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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Weerasinghe, Shakthi Zaslavsky, Arkady Loke, Seng W. Moghaddam, Valeh Becker, Christian Deakin University School of Information Technology Burwood Melbourne Australia Universität Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany

ISBN: (纸本)9798350341010

Context-awareness is becoming more relevant for smarter modern-day applications. With billions of IoT devices able to monitor a plethora of parameters in near real-time, inferring contextual information at scale while maintaining adequate Quality of Context and delivering in time has been non-trivial for state-of-the-art centralized Context Management Systems. Further, handling complex situations and entity relations based on local awareness are areas that still need investigation. In this paper, we propose a novel edge-computing based architecture for distributed contextual intelligence systems that could address these research problems. First, we critically evaluate the current state-of-the-art in context-awareness and establish the necessity of a distributed architecture. Then, our proposed architecture is introduced along with the protocols and algorithms accompanied by real-world examples. The paper also highlights the future direction for research work in the area. © 2023 IEEE.

关键词： Internet of things

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NebulaFL: Effective Asynchronous Federated Learning for JointCloud computing

arXiv

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arXiv 2024年

作者： Gao, Fei Hu, Ming Xie, Zhiyu Shi, Peichang Xie, Xiaofei Yi, Guodong Wang, Huaimin National Key Lab. of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computing and Information Systems Singapore Management University Singapore Xiangjiang Lab Changsha China

With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud computing (JCC) is promising to break through the resource constraints caused by heterogeneous edge devices in the traditional Federated Learning (FL) paradigm. Specifically, with the protection from TEE, data owners can achieve efficient model training with high-performance AI services in the cloud. By providing additional FL services, cloud service providers can achieve collaborative learning among data owners. However, FLaaS still faces three challenges, i.e., i) low training performance caused by heterogeneous data among data owners, ii) high communication overhead among different clouds (i.e., data centers), and iii) lack of efficient resource scheduling strategies to balance training time and cost. To address these challenges, this paper presents a novel asynchronous FL approach named NebulaFL for collaborative model training among multiple clouds. To address data heterogeneity issues, NebulaFL adopts a version control-based asynchronous FL training scheme in each data center to balance training time among data owners. To reduce communication overhead, NebulaFL adopts a decentralized model rotation mechanism to achieve effective knowledge sharing among data centers. To balance training time and cost, NebulaFL integrates a reward-guided strategy for data owners selection and resource scheduling. The experimental results demonstrate that, compared to the state-of-the-art FL methods, NebulaFL can achieve up to 5.71% accuracy improvement. In addition, NebulaFL can reduce up to 50% communication overhead and 61.94% costs under a target accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

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Towards World Wide Context Management: Architecting distributed Contextual Intelligence Systems for Real-Time IoT Applications

Towards World Wide Context Management: Architecting Distribu...

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International Conference on Mobile Data Management

作者： Shakthi Weerasinghe Arkady Zaslavsky Seng W. Loke Valeh Moghaddam Christian Becker School of Information Technology Deakin University Burwood Melbourne Australia Institute of Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany

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Simulating Cyber Attacks and Designing Malware using Python 10

Simulating Cyber Attacks and Designing Malware using Python

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Ankit, Akash Inder, Shubham Sharma, Anuj Johari, Rahul Vidyarthi, Deo Prakash University School of Information Communication and Technology Guru Gobind Singh Indraprastha University SWINGER: Security Wireless IoT Network Group of Engineering and Research Dwarka New Delhi110078 India MelbourneVIC3000 Australia Jawaharlal Nehru University Parallel and Distributed System Lab School of Computer and Systems Sciences New Delhi110067 India

ISBN: (纸本)9781665490993

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time. © 2023 IEEE.

关键词： Network security

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LRCB: A Comprehensive Benchmark Evaluation of Reference-free Lossless Compression Tools for Genomics Sequencing Long Reads Data

LRCB: A Comprehensive Benchmark Evaluation of Reference-free...

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Data Compression Conference (DCC)

作者： Hui Sun Huidong Ma Yingfeng Zheng Haonan Xie Meng Yan Cheng Zhong Xiaoguang Liu Gang Wang Nankai-Baidu Joint Lab Parallel and Distributed Software Technology Lab College of Computer Science Nankai University Tianjin China School of Electrical Engineering Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Nanning China

ISBN: (数字)9798350385878

ISBN: (纸本)9798350385885

The advancement of long reads sequencing technologies has led to a significant increase in biological sequencing big data. Although several reference-free compressors are available for saving long reads data storage space, choosing the suitable one is challenging due to the shortage of thorough and systematic evaluations of their lossless compression effectiveness, both dedicated and general-purpose. In this study, we performed benchmark examinations on 30 compressors, including 11 specialized for long reads and 19 general-purpose ones, using 31 real-world datasets with differing sequencing platforms, species, and lengths. Each lossless compressor was evaluated on 13 performance measures, including compression strength, compression robustness, as well as time and peak memory required for compression and decompression. Additionally, for future long reads data compressors, we outlined investigation directions with consideration for privacy-sensitive sequences data security, hardware parallel acceleration, parameter tuning framework, and system hardware-algorithm integration design. We summarized the results as the Long Reads Compression Benchmark, available at https://***/fahaihi/LRCB.

关键词： Sequential analysis Systematics Memory management Benchmark testing Compressors Loss measurement Time measurement

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hongzhou Wu Yifan Lyu Xingyu Shen Xuechen Zhao Mengzhu Wang Xiang Zhang Zhigang Luo Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Computer National University of Defense Technology Changsha China Institute for Quantum Information and the State Key Lab of High Performance Computing National University of Defense Technology Changsha China

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

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Simulating Cyber Attacks and Designing Malware using Python

Simulating Cyber Attacks and Designing Malware using Python

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Akash Ankit Shubham Inder Anuj Sharma Rahul Johari Deo Prakash Vidyarthi SWINGER: Security Wireless IoT Network Group of Engineering and Research University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dwarka New Delhi India Royal Melbourne Institute of Technology (RMIT University) Melboume Victoria Australia Parallel and Distributed System Lab School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

关键词： Surveys Protocols Malware Internet Web sites Security Signal resolution

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EdgeDis: Enabling Fast, Economical, and Reliable Data Dissemination for Mobile Edge computing

arXiv

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arXiv 2023年

作者： Li, Bo He, Qiang Chen, Feifei Lyu, Lingjuan Bouguettaya, Athman Yang, Yun The College of Arts Business Law Education and Information Technology Victoria University MelbourneVIC3122 Australia The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia The School of Information Technology Deakin University Geelong Australia SONY AI Inc. Tokyo108-0075 Japan The School of Computer Science University of Sydney CamperdownNSW2006 Australia

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis’s validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs. © 2023, CC BY-NC-SA.

关键词： Efficiency

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MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING

arXiv

引用

arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

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