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Container Anomaly Detection Using Neural networks Analyzing System Calls 28

Container Anomaly Detection Using Neural Networks Analyzing ...

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28th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Gantikow, Holger Zoehner, Tom Reich, Christoph Furtwangen Univ Appl Sci Inst Data Sci Cloud Comp & IT Secur Furtwangen Germany

ISBN: (纸本)9781728165820

Container environments permeate all areas of computing, such as HPC, since they are lightweight, efficient, and ease the deployment of software. However, due to the shared host kernel, their isolation is considered to be weak, so additional protection mechanisms are needed. This paper shows that neural networks can be used to do anomaly detection by observing the behavior of containers through system call data. In more detail the detection of anomalies in file and directory paths used by system calls is evaluated to show their advantages and drawbacks.

关键词： Container Security Anomaly Detection Neural networks

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CNN-Based Detection of DDoS Attacks in Multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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Communication, Computer Sciences and Engineering (IC3SE), international conference on

作者： Rashmi Verma Manisha Jailia Munish Kumar Bhawna Kaliraman Department of Computer Science and Engineering Banasthali Vidyapith Tonk Rajasthan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural networks (CNNs) to detect distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection.

关键词： Deep learning Cloud computing Accuracy System performance Data preprocessing Computer architecture Denial-of-service attack

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Encoding Following Information with Graph Attention Network for Opinion Prediction

Encoding Following Information with Graph Attention Network ...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Jiajian Jiang Xiaoliang Chen Yuntian Zhang Zexia Huang Xianyong Li Yajun Du School of Computer and Software Engineering Xihua University Chengdu P. R. China

Opinion prediction is an important and challenging task. Previous work often only predicts opinions based on users’ content, ignoring the influence of neighbors’ opinions on users in social networks. In this paper, we utlize Graph Attention networks to capture neighbor information for opinion prediction. Firstly, we divide users in the online social media platform into general users and online influencers by the number of their fans. Then we built the implicit link between general users by their following list, and construct the implicit network for opinion prediction. Experimental results show that the proposed model is more accurate in opinion prediction compared to many competitive baselines.

关键词： Fans Social networking (online) Computational modeling Neural networks Predictive models Ubiquitous computing Encoding

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Attention Mechanism, Linked networks, and Pyramid Pooling Enabled 3D Biomedical Image Segmentation

Attention Mechanism, Linked Networks, and Pyramid Pooling En...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Pooja Ravi Srijarko Roy Indira Dutta Kottilingam Kottursamy CSE SRM IST Computational Intelligence SRM IST

We present an approach to detect and segment tumorous regions of the brain by establishing three varied segmentation architectures for multiclass semantic segmentation along with data-specific customizations like residual blocks, soft attention mechanism, pyramid pooling, linked architecture, and 3D compatibility to work with 3D brain MRI images. The proposed segmentation architectures namely, Attention Residual U-Net 3D (ARU-Net 3D), LinkNet 3D and PSPNet 3D, segment the MRI images and successfully isolate three classes of tumors. By assigning pixel probabilities, each of these models differentiates between pixels belonging to tumorous and non-tumorous regions of the brain. By experimenting and observing the performance of each of the three architectures using metrics like Dice Loss and Dice Score, on the BraTS2020 dataset, we successfully establish the following validation scores: 0.6488, 0.6485, and 0.6501 for the ARU-Net, LinkNet, and PSPNet 3D architectures respectively. Code has been made available at: https://***/indiradutta/BrainTumorSeg-III-D.

关键词： Measurement Image segmentation Three-dimensional displays Codes Magnetic resonance imaging Semantic segmentation Computer architecture

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Real-Time Traffic Sign Detection for Self-Driving and Energy-Saving Driving Based on YOLOv4 Neural Network

Real-Time Traffic Sign Detection for Self-Driving and Energy...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Chi-Chun Chen Yuan-Hong Guan Nabila Rizqia Novianda Chung-Chen Teng Meng-Hua Yen Dept. of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan Master of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan

With the booming development of autonomous vehicles (AV) in recent years, a vehicle needs to have the ability to detect changes in the environment in real-time. If the vehicle can be decelerated in advance according to the traffic signs, it can effectively reduce fuel consumption and improve overall comfort. This paper uses the Kaggle data set for training based on marking the common traffic signs in foreign countries, adds the local data set in Taiwan to the testing data set, and uses the You Only Look Once v4 (YOLOv4) neural network to detect the traffic signs in real time. The experimental results show that YOLOv4 still has a good generalization ability in the case of slight differences in different national sign types, and the mean Average Precision (mAP) can reach more than 87.6%.

关键词： Training Neural networks Real-time systems Fuels Artificial intelligence Autonomous vehicles Testing

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Hand Gesture to Character Recognition using Convolutional Neural Network

Hand Gesture to Character Recognition using Convolutional Ne...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Divyansh Mahida Divyansh Jain Hansal Shah Jainil Patel Rajeev Kumar Gupta Ashutosh Sharma Computer Science Engineering Pandit Deendayal Energy University Gandhinagar Gujarat India Computer Science Engineering University of Petroleum & Energy Studies Dehradun India

ISBN: (纸本)9781665454025

Communication serves as the medium to interact with each other, be it verbal or nonverbal. Sign Language is a structured set of visual gestures and movements with specific meanings. It is a communication tool for people having hearing impairments. According to current statistics, over 5 per cent of the world's population suffers from hearing loss, that is 432 million adults and 34 million children. While the UN predicts that by 2050 every 4th person will suffer some hearing loss, the need to at least reduce this communication gap is a near-future reality. Sign langauges are often hard to learn and understand. The paper presents an optimal approach to providing a medium for effective communication for people having vocal and hearing disabilities using hand gestures. The proposed model interprets American Sign Language (ASL) from hand gestures. The paper focuses on using Convolutional Neural networks to predict ASL efficiently. This includes the translation of the 26 static sign language alphabets. Phase one involves preprocessing the visual input gesture, followed by its computation. Phase two consists of recognizing the letters with an accuracy of 99.52% and a Cohen Kappa score of 0.995. The paper further presents the statistical result evaluation of existing and proposed techniques.

关键词： Visualization Sociology Data preprocessing Auditory system Gesture recognition Assistive technologies Grid computing

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Localization and Semantic Segmentation of Polyp in an Effort of Early Diagnosis of Colorectal Cancer from Wireless Capsule Endoscopy Images

Localization and Semantic Segmentation of Polyp in an Effort...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Jothiraj S Jayanthy Anavai Kandaswami Department of Biomedical Engineering SRM Institute Science and Technology Chengalpattu Kattankulathur India

ISBN: (纸本)9781665454025

Cancer is characterized by the fast growth of aberrant cells that affect adjacent tissues. Colorectal cancer could be diagnosed in the early stage by identifying the predecessor polyp that are initially innocuous. Endoscopy aids in the identification of polyp in real time monitoring. Polyps are obscured by the mucosa that surrounds it in the lumen of the colon, making visual differentiation from the mucosa difficult for the physicians thereby increasing the miss rates. Recognizing the colorectal polyps is challenging as it varies widely in characteristics representing its features. With the emergence of deep learning techniques especially the convolutional neural network an effort was made to detect and segment the colorectal polyps. U-net architecture with the capability to learn deep features from the images is proposed in our paper for the semantic segmentation where polyps in the colon images are localized. Polyp images from Kvasir dataset obtained using wireless capsule endoscopy that provides the advantage of viewing the entire gastrointestinal tract was used in this paper. The deep learning framework was evaluated for assessing its performance and was optimized. The predicted results produced an accuracy, precision, sensitivity (recall), IoU and f1 score of 93.14%, 98.08%, 95.55%, 95.75% and 98.01% respectively.

关键词： Wireless communication Deep learning Wireless sensor networks Visualization Sensitivity Endoscopes Semantic segmentation

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Dynamic Path Based DNN Synergistic Inference Acceleration in Edge computing Environment 27

Dynamic Path Based DNN Synergistic Inference Acceleration in...

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27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Zhou, Meng Zhou, Bowen Wang, Huitian Dong, Fang Zhao, Wei School of Computer Science and Engineering Southeast University Nanjing210000 China Institute for Artficial Intelligence Hefei Comprehensive National Science Center Hefei230000 China School of Computer Science and technology Anhui University of Technology Ma'anshan243000 China

ISBN: (纸本)9781665408783

Deep Neural networks (DNNs) have achieved excellent performance in intelligent applications. Nevertheless, it is elusive for devices with limited resources to support computationally intensive DNNs, while employing the cloud may lead to prohibitive latency. Better solutions are exploiting edge computing and reducing unnecessary computation. Multi-exit DNN based on the early exit mechanism has an impressive effect in the latter, and in edge computing paradigm, model partition on multi-exit chain DNNs is proved to accelerate inference effectively. However, despite reducing computations to some extent, multiple exits may lead to instability of performance due to variable sample quality, performance inferior to the original model especially in the worst case. Furthermore, nowadays DNNs are universally characterized by a directed acyclic graph (DAG), complicating the partition of multi-exit DNN exceedingly. To solve the issues, in this paper, considering online exit prediction and model execution optimization for multi-exit DNN, we propose a Dynamic Path based DNN Synergistic inference acceleration framework (DPDS), where exit designators are designed to avoid iterative entry for exits;to further promote computational synergy in the edge, the multi-exit DNN is dynamically partitioned according to network environment to achieve fine-grained computing offloading. Experimental results show that DPDS can significantly accelerate DNN inference by 1.87× to 6.78×. © 2021 IEEE.

关键词： Deep neural networks

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7th Latin American High Performance computing conference, CARLA 2020

7th Latin American High Performance Computing Conference, CA...

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7th Latin American High Performance computing conference, CARLA 2020

ISBN: (纸本)9783030680343

The proceedings contain 15 papers. The special focus in this conference is on Latin American High Performance computing. The topics include: Electricity Demand Forecasting Using Computational Intelligence and High Performance computing;parallel/distributed Generative Adversarial Neural networks for Data Augmentation of COVID-19 Training Images;analysis of Regularization in Deep Learning Models on Testbed Architectures;computer Application for the Detection of Skin Diseases in Photographic Images Using Convolutional Neural networks;Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good;Fostering Remote Visualization: Experiences in Two Different HPC Sites;high Performance computing Simulations of Granular Media in Silos;performance Analysis of Main Public Cloud Big Data Services Processing Brazilian Government Data;accelerating Machine Learning Algorithms with TensorFlow Using Thread Mapping Policies;Methodology for Design and Implementation an Efficient HPC Cluster;estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulations;Using HPC as a Competitive Advantage in an international Robotics Challenge;a Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption.

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2nd international conference on computing, Communication, and Learning, CoCoLe 2023

2nd International Conference on Computing, Communication, an...

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2nd international conference on computing, Communication, and Learning, CoCoLe 2023

ISBN: (纸本)9783031569975

The proceedings contain 25 papers. The special focus in this conference is on computing, Communication, and Learning. The topics include: Yolo and RetinaNet Ensemble Transfer Learning Detector: Application in Pavement Distress;stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques;nearest Neighbor and Decision Tree Based Cloud Service QoS Classification;object Recognition Through Content Based Feature Extraction and Classification of Sounds in IoT Environment;detection of Malicious Activity on Credit Cards Using Machine Learning;agent-Based Modeling for Optimum Location Selection: Enhancing Day-Ahead Market Analysis for Microgrid Integration;a Comparative Approach for Skin Cancer Detection Using Artificial Bee Colony;a Hybrid Approach to Enhance Software Quality by Sentiment Analysis of Developer;unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic;sentiment Analysis of Crypto Currency Trading Applications in India Using Machine Learning;Graph Similarity Join (GSJ) Approach to Detect Near Duplicate Text Documents;three Factor Authentication Scheme for Telecare Medical Information System;an Effective Threshold Based Technique for Retinal Image Blood Vessel Segmentation on Fundus Image Using Average and Gaussian Filters;substantiation System Using Facial Recognition and Principal Component Analysis;impact of Stress on Faculties’ Attitudes: A Study of Higher Education Institutes;dynamic Cross-Layer Communication Design for Multi-objective Optimization in Wireless Sensor networks;Implementation of Weighted parallel Interference Cancellation Technique for Multi User Detection in UWA Communication;analysis and Comparison of Firefly Algorithm for Measuring Convergence Rate in distributed Environment;A Comparative Study on Social Behavior of Tribes and Non Tribes in ITDA Paderu by Using Network Science;raster Big Data Processing Using Spark with GeoTrellis.

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