datasets.

关键词： Federated learning Continual learning Generative adversarial networks

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Mobility-Aware Serverless Function Adaptations Across the Edge-Cloud Continuum 15

Mobility-Aware Serverless Function Adaptations Across the Ed...

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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Raith, Philipp Rausch, thomas Dustdar, Schahram Rossi, Fabiana Cardellini, Valeria Ranjan, Rajiv TU Wien Distributed Syst Grp Vienna Austria Univ Roma Tor Vergata DICII Rome Italy Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665460873

Serverless functions have emerged as a useful abstraction to manage the complexity of distributed and heterogeneous edge-cloud infrastructure. Current cloud-centric orchestration services and serverless platforms are not suitable for the edge-cloud continuum due to their mobility-unawareness. In this paper, we present a mobility-aware framework for edgecloud systems, where the operational mechanisms of placement, scaling, and routing of serverless functions work in tandem. To that end, we formulate the concept of pressure that captures complex system behavior in a single metric. the pressure-based framework handles geo-distributed workload and user mobility by reducing overall function latency and increasing data throughput while efficiently using edge resources. Our contributions include a novel framework revolving around pressure and a realworld proof of concept evaluation. Our approach combines the benefits of a centralized control-plane with a decentralized dataplane. the results show the efficacy of the platform to address operational goals and make effective and deterministic tradeoffs between system utilization and application performance. the novel concept of pressure shows great extensibility and builds the base for a plethora of future works.

关键词： Edge Cloud Continuum Serverless computing Function as a Service Edge Intelligence

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Social Media Text to Formal Representation: A Knowledge Graph Approach 9

Social Media Text to Formal Representation: A Knowledge Grap...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Kommineni, Yaswanth Phani Somanchi, Srilekha Pavan Kumar, Boina Sai Ladani, Yash Nair, Lekshmi S Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350397376

Social media is an online platform where users freely share their views on various topics. By sharing their views, they come to a mutual agreement or disagreement with other users' views. Users are quite active on social media and the number of users is growing exponentially with time. this leads to an abundance of data available through social media. this data helps us in curating different research problems. One such research problem can be finding similarities between users by analyzing user data. Data on social media is available in many forms like textual posts, comments, likes, shares, etc. Users often tend to express their diversity of opinions widely through comments on social media. Many opinionated and active users on social media are found in the comments section under a post. Hence, comments play a major role in determining similarity in user judgment about a topic. Likewise, people liking a post is also an essential feature in finding similarities between users. People with similar opinions tend to like posts of a specific type. this paper talks about how comments, textual posts, and likes for comments can be combined to form something called a keyword or a tag. these tags that are extracted from user data are further used in building a tag network. the tag network is used to make communities of users with similar interests. User grouping can also be done based on these extracted tags and comments under the posts. the proposed methods in this paper to achieve the above-mentioned are TF-IDF (Term frequency-inverse document frequency) followed by the TextRank algorithm. © 2023 IEEE.

关键词： Social networking (online)

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Apple and Tomato Leaves Disease Detection using Emperor Penguins Optimizer based CNN 9

Apple and Tomato Leaves Disease Detection using Emperor Peng...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Suguna, R. Vinoth Kumar, C.N.S. Deepa, S. Arunkumar, M.S. Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Tamilnadu Erode638401 India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Technology -PG Kongu Engineering College Tamilnadu Erode Perundurai638052 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350397376

the economy of the country is heavily dependent on agricultural productivity. the largest number of the crop yield trust on growth of the healthy plants. Nearly all the times the crop yields are heavily affected by leaves diseases. the vital role of the agriculture field is to discover the plant's leaves diseases in a timely premature point. An automatic disease detection technique is mandatory in agriculture field to identify and determine the kind of plant leave diseases in an earlier stage and give the right pesticides. the healthy plants are the major effect on quality and productivity of the agriculture products. An image segmentation and classification system is presented in this study to recognize the plant leaf diseases automatically. In this work, YOLO network of version 4 is used for object spotting (detection) and segmentation for detecting the affected leaves such as Apple and Tomato Leaves. Here, Convolutional Neural Network (CNN) acts as both feature extraction and also classifier, i.e. the color features and statistical attributes are bringing out using CNN. Finally, the learning rate of CNN is optimized by using Emperor Penguin Colony Optimizer (EPC). the prominent aspects of EPO is producing and computing the huddle boundary, distance, temperature, and effective mover all at the single point time. the experiments are done on publicly available datasets to analyze the performance of proposed algorithm with existing techniques. © 2023 IEEE.

关键词： Fruits

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TinyTricia - A Space-Optimized Patricia Trie For Transparent Access to Edge computing Services 15

TinyTricia - A Space-Optimized Patricia Trie For Transparent...

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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Hammer, Josef Hellwagner, Hermann Alpen Adria Univ Klagenfurt Inst Informat Technol Klagenfurt Austria

ISBN: (纸本)9781665460873

Multi-access Edge computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTricia, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (2(29)) keys <= 57 bits and up to a quarter of a billion (2(28)-1) keys <= 256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys <= 57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key);for keys >= 58 bits, add the key size (in whole bytes) to these values. thus, for 16.78 million (2(24)) 57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTricia allows adding and removing keys at runtime.

关键词： Patricia Trie Multi-Access Edge computing Space-Optimized Low Memory Footprint Software-Defined networking IPv4 IPv6

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