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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Jin, Richeng Dai, Philip Xiong, Kaiqi College of Information Science and Electronic Engineering Zhejiang University Zhejiang-Singapore Innovation and Ai Joint Research Lab Hangzhou310000 China Enloe Magnet High School RaleighNC27610 United States University of South Florida Intelligent Computer and Networking Security Lab and Cyber Florida TampaFL33620 United States

ISBN: (纸本)9781665451819

Federated learning (FL) has recently emerged as a popular distributed learning paradigm since it allows collaborative training of a global machine learning model while keeping the training data of its participating workers locally. This paradigm enables the model training to harness the computing power across the network of FL and preserves the privacy of local training data. However, communication efficiency has become one of the major concerns of FL due to frequent model updates through the network, especially for devices in wireless networks that have limited communication resources. Despite that various communication-efficient compression mechanisms (e.g., quantization and sparsification) have been incorporated into FL, most of the existing studies are only concerned with resource allocation optimization given predetermined compression mechanisms, and few of them take wireless communication into consideration in the design of the compression mechanisms. In this paper, we study the impact of sparsification and wireless channels on FL performance. Specifically, we propose a channel-aware sparsification mechanism and derive a closed-form solution for communication time allocation for workers in a TDMA setting. Extensive simulations are conducted to validate the effectiveness of the proposed mechanism. © 2023 IEEE.

关键词： Wireless networks

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Enhancing Approximate Message Passing via Diffusion Models Towards On-Device Intelligence

Enhancing Approximate Message Passing via Diffusion Models T...

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IEEE International Conference on Communications Workshops, ICC

作者： Philip Dai Kai Yue Richeng Jin Tianfu Matt Wu Kaiqi Xiong Enloe High School Raleigh NC USA Department of Electrical and Computer Engineering NC State University Raleigh NC USA College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Intelligent Computer and Networking Security Lab and Cyber Florida University of South Florida Tampa FL USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, we introduce a novel denoising-based approximate message passing framework augmented by a noise estimation module realized through a reverse diffusion process. This approach offers significant improvements in training simplicity, recovery quality, and inference efficiency, particularly suitable for deployment at mobile terminals in dynamic environments to facilitate edge computing. Extensive experiments verify the superiority of the proposed method over the state of the art, and demonstrate its potential towards sustainable edge computing and on-device intelligence at scale.

关键词： Training Message passing Conferences Computational modeling Noise Estimation Diffusion processes

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Communication-Efficient Federated Learning with Channel-Aware Sparsification over Wireless Networks

Communication-Efficient Federated Learning with Channel-Awar...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Richeng Jin Philip Dai Kaiqi Xiong Zhejiang-Singapore Innovation and AI Joint Research Lab College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Enloe Magnet High School Raleigh NC USA Intelligent Computer and Networking Security Lab and Cyber Florida University of South Florida Tampa FL

关键词： Training Time division multiple access Quantization (signal) Federated learning Wireless networks Computational modeling Training data

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Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model

Interpretation-Enabled Software Reuse Detection Based on a M...

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International Conference on Software Engineering (ICSE)

作者： Xi Xu Qinghua Zheng Zheng Yan Ming Fan Ang Jia Ting Liu Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi’an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland School of Cyber Science and Engineering Xi’an Jiaotong University China

ISBN: (纸本)9781665402965;9781665448314

Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for "intent search based on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.

关键词： Law Semantics Libraries security Software reusability Software engineering Resilience

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Interpretation-enabled software reuse detection based on a multi-level birthmark model

arXiv

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arXiv 2021年

作者： Xu, Xi Zheng, Qinghua Yan, Zheng Fan, Ming Jia, Ang Liu, Ting Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Engineering Xi'an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland

关键词： computer software reusability

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Facilitation of cybersecurity learning through real-world hands-on labs 126

Facilitation of cybersecurity learning through real-world ha...

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Rahouti, Mohamed Xiong, Kaiqi Department of Electrical Engineering University of South Florida Tampa33620 United States Cyber Florida University of South Florida Tampa33620 United States Department of Mathematics and Statistics University of South Florida Tampa33620 United States Intelligent Computer Networking and Security Lab University of South Florida Tampa33620 United States

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Understanding global environment for network innovations (GENI) and software-defined networking (SDN) for computer networking and security education 126

Understanding global environment for network innovations (GE...

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

Hands-on modules and experiments are considered essential and fundamental components in cyber security curriculums. However, because of the significant increase in student enrollments in both online and face-to-face courses, universities face various challenges mainly related to financial support and computing resources that could be limited. To overcome such key challenges, universities have been considering alternative solutions to solve resource issues and allow students to practice real-world lab experiments through a virtual environment such as the Global Environment for Network Innovations (GENI). GENI is a real-world, repeatable, programmable, at-scale, virtual infrastructure for experiments in a variety of computer science areas such as networking, security, and distributed computing sponsored by National Science Foundation (NSF). Furthermore, Software-Defined networking (SDN) has been a core technology in cloud computing and other cyber-physical systems where SDN facilitates network management and enables network programmability and efficient network configuration to improve network performance, monitoring, and security. In this paper, we will demonstrate our great efforts in the development of GENI and SDN learning and experimental modules for computer networking and security courses in order to achieve the goal of our funded NSF project. Specifically, we will first present our methodology for the design of our modules and then give the detail of GENI and SDN modules including GENI account setup and resource reservation, measurement tool labs, as well as SDN labs for network traffic management and the detection and mitigation of several well-known security attacks, such as Denial of Service Attacks (DoS), Distributed Denial of Service Attacks (DDoS), phishing attacks, and Domain Generation Algorithm (DGA) malware detection. Those learning and experimental modules have been developed at different levels to meet the need of different types of students. Final

关键词： Software defined networking

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A customized educational booster for online students in cybersecurity education 11

A customized educational booster for online students in cybe...

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11th International Conference on computer Supported Education, CSEDU 2019

作者： Rahouti, Mohamed Xiong, Kaiqi Department of Electrical Engineering University of South Florida TampaFL33620 United States Cyber Florida University of South Florida TampaFL33620 United States Department of Mathematics and Statistics University of South Florida TampaFL33620 United States Intelligent Computer Networking and Security Lab University of South Florida TampaFL33620 United States

ISBN: (纸本)9789897583674

Real-world lab experiments have been an integral part of computer science and engineering curriculums. However, human and computing resources as well as financial support to courses may be limited at many universities ranging from small to large universities and from liberal arts colleges to top research universities as there is a dramatic increase of student enrollments in computer science and engineering for the past ten years. Nowadays, like many other universities nation wide, University of South florida (USF) in collaboration with the cyber florida center offers online cyber security degrees as well as certificates. Throughout such programs, online students are expected to acquire knowledge via an interdisciplinary set of core courses prior to taking a deep dive into one of four following concentrations: cyber intelligence, digital forensics, information assurance and computer security fundamentals. However, when it comes to training student with real-world cyber security labs, both instructors and students face various challenges with regard to resources and virtual environment for exploring and running a broad range of security experiments and tests. In order to achieve the goal of our funded NSF project, in this paper we will discuss our teaching contributions to the development of a broad range of cyber security labs, facilitation of applied cryptography learning through experimental modules, and our customized virtual machine that fits the needs of various online computer networking and security courses. Specifically, we will first present our methodology for the design of our experimental modules and then present in details our pre-built Linux-based portable virtual machine. Those learning and experimental modules have been developed at different levels to meet the need of students with different academic and industrial backgrounds. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Virtual reality

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