Computers, materials & continua最新文献

{"title":"Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing","authors":"Zhihong Ouyang, Lei Xue, Feng Ding, Yongsheng Duan","doi":"10.32604/cmc.2023.042222","DOIUrl":"https://doi.org/10.32604/cmc.2023.042222","url":null,"abstract":"Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge. Based on these findings, mutually exclusive exemplar detection was conducted on the current AP exemplars, and a pair of unsuitable exemplars for coexistence is recommended. The recommendation is then mapped as a novel constraint, designated mutual exclusion and aggregation. To address this limitation, a modified AP clustering model is derived and the clustering is restarted, which can result in exemplar number reduction, exemplar selection adjustment, and other data point redistribution. The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected. Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison, and many internal and external clustering evaluation indexes are used to measure the clustering performance. The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DTHN: Dual-Transformer Head End-to-End Person Search Network","authors":"Cheng Feng, Dezhi Han, Chongqing Chen","doi":"10.32604/cmc.2023.042765","DOIUrl":"https://doi.org/10.32604/cmc.2023.042765","url":null,"abstract":"Person search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network (CNN) (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically, after the image goes through the ResNet backbone network to extract features, the Region Proposal Network (RPN) proposes possible bounding boxes. The box head then extracts more efficient features within these bounding boxes for detection. Following this, the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds. Extensive experiments on two widely used benchmark datasets, CUHK-SYSU and PRW, achieve state-of-the-art performance levels, 94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset, and 51.6 mAP and 87.6 top-1 scores on the PRW dataset, which demonstrates the advantages of this paper’s approach. The efficiency comparison also shows our method is highly efficient in both time and space.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified MMS: Minimization Approach for Model Subset Selection","authors":"C. Rajathi, P. Rukmani","doi":"10.32604/cmc.2023.041507","DOIUrl":"https://doi.org/10.32604/cmc.2023.041507","url":null,"abstract":"Considering the recent developments in the digital environment, ensuring a higher level of security for networking systems is imperative. Many security approaches are being constantly developed to protect against evolving threats. An ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior studies. This research work aimed to create a more diverse and effective ensemble model. To this end, selected six classification models, Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) from existing study to run as independent models. Once the individual models were trained, a Correlation-Based Diversity Matrix (CDM) was created by determining their closeness. The models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection (Modified-MMS) from Lower triangular-CDM (L-CDM) as input. The proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases (NSL-KDD) dataset, and several performance metrics, including accuracy, precision, recall, and F1-score. By selecting a diverse set of models, the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction accuracy. The proposed work achieved an impressive accuracy of 99.26%, using only two classification models in an ensemble, which surpasses the performance of a larger ensemble that employs six classification models.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Analysis of Network Parameters for Secure Implementations of SDN-Based Firewall","authors":"Rizwan Iqbal, Rashid Hussain, Sheeraz Arif, Nadia Mustaqim Ansari, Tayyab Ahmed Shaikh","doi":"10.32604/cmc.2023.042432","DOIUrl":"https://doi.org/10.32604/cmc.2023.042432","url":null,"abstract":"","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135504582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Secure and Efficient Information Authentication Scheme for E-Healthcare System","authors":"Naveed Khan, Jianbiao Zhang, Ghulam Ali Mallah, Shehzad Ashraf Chaudhry","doi":"10.32604/cmc.2023.032553","DOIUrl":"https://doi.org/10.32604/cmc.2023.032553","url":null,"abstract":"The mobile cellular network provides internet connectivity for heterogeneous Internet of Things (IoT) devices. The cellular network consists of several towers installed at appropriate locations within a smart city. These cellular towers can be utilized for various tasks, such as e-healthcare systems, smart city surveillance, traffic monitoring, infrastructure surveillance, or sidewalk checking. Security is a primary concern in data broadcasting, particularly authentication, because the strength of a cellular network’s signal is much higher frequency than the associated one, and their frequencies can sometimes be aligned, posing a significant challenge. As a result, that requires attention, and without information authentication, such a barrier cannot be removed. So, we design a secure and efficient information authentication scheme for IoT-enabled devices to mitigate the flaws in the e-healthcare system. The proposed protocol security shall check formally using the Real-or-Random (ROR) model, simulated using ProVerif2.03, and informally using pragmatic discussion. In comparison, the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection","authors":"Hui Xu, Yalin Hu, Weidong Cao, Longjie Han","doi":"10.32604/cmc.2023.039227","DOIUrl":"https://doi.org/10.32604/cmc.2023.039227","url":null,"abstract":"The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimization-seeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the harris hawk optimization (HHO) and small hole imaging (HHJSOA). We use it in network traffic identification feature selection. First, the method incorporates the HHO escape energy factor and the hard siege strategy to form a new search strategy for HHJSOA. This location update strategy enhances the search range of the optimal solution of HHJSOA. We use small hole imaging to update the inferior individual. Next, the feature selection problem is coded to propose a jumping spiders individual coding scheme. Multiple iterations of the HHJSOA algorithm find the optimal individual used as the selected feature for KNN classification. Finally, we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset. Experimental results show that compared with other algorithms for the UNSW-NB15 dataset, the improvement is at least 0.0705, 0.00147, and 1 on the accuracy, fitness value, and the number of features. In addition, compared with other feature selection methods for the same datasets, the proposed algorithm has faster convergence, better merit-seeking, and robustness. Therefore, HHJSOA can improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy","authors":"Zeliang An, Tianqi Zhang, Debang Liu, Yuqing Xu, Gert Fr鴏und Pedersen, Ming Shen","doi":"10.32604/cmc.2023.037832","DOIUrl":"https://doi.org/10.32604/cmc.2023.037832","url":null,"abstract":"With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAM industry IoT network is inevitably vulnerable to adversary attacks from malicious IoT nodes. The FBMC-OQAM industry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle the above challenge. As a pivotal step of ICRNet, blind modulation recognition (BMR) can detect and recognize the modulation type of malicious signals. The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes. A novel FBMC BMR algorithm is proposed with the transform channel convolution network (TCCNet) rather than a complicated two-dimensional convolution. Firstly, this is achieved by designing a low-complexity binary constellation diagram (BCD) gridding matrix as the input of TCCNet. Then, a transform channel convolution strategy is developed to convert the image-like BCD matrix into a series-like data format, accelerating the BMR process while keeping discriminative features. Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8% and 40% over the traditional in-phase/quadrature (I/Q)-based and constellation diagram (CD)-based methods at a signal noise ratio (SNR) of 12 dB, respectively. Moreover, the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network (CD-AlexNet) and I/Q-Convolutional Long Deep Neural Network (I/Q-CLDNN) algorithms, respectively.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers","authors":"Asma A. Alhashmi, Abdulbasit A. Darem, Sultan M. Alanazi, Abdullah M. Alashjaee, Bader Aldughayfiq, Fuad A. Ghaleb, Shouki A. Ebad, Majed A. Alanazi","doi":"10.32604/cmc.2023.041038","DOIUrl":"https://doi.org/10.32604/cmc.2023.041038","url":null,"abstract":"In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for a multitude of sectors including petroleum and mining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features, providing a holistic and comprehensive view of malware behavior. From these features, we construct two XGBoost predictors, each of which contributes a valuable perspective on the malicious activities under scrutiny. The outputs of these predictors, interpreted as malicious scores, are then fed into an ANN-based classifier, which processes this data to derive a final decision. The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features, and most importantly, in its ability to extract and analyze the hidden relations between these two types of features. The efficacy of our proposed API-based hybrid model is evident in its performance metrics. It outperformed other models in our tests, achieving an impressive accuracy of 95% and an F-measure of 93%. This significantly improved the detection performance of malware variants, underscoring the value and potential of our approach in the challenging field of cybersecurity.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification","authors":"Sidra Abbas, Gabriel Avelino Sampedro, Shtwai Alsubai, Ahmad Almadhor, Tai-hoon Kim","doi":"10.32604/cmc.2023.041031","DOIUrl":"https://doi.org/10.32604/cmc.2023.041031","url":null,"abstract":"Cardiac disease is a chronic condition that impairs the heart’s functionality. It includes conditions such as coronary artery disease, heart failure, arrhythmias, and valvular heart disease. These conditions can lead to serious complications and even be life-threatening if not detected and managed in time. Researchers have utilized Machine Learning (ML) and Deep Learning (DL) to identify heart abnormalities swiftly and consistently. Various approaches have been applied to predict and treat heart disease utilizing ML and DL. This paper proposes a Machine and Deep Learning-based Stacked Model (MDLSM) to predict heart disease accurately. ML approaches such as eXtreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN), along with two DL models: Deep Neural Network (DNN) and Fine Tuned Deep Neural Network (FT-DNN) are used to detect heart disease. These models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart disease. Well-known evaluation measures (i.e., accuracy, precision, recall, F1-score, confusion matrix, and area under the Receiver Operating Characteristic (ROC) curve) are employed to check the efficacy of the proposed approach. Results reveal that the MDLSM achieves 94.14% prediction accuracy, which is 8.30% better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge","authors":"Shuqin Zhang, Xinyu Su, Peiyu Shi, Tianhui Du, Yunfei Han","doi":"10.32604/cmc.2023.040964","DOIUrl":"https://doi.org/10.32604/cmc.2023.040964","url":null,"abstract":"Cyber Threat Intelligence (CTI) is a valuable resource for cybersecurity defense, but it also poses challenges due to its multi-source and heterogeneous nature. Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly. To address these challenges, we propose a novel approach that consists of three steps. First, we construct the attack and defense analysis of the cybersecurity ontology (ADACO) model by integrating multiple cybersecurity databases. Second, we develop the threat evolution prediction algorithm (TEPA), which can automatically detect threats at device nodes, correlate and map multi-source threat information, and dynamically infer the threat evolution process. TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities. Third, we design the intelligent defense decision algorithm (IDDA), which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques. IDDA outperforms the baseline methods in the comparative experiment.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}