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Deep Packet Inspection at Scale: Search Optimization Through Locality-Sensitive Hashing 21

Deep Packet Inspection at Scale: Search Optimization Through...

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21st IEEE international symposium on Network computing and Applications, NCA 2022

作者： Kapoor, Maya Krishnan, Siddharth Moyer, Thomas Parsons Corporation Defense & Intelligence Sector CharlotteNC United States UNC Charlotte College of Computing & Informatics CharlotteNC United States

ISBN: (纸本)9798350397307

Deep packet inspection is a primary tool for security specialists, surveillance analysts, and network engineers to lawfully intercept and analyze network traffic. In order to process this data or select streams of interest from the large amount of data flowing in today's internet, solutions must be capable of identifying network traffic as quickly and accurately as possible. The ever-increasing diversity of data as well as sheer size has rendered the current regular expression matching and filtering solutions ineffective. We propose locality-sensitive hash embedding techniques Alpine and Palm for packet analysis. The fixed size of hashes as well as the adaptability of distance measures is proven to address the network traffic classification problem in our experiments and improves scalability over current state-of-the-art, automata-based search engines. In this paper, we analyze the system's ability to classify network traffic by many data layer protocols and traffic types with over 99% accuracy. The model is also proven effective in areas where the regular expressions are inapplicable, such as traffic profiling. Finally, we provide real benchmarks of the system's ability to scale to large signature and hash sets with much improved performance, demonstrating real-world applicability and generalizability of locality-sensitive hashing to deep packet inspection technology. © 2022 IEEE.

关键词： Benchmarking

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Multi-objective Deep Reinforcement Learning for Mobile Edge computing 21

Multi-objective Deep Reinforcement Learning for Mobile Edge ...

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21st international symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Yang, Ning Wen, Junrui Zhang, Meng Tang, Ming Institute of Automation Chinese Academy of Sciences Beijing100190 China The ZJU-UIUC Institute Zhejiang University Zhejiang314499 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783903176553

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences of these applications (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters. To address the challenge of unknown preferences, we design a multi-objective (deep) reinforcement learning (MORL)-based resource scheduling scheme with proximal policy optimization (PPO). In addition, we introduce a well-designed state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption. Simulation results demonstrate that our proposed MORL scheme enhances the hypervolume of the Pareto front by up to 233.1% compared to benchmarks. © 2023 IFIP.

关键词： Reinforcement learning

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Pervasive AI for Secure and Scalable IoT- Edge-Cloud Continuum: A Big Picture 25

Pervasive AI for Secure and Scalable IoT- Edge-Cloud Continu...

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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： Puthal, Deepak Mohanty, Saraju P. Yeun, Chan Yeob Damiani, Ernesto Pradhan, Biswajeet University of North Texas Department of Computer Science and Engineering DentonTX United States Khalifa University Center for C2PS Department of Eecs Abu Dhabi United Arab Emirates Università Degli Studi di Milano Department of Computer Science Milan Italy University of Technology Faculty of Engineering and It Sydney Australia

ISBN: (纸本)9798350330014

The proliferation of Internet of Things (IoT) devices has led to an explosion of data generated at the edge of the network. This has led to the development of the IoT -Edge-Cloud continuum, where data is processed and analyzed across multiple layers of the network. However, this also poses significant security and privacy challenges, as sensitive data is transmitted across the network. In this paper, we propose the use of Pervasive Artificial Intelligence (AI) to enhance the security and efficiency of the IoT -Edge-Cloud continuum. Pervasive AI can be used at each layer of the network to process and analyze data in real-time, reducing the amount of data that needs to be transmitted across the network. This can help to improve response times, reduce network traffic, and enhance security by keeping data localized. We also discuss the potential cybersecurity threats and future research directions for the use of Pervasive AI in the IoT - Edge-Cloud continuum. Our findings suggest that Pervasive AI has significant potential to enhance the security and efficiency of the IoT -Edge-Cloud continuum, and should be considered as a viable solution for future IoT deployments. © 2023 IEEE.

关键词： Internet of things

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A Novel Cache and Consistency Mechanism for IoT Time Series Data 25

A Novel Cache and Consistency Mechanism for IoT Time Series ...

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作者： Cai, Tao Huang, Zeyu Niu, Dejiao Ma, Yueming School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798350330014

The non-volatile memory storage devices offer high read-write speeds, non-volatility, and large capacity. It can provide essential support for efficiently storing and manage the vast amount of time series data concurrently collected by IoT devices. However, the cache and consistency mechanism of the existing IoT system are not well adapted to the characteristics of NVM devices. This paper designs a novel cache and consistency mechanism. Firstly, the address prediction exception cache is proposed to reduce the impact of prediction failures in query and improve the query throughput of IoT time series data. Secondly, the log with integrated outliers and the compressing strategy are designed to improve the efficiency of outliers queries. Then the consistency strategy of IoT time series data storage is designed to avoid partial writes of the IoT time series data block to NVM. Finally, the prototype system called NBETSMS is implemented based on the open source NVM driver from Intel named PMEM to avoid the impact of IoT stack in Linux. A dedicated test tool for IoT time series storage system named YCSB- TS is used to evaluate. Compared to InfluxDB, OpenTSDB and TimescaleDB, NBETSMS improves random query throughput by 180.1%, scan throughput by 128.2 %, and reduces log storage overhead by 15%. © 2023 IEEE.

关键词： Time series

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Virtual Club Shadow: A Real-time Projection of Golf Club Trajectory 21

Virtual Club Shadow: A Real-time Projection of Golf Club Tra...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Liao, Chen-Chieh Kikuchi, Haruki Hwang, Dong-Hyun Koike, Hideki Tokyo Inst Technol Sch Comp Tokyo Japan NAVER Corp CLOVA Voice & Avatar Seongnam Si South Korea

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

In sports like golf, the motion of a club or racket determines the outcome significantly, and it is essential to consistently move such properties with the correct forms to achieve high scores during competitions. This paper proposes a novel projection method that provides real-time visualization of golf club trajectories on the ground. The system can render the trajectory of a golf club along with conventional face-to-path angle information in real-time using an optical motion capture system and a video projector. Furthermore, the system utilizes dynamic changes of color to provide more detailed information about the pose of the golf club. This color-changing method aims to help users understand their club motion trajectories and explicitly adjust their form based on the visualization results.

关键词： Human-centered computing-Human computer interaction (HCI)-Interactive systems and tools Human-centered computing-Visualization-Visualization design and evaluation methods

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A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

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作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

Software developers create mashups of their interest by searching, selecting, and combining web Application Programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

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Real-time Gaze Tracking with Head-eye Coordination for Head-mounted Displays 21

Real-time Gaze Tracking with Head-eye Coordination for Head-...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Chen, Lingling Li, Yingxi Bai, Xiaowei Wang, Xiaodong Hu, Yongqiang Song, Mingwu Xie, Liang Yan, Ye Yin, Erwei Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin Peoples R China Acad Mil Sci Def Innovat Inst Beijing Peoples R China Tianjin Artificial Intelligence Innovat Ctr Tianjin Peoples R China

ISBN: (数字)9781665453257

ISBN: (纸本)9781665453257

high-accuracy, low-latency gaze tracking is becoming one of the indispensable features in augmented reality (AR) head-mounted devices (HMDs). Researchers have proposed different approaches to predict gaze positions from eye images. However, since only the eye modality is focused, these appearance-based algorithms are still struggle to trade off the accuracy and running speed in HMDs. In this paper, we propose a lightweight multi-modal network (HE-Tracker) to regress gaze positions. By fusing head-movement features with eye features, HE-Tracker achieves comparable accuracy (3.655. in all subjects) and 27x speedup (48 fps in the specialized AR HMD) compared to the state-of-the-art gaze tracking algorithm. We further demonstrate that when applying our head-eye coordination strategy to other baseline models, all these models achieve at least 6.36% performance improvement without a pronounced effect on running speed. Moreover, we construct HE-Gaze, the first multi-modal dataset with eye images and head-movement data for near-eye gaze tracking. This dataset is currently made of 757,360 frames and 15 persons, providing an opportunity to foster research in multi-modal gaze tracking approaches. Our dataset is available at DOWNLOAD LINK1.

关键词： Human-centered computing Human computer interaction (HCI) Interaction paradigms Augmented reality computing methodologies Artificial intelligence computer vision computer vision problems

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ZSS Signature Based Data Integrity Verification for Mobile Edge computing 21

ZSS Signature Based Data Integrity Verification for Mobile E...

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21st IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Wang, Haiyan Zhang, Jiawei Lin, Yi Huang, Haiping Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Jiangsu Co Ltd China Mobile Grp Nanjing Peoples R China

ISBN: (纸本)9781728195865

Mobile edge computing (MEC), which merits in reducing response time by executing services in close proximity to end devices, has recently emerged as one of promising solutions for mobile services and hence attracted increasing research. Edge nodes usually pre-download parts of private data which are stored in the cloud to enable end devices' fast access, which mitigates overburden of the centralized cloud. However, malicious attackers or unreliable services providers may corrupt with those private data on edge servers. To this end, how to validate data integrity in MEC environment safely and efficiently has become a crucial problem. In this paper, we propose a ZSS Signature based Data Integrity Verification solution for MEC (ZSDIV-MEC), which supports privacy protection and public auditing by introducing a third party auditor (TPA) and the employment of ZSS signature scheme. In the proposed ZSDIV-MEC, system architecture is described followed with a detailed verification protocol, which takes full consideration of verification for data on three cases including single edge, multiple edges, and a joint of multiple edges and the center cloud. performance analysis of the protocol including feasibility, security, privacy, and dynamicity is discussed respectively. Experimental results in comparison with baseline schemes demonstrate that our solution has better performance in data verification and computation overhead.

关键词： mobile edge computing data integrity verification short signature

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FAFMOTS: A Fast and Anchor Free Method for Online Joint Multi-Object Tracking and Segmentation 21

FAFMOTS: A Fast and Anchor Free Method for Online Joint Mult...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Li, Shuman Feng, Weijiang Yang, Longqi Yang, Wenjing Yang, Shaowu Lan, Long Natl Univ Def Technol Inst Quantum Informat Coll Comp Sci & Technol Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Coll Comp Sci & Technol Changsha Peoples R China PLA Informat Engn Univ Henan Key Lab Quantum Informat & Cryptog Zhengzhou Peoples R China Def Innovat Inst Changsha Peoples R China

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

Fast object tracking system is important to Augmented Reality (AR). However, tracking multiple objects fast and accurately is not a trivial task in computer vision, especially when the objects become crowded and frequently occluded. In this paper, we design an efficient segmentation model along with the tracking and carefully study the contributions of pixel-level appearance modeling in the presence of occlusions. Different from previous joint tracking and segmentation methods that depend on bounding boxes for instance segmentation, we proposed an anchor free model to speed the object mask estimation. Specifically, our proposed approach jointly addresses instance segmentation and tracking with a single convolutional network for the first time, and generates object masks to explicitly separate overlapping objects in tracking. As pointed out by several previous works, the goals of tracking and segmentation are not always consistent, we deliberately design three homogeneous branches in our network to balance the competitive three functional parts of objectness, masks, and re-identification simultaneously and constrain them in a comparable scale. With the effective three functional parts, our method could run fast for the time-sensitive tracking task and meanwhile relieve the effects of appearance noise brought by the occlusions. Experimental results on the MOTS benchmark demonstrate the effectiveness of the proposed method.

关键词： computing methodologies-Artificial intelligence-computer vision-Tracking computing methodologies-Artificial intelligence-computer vision-Video segmentation

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Overcomplete Deep Low-Rank Subspace Clustering 21

Overcomplete Deep Low-Rank Subspace Clustering

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21st international symposium on Distributed computing and Applications for Business Engineering and Science, DCABES 2022

作者： Feng, Yongmeng You, Congzhe School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China

ISBN: (纸本)9781665454629

Aiming at the fact that when the input data in the deep subspace clustering networks (DSC) has noise, its robustness is poor, the performance is significantly degraded, and the method has too many learnable parameters, we suggest an overcomplete deep low-rank subspace clustering (ODLRSC). The technique is easy to use, efficient, and has shown to be a great fit for subspace clustering. By inserting a fully connected linear layer and its transposition between the encoder and decoder in our suggested technique, we may automatically put rank restrictions on the learnt representations. Additionally, in order to obtain a more reliable representation of the input data for clustering, the characteristics of the under- and over-complete auto-encoder networks are fused in the encoder. Our technique beats DSC and other clustering algorithms in the field of clustering error, and can sustain high level of quality throughout a broad range of LRRs, according to experimental findings on benchmark datasets. © 2022 IEEE.

关键词： Benchmarking

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