Concurrency and Computation-Practice & Experience最新文献_第4页

Enhanced multi-key privacy-preserving distributed deep learning protocol with application to diabetic retinopathy diagnosis 应用于糖尿病视网膜病变诊断的增强型多密钥隐私保护分布式深度学习协议

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-22 DOI: 10.1002/cpe.8263

Emmanuel Antwi-Boasiako, Shijie Zhou, Yongjian Liao, Isaac Amankona Obiri, Eric Kuada, Ebenezer Kwaku Danso, Edward Mensah Acheampong

{"title":"Enhanced multi-key privacy-preserving distributed deep learning protocol with application to diabetic retinopathy diagnosis","authors":"Emmanuel Antwi-Boasiako, Shijie Zhou, Yongjian Liao, Isaac Amankona Obiri, Eric Kuada, Ebenezer Kwaku Danso, Edward Mensah Acheampong","doi":"10.1002/cpe.8263","DOIUrl":"10.1002/cpe.8263","url":null,"abstract":"<div>\u0000 \u0000 <p>In this work, privacy-preserving distributed deep learning (PPDDL) is re-visited with a specific application to diagnosing long-term illness like diabetic retinopathy. In order to protect the privacy of participants datasets, a multi-key PPDDL solution is proposed which is robust against collusion attacks and is also post-quantum robust. Additionally, the PPDDL solution provides robust network security in terms of integrity of transmitted ciphertexts and keys, forward secrecy, and prevention of man-in-the-middle attacks and is extensively verified using Verifpal. Proposed solution is evaluated on retina image datasets to detect diabetic retinopathy, with deep learning accuracy results of 96.30%, 96.21% and 96.20% for DDL, DDL + SINGLE and DDL + MULTI scenarios respectively. Results from our simulation indicate that accuracy of the PPDDL is maintained while protecting the privacy of the datasets of participants. Our proposed solution is also efficient in terms of the communication and run-time costs.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-task enhanced global–local temporal–spatial network for depression recognition from facial videos 用于从面部视频识别抑郁的全局-局部时空双任务增强网络

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-21 DOI: 10.1002/cpe.8255

Jinjie Shen, Jing Wu, Yan Xing, Min Hu, Xiaohua Wang, Daolun Li, Wenshu Zha

{"title":"Dual-task enhanced global–local temporal–spatial network for depression recognition from facial videos","authors":"Jinjie Shen, Jing Wu, Yan Xing, Min Hu, Xiaohua Wang, Daolun Li, Wenshu Zha","doi":"10.1002/cpe.8255","DOIUrl":"10.1002/cpe.8255","url":null,"abstract":"<div>\u0000 \u0000 <p>In previous studies on facial video depression recognition, although convolutional neural network (CNN) has become a mainstream method, its performance still has room for improvement due to the insufficient extraction of global and local information and the neglect of the correlation of temporal and spatial information. This paper proposes a novel dual-task enhanced global–local temporal–spatial network (DTE-GLTS) to enhance the extraction capability of global and local features and deepen the analysis of temporal–spatial information correlation. We design a dual-task learning mode that utilizes the data-efficient image transformer (Deit) as the main body to learn the global features of video sequences and guides Deit to learn local features with the pre-trained temporal–spatial fusion network (TSF). In addition, we propose the TSF mechanism to more effectively fuse temporal–spatial information in video sequences, strengthen the correlation between frames and pixels, and embed it in Resnet to form the TSF network. To the best of our knowledge, this is the first application of Deit and dual-task learning mode in the field of facial video depression recognition. The experimental results on AVEC 2013 and AVEC 2014 show that our method achieves competitive performance, with mean absolute error/root mean square error (MAE/RMSE) scores of 6.06/7.73 and 5.91/7.68, respectively, while significantly reducing the number of parameters.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image encryption algorithm based on a novel 2D logistic-sine-coupling chaos map and bit-level dynamic scrambling 基于新型二维逻辑正弦耦合混沌图和位级动态加扰的图像加密算法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-21 DOI: 10.1002/cpe.8261

Jie Fang, Kaihui Zhao, Shixiao Liang, Jiabin Wang

{"title":"Image encryption algorithm based on a novel 2D logistic-sine-coupling chaos map and bit-level dynamic scrambling","authors":"Jie Fang, Kaihui Zhao, Shixiao Liang, Jiabin Wang","doi":"10.1002/cpe.8261","DOIUrl":"10.1002/cpe.8261","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper develops a new image encryption algorithm based on a novel two-dimensional chaotic map and bit-level dynamic scrambling. First, multiple one-dimensional chaotic maps are coupled to construct a novel two dimensions Logistic-Sine-coupling chaos map (2D-LSCCM). The performance analysis shows that the 2D-LSCCM has more complex chaotic characteristics and wider chaotic range than many extant 2D chaos maps. Second, original image matrix combines with hash algorithm SHA-256 to generate a hash value. The initial values of 2D-LSCCM are generated based on the hash value. Third, the original image matrix is divided into multiple sub-matrices by wavelet transform, followed by scrambling by an improved Knuth shuffle algorithm. Fourth, the scrambled multiple sub-matrices are stitched into an image matrix of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 <mo>×</mo>\u0000 <mi>N</mi>\u0000 <mo>×</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 <annotation>$$ Mtimes Ntimes 3 $$</annotation>\u0000 </semantics></math> and converted into a binary matrix. The chaotic sequence generated by 2D-LSCCM is introduced as a control sequence to control the bit-level scrambling of pixel points, which realizes the bit-level dynamic scrambling. Finally, the diffusion operation is performed by parameter <i>par</i> and chaotic sequence to obtain the final encrypted image. The algorithm security analysis and simulation examples demonstrate the effectiveness of the proposed encryption scheme.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on insulator image segmentation and defect recognition technology based on U-Net and YOLOv7 基于 U-Net 和 YOLOv7 的绝缘体图像分割和缺陷识别技术研究

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-21 DOI: 10.1002/cpe.8266

Jiawen Chen, Chao Cai, Fangbin Yan, Bowen Zhou

{"title":"Research on insulator image segmentation and defect recognition technology based on U-Net and YOLOv7","authors":"Jiawen Chen, Chao Cai, Fangbin Yan, Bowen Zhou","doi":"10.1002/cpe.8266","DOIUrl":"10.1002/cpe.8266","url":null,"abstract":"<p>This study focuses on aerial images in power line inspection, using a small sample size and concentrating on accurately segmenting insulators in images and identifying potential “self-explode” defects through deep learning methods. The research process consists of four key steps: image segmentation of insulators, identification of small connected regions, data augmentation of original samples, and detection of insulator defects using the YOLO v7 model. In this paper, due to the small sample size, sample expansion is considered first. A sliding window approach is adopted to crop images, increasing the number of training samples. Subsequently, the U-Net neural network model for semantic segmentation is used to train insulator images, thereby generating preliminary mask images of insulators. Then, through connected region area filtering techniques, smaller connected regions are removed to eliminate small speckles in the predicted mask images, obtaining more accurate insulator mask images. The evaluation metric for image recognition, the dice coefficient, is 93.67%. To target the identification of insulator defects, 35 images with insulator defects from the original samples are augmented. These images are input into the YOLO v7 network for further training, ultimately achieving effective detection of insulator “self-explode” defects.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An archive-based method for efficiently handling small file problems in HDFS 基于归档的方法，有效处理 HDFS 中的小文件问题

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-20 DOI: 10.1002/cpe.8260

Junnan Liu, Shengyi Jin, Dong Wang, Han Li

{"title":"An archive-based method for efficiently handling small file problems in HDFS","authors":"Junnan Liu, Shengyi Jin, Dong Wang, Han Li","doi":"10.1002/cpe.8260","DOIUrl":"10.1002/cpe.8260","url":null,"abstract":"<div>\u0000 \u0000 <p>Hadoop distributed file system (HDFS) performs well when storing and managing large files. However, its performance significantly decreases when dealing with massive small files. In response to this problem, a novel archive-based solution is proposed. The archive refers to merging multiple small files into larger data files, which can effectively reduce the memory usage of the NameNode. The current archive-based solutions have the disadvantages of long access time, long archive construction time, and no support for storage, updating and deleting small files in the archive system. Our method utilizes a dynamic hash function to distribute the metadata of small files across multiple metadata files. We construct a primary index that combines dynamic and static indexes for these metadata files. Regarding data files, include some read-only files and one readable–writable file. A small file's contents are written into a readable and writable file. Upon reaching a predetermined threshold, the readable–writable file transitions into read-only status, with a fresh readable–writable file replacing it. Experimental results show that the scheme improves the efficiency of archive access and archive creation and is more efficient than the original HDFS storage and update efficiency.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recommending cloud services based on social trust: An overview 基于社会信任的云服务推荐：综述

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-20 DOI: 10.1002/cpe.8262

Fatma Zohra Lebib, Saida Kichou

{"title":"Recommending cloud services based on social trust: An overview","authors":"Fatma Zohra Lebib, Saida Kichou","doi":"10.1002/cpe.8262","DOIUrl":"10.1002/cpe.8262","url":null,"abstract":"<p>The continued expansion and development of the business requires great computing power and massive data storage systems. Cloud services deliver these resources in a simple, flexible and secure way. There is now a wide range of similar cloud services with different capabilities, which requires a recommendation system. Recommendation based on Quality of Service (QoS) is the first generation of service recommendation systems that only takes into account the rating information of all users without distinction. However, these systems suffer from many shortcomings, such as cold start and data sparsity issues, as well as poor accuracy and reliability of recommendation results. To address these issues and improve the quality of recommendations, a new generation of recommender systems has emerged, such as context-aware, domain-specific, and trust-aware recommender systems. These systems now focus more on how to leverage social data generated from user interactions with each other in social networks to recommend more suitable and reliable services in response to user needs. Due to the importance of considering trust in cloud environments, this study aims to provide an overview of the research on trust-based cloud service recommendation approaches proposed so far and highlights the current trend towards use new technologies such as deep learning to deal with certain challenges.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VoWiFi cell capacity improvement using A-MPDU frame aggregation technique for VBR traffic 利用 A-MPDU 帧聚合技术提高 VBR 流量的 VoWiFi 小区容量

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-20 DOI: 10.1002/cpe.8247

Ayes Chinmay, Hemanta Kumar Pati

{"title":"VoWiFi cell capacity improvement using A-MPDU frame aggregation technique for VBR traffic","authors":"Ayes Chinmay, Hemanta Kumar Pati","doi":"10.1002/cpe.8247","DOIUrl":"10.1002/cpe.8247","url":null,"abstract":"<div>\u0000 \u0000 <p>The expanding popularity of Voice over WiFi (VoWiFi) necessitates a concerted effort to identify novel ways to increase VoWiFi cell capacity. The primary objective of this study is to increase the capacity of VoWiFi cells by means of frame aggregation of aggregate MAC protocol data unit (A-MPDU) for variable bit rate (VBR) traffic. Taking into account Arbitration Inter-frame Spacing (AIFS), Compressed RTP (cRTP) and A-MPDU frames, we devised a formula to calculate an approximate number of concurrent VoWiFi users that can coexist with no detriment to the quality-of-service (QoS) of existing VoWiFi calls over the Wireless Fidelity (WiFi) standards. Here, we used AIFS to determine the channel's health before sending Voice over WiFi data and Short Inter-frame Spacing (SIFS) to transfer frames such as Request To Send (RTS)/Clear To Send (CTS) and Acknowledgement (ACK). We have used our suggested model to analyse the capacity of VoWiFi cells in IEEE 802.11b/g/n/ac/ax/be Wireless Local Area Network (WLAN)s with VBR traffic utilising DCF Inter-frame Spacing (DIFS) and AIFS. For IEEE 802.11b/g/n/ac/ax/be, we also determined the most number of MAC protocol data unit (MPDU)s that may be combined into a single A-MPDU. We have also studied the impact of voice packet retransmission on the cell capacity of a WLAN standard that offers VoWiFi service while taking A-MPDU method into account. We have compared the results gained using IEEE 802.11be with earlier WLAN standards like IEEE 802.11b/g/n/ac/ax considering the constant bit rate (CBR) and VBR traffics.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The blockchain-based privacy-preserving searchable attribute-based encryption scheme for federated learning model in IoMT IoMT 联合学习模型中基于区块链的隐私保护可搜索属性加密方案

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-20 DOI: 10.1002/cpe.8257

Ziyu Zhou, Na Wang, Jianwei Liu, Junsong Fu, Lunzhi Deng

{"title":"The blockchain-based privacy-preserving searchable attribute-based encryption scheme for federated learning model in IoMT","authors":"Ziyu Zhou, Na Wang, Jianwei Liu, Junsong Fu, Lunzhi Deng","doi":"10.1002/cpe.8257","DOIUrl":"10.1002/cpe.8257","url":null,"abstract":"<p>Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy-preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non-target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain-based privacy-preserving searchable attribute-based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM-FL). We first adopt fine-grained model trainer participation policies for federated learning, using the attribute-based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword-based search and model users' attribute-based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid energy-Efficient distributed aided frog leaping dynamic A* with reinforcement learning for enhanced trajectory planning in UAV swarms large-scale networks 混合节能分布式辅助蛙跳动态 A* 与强化学习用于增强无人机群大规模网络的轨迹规划

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-20 DOI: 10.1002/cpe.8237

R. Christal Jebi, S. Baulkani, L. Femila

{"title":"Hybrid energy-Efficient distributed aided frog leaping dynamic A* with reinforcement learning for enhanced trajectory planning in UAV swarms large-scale networks","authors":"R. Christal Jebi, S. Baulkani, L. Femila","doi":"10.1002/cpe.8237","DOIUrl":"10.1002/cpe.8237","url":null,"abstract":"<div>\u0000 \u0000 <p>UAVs are emerging as a critical asset in the field of data collection from extensive wireless sensor networks (WSNs) on a large scale. UAVs can be used to deploy energy-efficient nodes or recharge nodes, but it should not compromise the network's coverage and connectivity. This paper proposes a comprehensive approach to optimize UAV trajectories within large-scale WSNs, utilizing Multi-Objective Reinforcement Learning (MORL) to balance critical objectives such as coverage, connectivity, and energy efficiency. This research investigates the configuration of a Wireless Sensor Network (WSN) assisted by a pen_spark UAV. In this network, Cluster Heads (CHs) act as central points for collecting data from their assigned sensor nodes. A predefined path is established for the UAV to efficiently gather data from these CHs. The Hybrid Threshold-sensitive Energy Efficient Network (Hy-TEEN) encompasses sophisticated algorithms for CH selection, dynamic A* for 3D trajectory planning and leverages reinforcement learning for multi-objective optimization. The experimental results and analysis demonstrate the effectiveness and efficiency of the proposed approach in improving UAV performance and energy efficiency. The results demonstrate that the proposed methodology's trajectories are capable of achieving a time savings of 3.52% in mission completion when contrasted with conventional baseline methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HybGBS: A hybrid neural network and grey wolf optimizer for intrusion detection in a cloud computing environment HybGBS：用于云计算环境中入侵检测的混合神经网络和灰狼优化器

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-19 DOI: 10.1002/cpe.8264

S Sumathi, R Rajesh

{"title":"HybGBS: A hybrid neural network and grey wolf optimizer for intrusion detection in a cloud computing environment","authors":"S Sumathi, R Rajesh","doi":"10.1002/cpe.8264","DOIUrl":"https://doi.org/10.1002/cpe.8264","url":null,"abstract":"<div>\u0000 \u0000 <p>The cloud computing environment is subject to unprecedented cyber-attacks as its infrastructure and protocols may contain vulnerabilities and bugs. Among these, Distributed Denial of Service (DDoS) is chosen by most cyber extortionists, creating unusual traffic that drains cloud resources, making them inaccessible to customers and end users. Hence, security solutions to combat this attack are in high demand. The existing DDoS detection techniques in literature have many drawbacks, such as overfitting, delay in detection, low detection accuracy for attacks that target multiple victims, and high False Positive Rate (FPR). In this proposed study, an Artificial Neural Network (ANN) based hybrid GBS (Grey Wolf Optimizer (GWO) + Back Propagation Network (BPN) + Self Organizing Map (SOM)) Intrusion Detection System (IDS) is proposed for intrusion detection in the cloud computing environment. The base classifier, BPN, was chosen for our research after evaluating the performance of a comprehensive set of neural network algorithms on the standard benchmark UNSW-NS 15 dataset. BPN intrusion detection performance is further enhanced by combining it with SOM and GWO. Hybrid Feature Selection (FS) is made using a correlation-based approach and Stratified 10-fold cross-validation (STCV) ranking based on Weight matrix value (W). These selected features are further fine-tuned using metaheuristic GWO hyperparameter tuning based on a fitness function. The proposed IDS technique is validated using the standard benchmark UNSW-NS 15 dataset, which consists of 1,75,341 and 82,332 attack cases in the training and testing datasets. This study's findings demonstrate that the proposed ANN-based hybrid GBS IDS model outperforms other existing IDS models with a higher intrusion detection accuracy of 99.40%, fewer false alarms (0.00389), less error rate (0.001), and faster prediction time (0.29 ns).</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142404823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0