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Blockchain-Based Certificateless Bidirectional Authenticated Searchable Encryption Scheme in Cloud Email System

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3287-3310页

作者： Yanzhong Sun Xiaoni Du Shufen Niu Xiaodong Yang college of Mathematics and Statistics Northwest Normal UniversityLanzhou730070China college of computer Science and Engineering Northwest Normal UniversityLanzhou730070China

Traditional email systems can only achieve one-way communication,which means only the receiver is allowed to search for emails on the email *** this paper,we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption(CL-BSE)by combining the storage function of cloud server with the communication function of email *** the new model,not only can the data receiver search for the relevant content by generating its own trapdoor,but the data owner also can retrieve the content in the same ***,there are dual authentication functions in our ***,during encryption,the data owner uses the private key to authenticate their identity,ensuring that only legal owner can generate the keyword ***,the blockchain verifies the data owner’s identity by the received ciphertext,allowing only authorized members to store their data in the server and avoiding unnecessary storage space *** obtain a formal definition of CL-BSE and formulate a specific scheme from the new system *** the security of the scheme is analyzed based on the formalized security *** results demonstrate that the scheme achieves multikeyword ciphertext indistinguishability andmulti-keyword trapdoor privacy against any adversary *** addition,performance evaluation shows that the new scheme has higher computational and communication efficiency by comparing it with some existing ones.

关键词： Cloud email system authenticated searchable encryption blockchain-based designated server test multi-trapdoor privacy multi-ciphertext indistinguishability

Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

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Journal of Ocean University of China 2024年第3期23卷 654-660页

作者： WANG Fangchen ZHONG Guoqiang WANG Liang college of computer Science and Technology Ocean University of ChinaQingdao 266100China college of Marine Technology Ocean University of ChinaQingdao 266100China

Underwater pulse waveform recognition is an important method for underwater object *** existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform *** propagation channels in seawater are time-and space-varying convolutional *** the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent *** propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform *** the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is *** constraint can ensure that the influence of convolutional channels on hash features is *** addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash *** results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

关键词： convolutional channel hash aggregate discriminative network aggregate discriminant loss waveform recognition

Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

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computers, Materials & Continua 2024年第8期80卷 1809-1829页

作者： Yu Zhou Yun Zhang Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu North China Institute of Computing Technology Beijing Beijing100080China School of computer and communication Engineering University of Science and Technology BeijingBeijing100083China

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often *** is frequently assumed that vehicles can be accurately modeled during actual motion ***,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network *** into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading *** optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming *** to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and ***,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization *** results show that the algorithm proposed in this paper is able to achieve lower latency task computation ***,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

关键词： Component vehicular dynamic task offloading resource scheduling

Introduction to the Special Issue on Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

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computer Modeling in Engineering & Sciences 2024年第10期141卷 7-11页

作者： Xiong Luo Yongqiang Cheng Zhifang Liao School of computer and communication Engineering University of Science and Technology BeijingBeijing100083China Faculty of Technology University of SunderlandSunderlandSR60DDUK School of computer Science and Engineering Central South UniversityChangsha410083China

With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human ***,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].

关键词： Intelligence bringing intelligent

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

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computers, Materials & Continua 2024年第4期79卷 1539-1560页

作者： Fayez Alfayez Department of computer Science and Information College of ScienceMajmaah UniversityAL-Majmaah11952Saudi Arabia

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for *** an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

关键词： Feature reduction image classification X-ray images

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

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computers, Materials & Continua 2024年第1期78卷 411-433页

作者： Zhihua Liu Shengquan Liu Jian Zhang college of computer Science and Technology Xinjiang UniversityXinjiang Uygur Autonomous RegoinUrumqi830000China

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant ***,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection *** the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal *** address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion ***,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network ***,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network *** addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each *** experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.

关键词： Intrusion detection industrial internet channel spatial attention multiscale features dynamic fusion multi-output learning strategy

YOLO-Drone:A Scale-Aware Detector for Drone Vision

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Chinese Journal of Electronics 2024年第4期33卷 1034-1045页

作者： Yutong LI Miao MA Shichang LIU Chao YAO Longjiang GUO School of computer Science Shaanxi Normal University Key Laboratory of Modern Teaching Technology Ministry of Education college of computer Science Sichuan University

Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention(SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss(SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset(detection track) show that our detector can achieve average precision(AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile,reducing 39.8% of the computational cost.

关键词： YOLO Costs Object oriented modeling Computational modeling Detectors Feature extraction Data models

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

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Journal of Systems Engineering and Electronics 2024年第2期35卷 316-329页

作者： WEI Shaopeng ZHANG Lei LU Jingyue LIU Hongwei college of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao 266580China School of Electronics Engineering Xidian UniversityXi’an 710071China School of Electronics and communication Engineering Shenzhen Campus of Sun Yat-sen UniversityGuangzhou 518107China School of computer Science and Technology Xidian UniversityXi’an 710071China

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation *** paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary *** the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time *** dictionary learning is followed to separate real signals from jamming *** the learned representation,time-varying MISRJs are suppressed *** simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

关键词： synthetic aperture radar(SAR) modulated interrupt sampling jamming(MISRJ) online dictionary learning

Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)