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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Optimal Selection of Software Reliability Growth Models: A CRITIC-CODAS Technique 10

Optimal Selection of Software Reliability Growth Models: A C...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Singh, Veenu Kumar, Vijay Singh, V.B Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Amity University Department of Applied Mathematics Aias Uttar Pradesh Noida India School of Computer and System Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

A plethora of software reliability growth models (SRGMs) have been proposed in the last forty years. SRGMs are the mathematical relation which are helpful in predicting the remaining number of faults, software release time, software reliability etc., during the testing phase. However, the SRGM are dataset dependent, so evaluating and selecting the best SRGM based on the user's requirements becomes a challenging task. Inaccurate selection of SRGM can lead to irrelevant results and delays in software releases. In order to address this issue, we have proposed an integrated technique based on Criteria Importance Through Inter-Criteria Correlation (CRITIC) and Combinative Distance-Based Assessment (CODAS) to rank the SRGMs. In order to demonstrate the proposed technique on a given dataset, we have selected ten SRGM and six selection criteria. The results are beneficial for software managers to select appropriate SRGM. © 2022 IEEE.

关键词： Software testing

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Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications

Performance Analysis of IDS_MDL Algorithm to Predict Intrusi...

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National Conference on Radio Science (NSRC)

作者： Walaa Adel Mahmoud Mohamad Fathi Hesham El-Badawy Rowayda Sadek Information System Department College of Computing & Information Technology Arab Academy for Science Technology and Maritime Transport Cairo Egypt National Telecommunication Institute Cairo Egypt Faculty of Computer and Artificial Intelligence Helwan University Cairo Egypt

In the last year, many modern attacks have targeted Internet of Things (IoT) devices. IoT botnet attacks are the most common. Despite the plethora of detection and prevention systems for cyberattacks, a security mechanism for IoT settings is still required since these devices have certain restrictions that make it difficult to properly implement the detection system. These memory resources are limited to processing, storing, and calculating. An effective defense against cyberattacks is an intrusion detection system (IDS). To develop the rules for IDS_MDL-IoT, we used the contemporary botnet attack dataset. Because IoT devices have limited resources, it is difficult to employ all attack patterns in the initial anomaly dataset in traditional anomaly-based IDS. Therefore, in this study, the tested ML model resulting in NB has proven to be the best classification algorithm to classify the risk of attack with 99.7% accuracy predicting from a time-speed of 3 seconds, and the tested DL model resulting in RNN has proven to be the best classification algorithm to classify the risk of attack with 99% accuracy predicting from a time-speed of 8 seconds.

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Improving Smart Contract Security with Contrastive Learning-Based Vulnerability Detection

Improving Smart Contract Security with Contrastive Learning-...

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International Conference on Software engineering (ICSE)

作者： Yizhou Chen Zeyu Sun Zhihao Gong Dan Hao Key Lab of HCST (PKU) MOE School of Computer Science Peking University Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a con-trastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training pro-cess of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all base-line methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.

关键词： Deep learning Training Correlation Smart contracts Semantics Fitting Blockchains

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Multi-party secure communication using blockchain over 5G

Multi-party secure communication using blockchain over 5G

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Archana, K. Kareem, Z.H. Al-Farhani, Liwa H. Bagyalakshmi, K. Jenvi, Ignatia K. Majella Kumar, Ashok Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Maharashtra Pune India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq System Analysis Control and Information Processing dep Academy of Engineering RUDN University Moscow Russia Sri Ranganathar Institute of Engineering and Technology Coimbatore India Mathematics Department Saveetha School of Engineering SIMATS Saveetha Nagar Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9781032669663

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 The Author(s).

关键词： Blockchain

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Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

arXiv

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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Deploying Healthcare Monitoring system For Elderly Patient Care using IoT and Neural Network Techniques

Deploying Healthcare Monitoring System For Elderly Patient C...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Jagendra Singh Neha Garg Ravikumar Sethuraman Soumi Ghosh A Hemantha Kumar Raju Kolluri School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Mechanical Engineer Annamacharya Institute of Technology and Sciences India Department Of Electronics and Communication Engineering Narasaraopet Engineering College Narasaraopet India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Utilizing Internet of Things (IoT) technology in the healthcare sector can revolutionize the care provided to individuals. This study illustrates the application of IoT in ensuring continuous healthcare for elderly patients, particularly focusing on scenarios like epidemiology in rehabilitation centers, intensive care units (ICU), and home health care. The research integrates a variety of sensors, machine learning algorithms, and cloud computing to establish a healthcare system driven by IoT. With a participant pool of 50 individuals, including both healthy adults and those with various ailments, the study employs Short-Term Memory (LSTM), decision trees, and Artificial Neural Network (ANN) models to analyze sensor data, specifically temperature and heart rate measurements. The primary objective is to identify uncertainties and facilitate timely interventions. Results indicate that ANN surpasses LSTM and tree models in parameter estimation, displaying superior accuracy and F1 scores in anomaly detection while minimizing false negatives. This use of predictive ability has the chance of reforming treatment through early detection and administration of treatments. This research, in short, exemplifies the importance of integrating prognostic analytics in health systems that support IoT; in other words, it enables continuous monitoring, forecasting, and access to care requirements concerning changing forms of illness. From a scientific perspective, this work is seen as being proactive because it adopts green medical materials and uses IoT-based methods so that control performance may be improved for the patients.

关键词： Temperature sensors Cloud computing Machine learning algorithms Medical services Internet of Things Decision trees Older adults Monitoring Medical diagnostic imaging Long short term memory

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Analysis of Eavesdropping Region in Hybrid mmWave-Microwave Wireless systems

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Qu, Qianyue Zhang, Yuanyu Kasahara, Shoji Graduate School of Information Science Nara Institute of Science and Technology Ikoma Japan School of Computer Science and Technology Xidian University Xi’an China Shannxi Key Laboratory of Network and System Security Beijing Sunwise Information Technology Ltd Beijing China

Hybrid communication systems, where millimeter-wave (mmWave) links coexist with microwave links, have been an essential component in the fifth-generation (5G) wireless networks. Nevertheless, the open feature of the wireless medium makes hybrid systems vulnerable to eavesdropping attacks. Eavesdroppers in hybrid communication systems can enhance their attack performance by opportunistically eavesdropping on mmWave or microwave links. This paper, therefore, aims to answer a natural question: in which region do eavesdroppers prefer the mmWave links? To this end, we first formulate this question as an eavesdropping region characterization problem from the physical layer security perspective, where eavesdroppers select the link to eavesdrop based on the ratio between the security performances of the mmWave and microwave links. To model the security performances of both the mmWave and microwave links, we derive closed-form expressions for the secrecy outage probabilities and lower bounds/exact expressions for the secrecy rates of both links. Finally, we provide numerical results to validate our theoretical analysis and also illustrate the mmWave eavesdropping region under various network parameter settings. Copyright © 2023 Qianyue Qu et al.

关键词： Millimeter waves

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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