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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Tarun, R.G. Aggarwal, Priyanshu Ashwini, K. Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522100

Images are present in everything these days, from satellite photography to medical scans, and they have a profound impact on how people view and understand the world. The daily generation of an ever-increasing volume of image data has made it imperative to identify effective ways to convey these images. This research presents a novel picture recovery approach that makes use of Chaotic Maps for increased security and Compressive Sensing for effective compression. Compressive Sensing makes use of the sparsity seen in images to minimize the number of data points required. Chaotic Map is used in encryption, is also to guarantee the security of the compressed data. The Neural network model built to recover images is a hybrid of Conditional Generative Adversarial Networks (C-GANs), patch discriminator and U-net generator. This allows for high-quality image reconstruction. This approach is suitable for usage in healthcare and marine life. © 2025 IEEE.

关键词： Image compression

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and Technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Semi-supervised learning based hybrid beamforming under time-varying propagation environments

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Digital Communications and Networks 2024年第4期10卷 1168-1177页

作者： Yin Long Hang Ding Simon Murphy School of Computer Science and Technology Southwest University of Science and TechnologyMianyang621000China Department of Computing Science University of AlbertaEdmontonT6G 2R3Canada

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)*** this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming ***,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid *** with the existing network structure,the proposed network structure can achieve better transmission performance and lower ***,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data *** the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed *** the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel *** results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single ***,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

关键词： Hybrid beamforming Time-varying environments Broad network Semi-supervised learning Online learning

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai China Ant Group Shanghai China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Fudan University School of Computer Science Shanghai China Tongji University Department of Control Science and Engineering Shanghai China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the- art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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A comprehensive survey of federated transfer learning:challenges,methods and applications

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Frontiers of computer science 2024年第6期18卷 27-60页

作者： Wei GUO Fuzhen ZHUANG Xiao ZHANG Yiqi TONG Jin DONG Institute of Artificial Intelligence Beihang UniversityBeijing 100191China SKLSDE School of Computer ScienceBeihang UniversityBeijing 100191China School of Computer Science and Technology Shandong UniversityShandong 266237China Beijing Academy of Blockchain and Edge Computing Beijing 100080China

Federated learning(FL)is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data *** practice,FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target ***,many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying ***,the differences in their local devices(system heterogeneity),the continuous influx of online data(incremental data),and labeled data scarcity may further influence the performance of these *** solve this problem,federated transfer learning(FTL),which integrates transfer learning(TL)into FL,has attracted the attention of numerous ***,since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants,FTL faces many unique challenges that are not present in *** this survey,we focus on categorizing and reviewing the current progress on federated transfer learning,and outlining corresponding solutions and ***,the common setting of FTL scenarios,available datasets,and significant related research are summarized in this survey.

关键词： federated transfer learning federated learning transfer learning survey

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Surface Multiple Object Tracking: An Accurate HAT-YOLOv8-ADT Tracking Model

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IEEE Internet of Things Journal 2025年第11期12卷 18266-18278页

作者： Lin, Na Zhang, Lei Wu, Tianxiong Hawbani, Ammar Zhou, Huiyu Zhao, Liang Shenyang Aerospace University School of Computer Science Shen-yang110136 China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom

With the development of artificial intelligence technology, Autonomous aerial vehicles (AAV) have the ability to sense the environment. Multiple object tracking (MOT) in AAV video is a very important vision task with a wide variety of applications. However, there are still many challenges in MOT in AAV video. First, the movement of the onboard camera in the three-dimensional (3D) direction during the tracking process, as well as the unpredictable measurement noise characteristics of AAVs flying at high speeds, can lead to significant deviations in the prediction of the object's position. Second, the applicability of the traditional detection algorithm decreases when the object is small and dense in the AAV viewpoint during detection. Finally, the traditional intersection over union (IoU) matching approach does not take into account the effects of the height and width of the box, and the matching results are inaccurate for the prediction and detection box. In order to address these challenges, we recommend the adaptive DeepSort (ADT) algorithm to reduce the prediction bias due to camera movement and difficulty in predetermining measurement noise characteristics, the hybrid attention transformer-YOLOv8 (HAT-YOLOv8) algorithm to enhance the detection capability of tiny objects, and the intersection over union of height and width (HWIoU) matching algorithm, which improves the matching accuracy and thus the tracking accuracy. Experimental results show that our proposed solution outperforms the baseline solution. It outperforms the current mainstream StrongSort in MOTA, HOTA and IDF1 by 2.86%, 0.9% and 9.36%. Code repository link: https://***/networkcommunication/. © 2025 IEEE.

关键词： Object tracking

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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Amazon Web Services Cloud Compliance Automation with Open Policy Agent 4

Amazon Web Services Cloud Compliance Automation with Open Po...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Paul, Alen Manoj, Rishi Udhayakumar, S. Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350385793

The security challenges posed by Infrastructure as code (IaC) are outgrowing established security procedures in an era of rapidly adopting cloud computing and DevOps methodologies. Using security principles to be integrated into the CI/CD pipeline for continuous validation and remediation, this research work proposes a DevSecOps approach to enable cloud environment IaC security. The proposed architecture evaluates CloudFormation Template file to the specified security policy by utilizing Open Policy Agent (OPA). Any violations are flagged by OPA, which may force the deployment process to stop. By incorporating security and compliance considerations into the development pipeline, this method guarantees that vulnerabilities are kept out of production systems. Organizations can improve the overall security posture of their cloud systems by proactively identifying and remediating security problems by implementing a DevSecOps approach. By ensuring that IaC deployments adhere to specified security policies based on the compliance requirements, OPA's continuous security validation reduces the possibility of misconfigurations and security flaws. © 2024 IEEE.

关键词： Cloud security

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