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An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

computers, Materials & Continua 2021年第5期67卷 2527-2546页

作者： Shahzad Hameed Qurratul-Ain Minhas Sheeraz Ahmed Shabana Habib Mohammad Kamrul Hasan Muhammad Islam Sheroz Khan Quaid-i-Azam University IslamabadPakistan Iqra National University PeshawarPakistan Network and Communication Technology Lab Center for Cyber SecurityUKMBangiMalaysia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Electrical Engineering Onaizah College of Engineering&Information TechnologyAl-QassimSaudi Arabia

Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the *** network topology and uncertain node mobility structure of FANETs do not aim to consider only one path *** different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical *** approaches mostly employ traditional concepts for problem *** of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired *** Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network *** such species that resembles the dynamicity of FANETs are *** this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying ***,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for *** paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s *** way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%***,the average link duration is improved by 25%compared to the BAT-FANET protocol.

关键词： Routing protocols UAVs FANETs iBATCOOP BATCOOP

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peijie Xian Ying He F. Richard Yu Jianbo Du College of Computer Science and Software Engineering Shenzhen Univ. Shenzhen China Shaanxi Key Lab. of Information Comm. Network and Security Xi’an Univ. of Posts & Telecomm. Xi’an China School of Information Technology Carleton Univ. Ottawa Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation.

关键词： Cloud computing Adaptation models Heuristic algorithms Computational modeling Quality of service Reinforcement learning Dynamic scheduling Inference algorithms Resource management Long short term memory

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Robot and its living space:A roadmap for robot development based on the view of living space

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Digital Communications and networks 2021年第4期7卷 505-517页

作者： Xiaofeng Cai Huansheng Ning Sahraoui Dhelim Rongjuan Zhou Tao Zhang Yang Xu Yueliang Wan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China School of Foreign Studies University of Science and Technology Beijing Beijing 100083China Key Lab of Information Network Security Ministry of Public SecurityChina Run Technologies Co. LidBeijing China

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human *** the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so *** robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and *** better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our *** this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot *** research in this paper contains three central ***,we interpret the concept of the robot’s living space and the functions of each ***,we analyze and summarize the relative technologies which support robots living well in each ***,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of *** the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid *** build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

关键词： Robot's living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society

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SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector

arXiv

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arXiv 2024年

作者： Li, Kaiyu Cao, Xiangyong Deng, Yupeng Song, Jiayi Liu, Junmin Meng, Deyu Wang, Zhi School of Software Engineering Xi’an Jiaotong University Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an China Guangdong Guangzhou China

Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multi-temporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual language models (VLMs) for zero-shot, open-vocabulary, etc. with prompt-based reasoning, it is promising to utilize VLMs to make better CD under limited labeled data. In this paper, we propose a VLM guidance-based semisupervised CD method, namely SemiCD-VL. The insight of SemiCD-VL is to synthesize free change labels using VLMs to provide additional supervision signals for unlabeled data. However, almost all current VLMs are designed for single-temporal images and cannot be directly applied to bi- or multi-temporal images. Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo labels for unlabeled CD data. Since the additional supervised signals provided by these VLM-driven pseudo labels may conflict with the original pseudo labels from the consistency regularization paradigm (e.g. FixMatch), we propose the dual projection head for de-entangling different signal sources. Further, we explicitly decouple the bi-temporal images semantic representation through two auxiliary segmentation decoders, which are also guided by VLM. Finally, to make the model more adequately capture change representations, we introduce contrastive consistency regularization by constructing feature-level contrastive loss in auxiliary branches. Extensive experiments show the advantage of SemiCD-VL. For instance, SemiCD-VL improves the FixMatch baseline by +5.3 IoUc on WHU-CD and by +2.4 IoUc on LEVIR-CD with 5% labels, and SemiCD-VL requires only 5% to 10% of the labels to achieve performance similar to the supervised methods. In addition, our CEG strategy, in an un-supervised manner, can achieve performance far superior to state-of-t

关键词： Visual languages

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An Efficient and Accurate Encrypted Image Retrieval Scheme via Ball Tree

An Efficient and Accurate Encrypted Image Retrieval Scheme v...

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International Conference on Big Data Computing and Communications (BIGCOM)

作者： Xianxian Li Jie Lei Zhenkui Shi Feng Yu College of computer science Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network Information Integration Southeast University Nanjing China

With the rise and development of cloud computing, more and more companies try to outsource computing and storage to cloud in order to save storage and computing cost. Due to the rich information contained in images, the explosion of images is booming the image outsourcing. However, images may contain a lot of sensitive information and cloud servers are always not trusted. Directly outsourcing may lead to data breaches and incur privacy and security concerns. This has partly led to renewed interest in privacy-preserving encrypted image retrieval. However, there are still many challenges, such as low search accuracy and inefficiency due to the hundreds of high dimensional features extracted from a single image and the large scale of images. To address these challenges, in this paper, we propose an efficient, scalable and privacy-preserving image retrieval scheme via ball tree. First, the pre-trained Convolutional Neural network (CNN) model is employed to extract image feature vectors to improve search accuracy. Next, an encrypted ball tree is constructed by using Learning With Errors(LWE)-based secure k-Nearest Neighbor (kNN) algorithm. Finally, we conduct comprehensive experiments on real-world datasets and give a brief security analysis. The results show that our scheme is practical in terms of security, accuracy, and efficiency.

关键词： Cloud computing Costs Image retrieval Feature extraction Search problems Explosions Outsourcing

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit networks

arXiv

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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A Lightweight Sparse Focus Transformer for Remote Sensing Image Change Captioning

arXiv

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arXiv 2024年

作者： Sun, Dongwei Bao, Yajie Liu, Junmin Cao, Xiangyong School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Department of Information Science School of Mathematics and Statistics Xi’an Jiaotong University No.28 Xianning West Road Shaanxi Xi’an710049 China

Remote sensing image change captioning (RSICC) aims to automatically generate sentences that describe content differences in remote sensing bitemporal images. Recently, attention-based transformers have become a prevalent idea for capturing the features of global change. However, existing transformer-based RSICC methods face challenges, e.g., high parameters and high computational complexity caused by the self-attention operation in the transformer encoder component. To alleviate these issues, this paper proposes a Sparse Focus Transformer (SFT) for the RSICC task. Specifically, the SFT network consists of three main components, i.e. a high-level features extractor based on a convolutional neural network (CNN), a sparse focus attention mechanism-based transformer encoder network designed to locate and capture changing regions in dual-temporal images, and a description decoder that embeds images and words to generate sentences for captioning differences. The proposed SFT network can reduce the parameter number and computational complexity by incorporating a sparse attention mechanism within the transformer encoder network. Experimental results on various datasets demonstrate that even with a reduction of over 90% in parameters and computational complexity for the transformer encoder, our proposed network can still obtain competitive performance compared to other state-of-the-art RSICC methods. The code is available at Lite_Chag2cap. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Natural Language Processing Approach for Instruction Set Architecture Identification

arXiv

引用

arXiv 2022年

作者： Sahabandu, Dinuka Mertoguno, Sukarno Poovendran, Radha The Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States The Institute for Information Security & Privacy Georgia Institute of Technology North Avenue AtlantaGA30332 United States

Binary analysis of software is a critical step in cyber forensics applications such as program vulnerability assessment and malware detection. This involves interpreting instructions executed by software and often necessitates converting the software’s binary file data to assembly language. The conversion process requires information about the binary file’s target instruction set architecture (ISA). However, ISA information might not be included in binary files due to compilation errors, partial downloads, or adversarial corruption of file metadata. Machine learning (ML) is a promising methodology that can be used to identify the target ISA using binary data in the object code section of binary files. In this paper we propose a binary code feature extraction model to improve the accuracy and scalability of ML-based ISA identification methods. Our feature extraction model can be used in the absence of domain knowledge about the ISAs. Specifically, we adapt models from natural language processing (NLP) to i) identify successive byte patterns commonly observed in binary codes, ii) estimate the significance of each byte pattern to a binary file, and iii) estimate the relevance of each byte pattern in distinguishing between ISAs. We introduce character-level features of encoded binaries to identify fine-grained bit patterns inherent to each ISA. We use a dataset with binaries from 12 different ISAs to evaluate our approach. Empirical evaluations show that using our byte-level features in ML-based ISA identification results in an 8% higher accuracy than the state-of-the-art features based on byte-histograms and byte pattern signatures. We observe that character-level features allow reducing the size of the feature set by up to 16x while maintaining accuracy above 97%. © 2022, CC BY.

关键词： Extraction

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New Identity Based Proxy Re-Encryption Scheme from Lattices

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China Communications 2019年第10期16卷 174-190页

作者： Liqiang Wu Xiaoyuan Yang Minqing Zhang Longfei Liu Key Lab of Network and Information Security Engineering University of Chinese Armed Police ForceXi’an 710086China Key Lab of Computer Network and Information Security Xi Dian UniversityXi’an 710071China

Identity-Based Proxy Re-Encryption (IB-PRE) allows a semi-trusted proxy to convert the ciphertext encrypted under Alice’s identity into Bob’s ciphertext of the same message without leaking plaintext. Lattice-based cryptography enjoys potential resistance to quantum analysis and low computational complexity. A multi-hop and unidirectional IB-PRE from lattices is presented. We split the functions of decryption and ciphertext transformation separately, and design the double private keys mechanism, where two keys are generated for each user, one key is used to decrypt the ciphertext by Pre-Image Sampling technique, and the other is used to generate the re-encryption key by Bonsai Trees technique. The generation of the re-encryption key is non-interactive and collusion resistant. Moreover, its IND-sID-CPA security over the decisional Learning With Errors (LWE) assumption under the standard model is proved. Compared with some previous IBPRE schemes from Bilinear Pairings, the format of transformed ciphertext in our scheme remains unchanged, furthermore, it can also resist quantum analysis. Compared with some existing IB-PRE schemes from lattices with similar properties, the space of the message in our scheme is a vector of length l and the encryption process remains a lower encryption blowup factor. At last, a proof-of-concept implementation is provided.

关键词： IB-PRE lattices bonsai trees collusion resistance

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Splicer+: Secure Hub Placement and Deadlock-Free Routing for Payment Channel network Scalability

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wang, Wei Gao, Sheng Qu, Qiang Tian, Wensheng Shen, Yulong The School of Computer Science and Technology Xidian University Xi’an710071 China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China The Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China The School of Information Central University of Finance and Economics Beijing100081 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Huawei Blockchain Lab Huawei Cloud Tech Co. Ltd. Shenzhen518055 China The Nanhu Lab Jiaxing314001 China The Cryptology and Computer Security Laboratory Shanghai Jiao Tong University Shanghai200240 China

Payment channel hub (PCH) is a promising approach for payment channel networks (PCNs) to improve efficiency by deploying robust hubs to steadily process off-chain transactions. However, existing PCHs, often preplaced without considering payment request distribution across PCNs, can lead to load imbalance. PCNs' reliance on source routing, which makes decisions based solely on individual sender requests, can degrade performance by overlooking other requests, thus further impairing scalability. In this paper, we introduce Splicer+, a highly scalable multi-PCH solution based on the trusted execution environment (TEE). We study tradeoffs in communication overhead between participants, transform the original NP-hard PCH placement problem by mixed-integer linear programming, and propose optimal/approximate solutions with load balancing for different PCN scales using supermodular techniques. Considering global PCN states and local directly connected sender requests, we design a deadlock-free routing protocol for PCHs. It dynamically adjusts the payment processing rate across multiple channels and, combined with TEE, ensures high-performance routing with confidential computation. We provide a formal security proof for the Splicer+ protocol in the UC-framework. Extensive evaluations demonstrate the effectiveness of Splicer+, with transaction success ratio (↑51.1%), throughput (↑181.5%), and latency outperforming state-of-the-art PCNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

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