Abstract In this study, we proposed a fast line-structured light stripe center extraction algorithm based on an improved barycenter algorithm to address the problem that the conventional strip center extraction algorithm cannot meet the requirements of a structured light 3D measurement system in terms of speed and accuracy. First, the algorithm performs pretreatment of the structured light image and obtains the approximate position of the stripe center through skeleton extraction. Next, the normal direction of each pixel on the skeleton is solved using the gray gradient method. Then, the weighted gray center of the gravity method is used to solve the stripe center coordinates along the normal direction. Finally, a smooth strip centerline is fitted using the least squares method. The experimental results show that the improved algorithm achieved significant improvement in speed, sub-pixel level accuracy, and a good structured light stripe center extraction effect, as well as the repeated measurement accuracy of the improved algorithm is within 0.01 mm, and the algorithm has good repeatability.
{"title":"Research on the center extraction algorithm of structured light fringe based on an improved gray gravity center method","authors":"Jun Wang, Jingjing Wu, Xiang Jiao, Yue Ding","doi":"10.1515/jisys-2022-0195","DOIUrl":"https://doi.org/10.1515/jisys-2022-0195","url":null,"abstract":"Abstract In this study, we proposed a fast line-structured light stripe center extraction algorithm based on an improved barycenter algorithm to address the problem that the conventional strip center extraction algorithm cannot meet the requirements of a structured light 3D measurement system in terms of speed and accuracy. First, the algorithm performs pretreatment of the structured light image and obtains the approximate position of the stripe center through skeleton extraction. Next, the normal direction of each pixel on the skeleton is solved using the gray gradient method. Then, the weighted gray center of the gravity method is used to solve the stripe center coordinates along the normal direction. Finally, a smooth strip centerline is fitted using the least squares method. The experimental results show that the improved algorithm achieved significant improvement in speed, sub-pixel level accuracy, and a good structured light stripe center extraction effect, as well as the repeated measurement accuracy of the improved algorithm is within 0.01 mm, and the algorithm has good repeatability.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"80 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":"134883378","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}
Abstract In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use.
{"title":"A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor","authors":"K. Archana, G. Komarasamy","doi":"10.1515/jisys-2022-0206","DOIUrl":"https://doi.org/10.1515/jisys-2022-0206","url":null,"abstract":"Abstract In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86866533","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}
Abstract An intrusion detection system plays an essential role in system security by discovering and preventing malicious activities. Over the past few years, several research projects on host-based intrusion detection systems (HIDSs) have been carried out utilizing the Australian Defense Force Academy Linux Dataset (ADFA-LD). These HIDS have also been subjected to various algorithm analyses to enhance their detection capability for high accuracy and low false alarms. However, less attention is paid to the actual implementation of real-time HIDS. Our principal objective in this study is to create a performant real-time HIDS. We propose a new model, “Better Similarity Algorithm for Host-based Intrusion Detection System” (BSA-HIDS), using the same dataset ADFA-LD. The proposed model uses three classifications to represent the attack folder according to certain criteria, the entire system call sequence is used. Furthermore, this work uses textual distance and compares five algorithms like Levenshtein, Jaro–Winkler, Jaccard, Hamming, and Dice coefficient, to classify the system call trace as attack or non-attack based on the notions of interclass decoupling and intra-class coupling. The model can detect zero-day attacks because of the threshold definition. The experimental results show a good detection performance in real-time for Levenshtein/Jaro–Winkler algorithms, 99–94% in detection rate, 2–5% in false alarm rate, and 3,300–720 s in running time, respectively.
{"title":"Towards a better similarity algorithm for host-based intrusion detection system","authors":"Lounis Ouarda, Malika Bourenane, Bouderah Brahim","doi":"10.1515/jisys-2022-0259","DOIUrl":"https://doi.org/10.1515/jisys-2022-0259","url":null,"abstract":"Abstract An intrusion detection system plays an essential role in system security by discovering and preventing malicious activities. Over the past few years, several research projects on host-based intrusion detection systems (HIDSs) have been carried out utilizing the Australian Defense Force Academy Linux Dataset (ADFA-LD). These HIDS have also been subjected to various algorithm analyses to enhance their detection capability for high accuracy and low false alarms. However, less attention is paid to the actual implementation of real-time HIDS. Our principal objective in this study is to create a performant real-time HIDS. We propose a new model, “Better Similarity Algorithm for Host-based Intrusion Detection System” (BSA-HIDS), using the same dataset ADFA-LD. The proposed model uses three classifications to represent the attack folder according to certain criteria, the entire system call sequence is used. Furthermore, this work uses textual distance and compares five algorithms like Levenshtein, Jaro–Winkler, Jaccard, Hamming, and Dice coefficient, to classify the system call trace as attack or non-attack based on the notions of interclass decoupling and intra-class coupling. The model can detect zero-day attacks because of the threshold definition. The experimental results show a good detection performance in real-time for Levenshtein/Jaro–Winkler algorithms, 99–94% in detection rate, 2–5% in false alarm rate, and 3,300–720 s in running time, respectively.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"20 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85007870","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}
Abstract This study based on the standard differential evolution (DE) algorithm was carried out to address the issues of control parameter imprinting, mutation process, and crossover process in the standard DE algorithm as well as the issue of multidimensional circuit testing optimization. A rotation control vector was introduced to expand the search range in the poor strategy to the circumference range of the individual and the parent target individual, and a rotation crossover operator and a binomial poor operator were combined. Finally, an improved adaptive DE algorithm based on a multi-angle search rotation crossover strategy was obtained. The research will improve the DE algorithm to optimize the testing of multidimensional circuits. It can be noted that the improved average precision value is 0.9919 when comparing the precision recall curves of the DE algorithm before and after the change, demonstrating a significant improvement in accuracy and stability. The fitness difference of the 30-dimensional problem is discovered to be between 0.25 × 103 and 0.5 × 103 by comparing the box graphs of the 30-dimensional problem with that of the 50-dimensional problem. On the 50-dimensional problem, when calculating the F4–F10 function, the fitness difference of the improved DE algorithm is 0.2 × 104–0.4 × 104. In summary, the improved DE algorithm proposed in this study compensates for the shortcomings of traditional algorithms in complex problem calculations and has also achieved significant optimization results in multidimensional circuit testing.
{"title":"Application of adaptive improved DE algorithm based on multi-angle search rotation crossover strategy in multi-circuit testing optimization","authors":"Wenchang Wu","doi":"10.1515/jisys-2022-0269","DOIUrl":"https://doi.org/10.1515/jisys-2022-0269","url":null,"abstract":"Abstract This study based on the standard differential evolution (DE) algorithm was carried out to address the issues of control parameter imprinting, mutation process, and crossover process in the standard DE algorithm as well as the issue of multidimensional circuit testing optimization. A rotation control vector was introduced to expand the search range in the poor strategy to the circumference range of the individual and the parent target individual, and a rotation crossover operator and a binomial poor operator were combined. Finally, an improved adaptive DE algorithm based on a multi-angle search rotation crossover strategy was obtained. The research will improve the DE algorithm to optimize the testing of multidimensional circuits. It can be noted that the improved average precision value is 0.9919 when comparing the precision recall curves of the DE algorithm before and after the change, demonstrating a significant improvement in accuracy and stability. The fitness difference of the 30-dimensional problem is discovered to be between 0.25 × 103 and 0.5 × 103 by comparing the box graphs of the 30-dimensional problem with that of the 50-dimensional problem. On the 50-dimensional problem, when calculating the F4–F10 function, the fitness difference of the improved DE algorithm is 0.2 × 104–0.4 × 104. In summary, the improved DE algorithm proposed in this study compensates for the shortcomings of traditional algorithms in complex problem calculations and has also achieved significant optimization results in multidimensional circuit testing.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81469329","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}
I. Salman, K. M. Saffer, Hayder H. Safi, S. Mostafa, Bashar Ahmad Khalaf
Abstract The efficiency of distribution networks is hugely affected by active and reactive power flows in distribution electric power systems. Currently, distributed generators (DGs) of energy are extensively applied to minimize power loss and improve voltage deviancies on power distribution systems. The best position and volume of DGs produce better power outcomes. This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. The SSA–GWO algorithm ensures generating the best size and site of one and multi-DGs on the radial distribution network to decrease real power losses (RPL) (kW) on lines and resolve voltage deviancies. Our novel algorithm is executed on IEEE 123-bus radial distribution test systems. The results confirm the success of the suggested hybrid SSA–GWO algorithm compared with implementing the SSA and GWO individually. Through the proposed SSA–GWO algorithm, the study decreases the RPL and improves the voltage profile on distribution networks with multiple DGs units.
{"title":"Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems","authors":"I. Salman, K. M. Saffer, Hayder H. Safi, S. Mostafa, Bashar Ahmad Khalaf","doi":"10.1515/jisys-2022-0221","DOIUrl":"https://doi.org/10.1515/jisys-2022-0221","url":null,"abstract":"Abstract The efficiency of distribution networks is hugely affected by active and reactive power flows in distribution electric power systems. Currently, distributed generators (DGs) of energy are extensively applied to minimize power loss and improve voltage deviancies on power distribution systems. The best position and volume of DGs produce better power outcomes. This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. The SSA–GWO algorithm ensures generating the best size and site of one and multi-DGs on the radial distribution network to decrease real power losses (RPL) (kW) on lines and resolve voltage deviancies. Our novel algorithm is executed on IEEE 123-bus radial distribution test systems. The results confirm the success of the suggested hybrid SSA–GWO algorithm compared with implementing the SSA and GWO individually. Through the proposed SSA–GWO algorithm, the study decreases the RPL and improves the voltage profile on distribution networks with multiple DGs units.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"8 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78754394","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}
Abstract Anomaly detection is a fundamental problem in data science and is one of the highly studied topics in machine learning. This problem has been addressed in different contexts and domains. This article investigates anomalous data within time series data in the maritime sector. Since there is no annotated dataset for this purpose, in this study, we apply an unsupervised approach. Our method benefits from the unsupervised learning feature of autoencoders. We utilize the reconstruction error as a signal for anomaly detection. For this purpose, we estimate the probability density function of the reconstruction error and find different levels of abnormality based on statistical attributes of the density of error. Our results demonstrate the effectiveness of this approach for localizing irregular patterns in the trajectory of vessel movements.
{"title":"Anomaly detection for maritime navigation based on probability density function of error of reconstruction","authors":"Zahra Sadeghi, Stan Matwin","doi":"10.1515/jisys-2022-0270","DOIUrl":"https://doi.org/10.1515/jisys-2022-0270","url":null,"abstract":"Abstract Anomaly detection is a fundamental problem in data science and is one of the highly studied topics in machine learning. This problem has been addressed in different contexts and domains. This article investigates anomalous data within time series data in the maritime sector. Since there is no annotated dataset for this purpose, in this study, we apply an unsupervised approach. Our method benefits from the unsupervised learning feature of autoencoders. We utilize the reconstruction error as a signal for anomaly detection. For this purpose, we estimate the probability density function of the reconstruction error and find different levels of abnormality based on statistical attributes of the density of error. Our results demonstrate the effectiveness of this approach for localizing irregular patterns in the trajectory of vessel movements.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"44 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":"135710303","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}
Abstract In this work, a technique is proposed to identify the diseases that occur in plants. The system is based on a combination of residual network and attention learning. The work focuses on disease identification from the images of four different plant types by analyzing leaf images of the plants. A total of four datasets are used for the work. The system incorporates attention-aware features computed by the Residual Attention Network (Res-ATTEN). The base of the network is ResNet-18 architecture. Integrating attention learning in the residual network helps improve the system's overall accuracy. Various residual attention units are combined to create a single architecture. Unlike the traditional attention network architectures, which focus only on a single type of attention, the system uses a mixed type of attention learning, i.e., a combination of spatial and channel attention. Our technique achieves state-of-the-art performance with the highest accuracy of 99%. The results show that the proposed system has performed well for both purposes and notably outperformed the traditional systems.
{"title":"A multi-crop disease identification approach based on residual attention learning","authors":"Kirti, N. Rajpal","doi":"10.1515/jisys-2022-0248","DOIUrl":"https://doi.org/10.1515/jisys-2022-0248","url":null,"abstract":"Abstract In this work, a technique is proposed to identify the diseases that occur in plants. The system is based on a combination of residual network and attention learning. The work focuses on disease identification from the images of four different plant types by analyzing leaf images of the plants. A total of four datasets are used for the work. The system incorporates attention-aware features computed by the Residual Attention Network (Res-ATTEN). The base of the network is ResNet-18 architecture. Integrating attention learning in the residual network helps improve the system's overall accuracy. Various residual attention units are combined to create a single architecture. Unlike the traditional attention network architectures, which focus only on a single type of attention, the system uses a mixed type of attention learning, i.e., a combination of spatial and channel attention. Our technique achieves state-of-the-art performance with the highest accuracy of 99%. The results show that the proposed system has performed well for both purposes and notably outperformed the traditional systems.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84122450","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}
Abstract Industrialization has advanced quickly, bringing intelligent production and manufacturing into people’s daily lives, but it has also created a number of issues with the ability of intelligent control systems for industrial robots. As a result, a study has been conducted on the use of multi-source data fusion methods in the mechanical industry. First, the research analyzes and discusses the existing research at home and abroad. Then, a robot intelligent control system based on multi-source fusion method is proposed, which combines multi-source data fusion with principal component analysis to better fuse data of multiple control periods; In the process, the experimental results are dynamically evaluated, and the performance of the proposed method is compared with other fusion methods. The results of the study showed that the confidence values and recognition correctness of the intelligent control system under the proposed method were superior compared to the Yu, Murphy, and Deng methods. Applying the method to the comparison of real-time and historical data values, it is found that the predicted data under the proposed method fits better with the actual data values, and the fit can be as high as 0.9945. The dynamic evaluation analysis of single and multi-factor in the simulation stage demonstrates that the control ability in the training samples of 0–100 is often better than the actual results, and the best evaluation results may be obtained at the sample size of 50 per batch. The aforementioned findings demonstrated that the multi-data fusion method that was suggested had a high degree of viability and accuracy for the intelligent control system of industrial robots and could offer a fresh line of enquiry for the advancement and development of the mechanical industrialization field.
{"title":"Intelligent control system for industrial robots based on multi-source data fusion","authors":"Yang Zhang","doi":"10.1515/jisys-2022-0286","DOIUrl":"https://doi.org/10.1515/jisys-2022-0286","url":null,"abstract":"Abstract Industrialization has advanced quickly, bringing intelligent production and manufacturing into people’s daily lives, but it has also created a number of issues with the ability of intelligent control systems for industrial robots. As a result, a study has been conducted on the use of multi-source data fusion methods in the mechanical industry. First, the research analyzes and discusses the existing research at home and abroad. Then, a robot intelligent control system based on multi-source fusion method is proposed, which combines multi-source data fusion with principal component analysis to better fuse data of multiple control periods; In the process, the experimental results are dynamically evaluated, and the performance of the proposed method is compared with other fusion methods. The results of the study showed that the confidence values and recognition correctness of the intelligent control system under the proposed method were superior compared to the Yu, Murphy, and Deng methods. Applying the method to the comparison of real-time and historical data values, it is found that the predicted data under the proposed method fits better with the actual data values, and the fit can be as high as 0.9945. The dynamic evaluation analysis of single and multi-factor in the simulation stage demonstrates that the control ability in the training samples of 0–100 is often better than the actual results, and the best evaluation results may be obtained at the sample size of 50 per batch. The aforementioned findings demonstrated that the multi-data fusion method that was suggested had a high degree of viability and accuracy for the intelligent control system of industrial robots and could offer a fresh line of enquiry for the advancement and development of the mechanical industrialization field.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"22 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74451422","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}
Abstract Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.
{"title":"Deep learning for content-based image retrieval in FHE algorithms","authors":"Sura Mahmood Abdullah, Mustafa Musa Jaber","doi":"10.1515/jisys-2022-0222","DOIUrl":"https://doi.org/10.1515/jisys-2022-0222","url":null,"abstract":"Abstract Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"21 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81845440","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}
Firas Mohammed Aswad, Ali Ahmed, N. A. M. Alhammadi, Bashar Ahmad Khalaf, S. Mostafa
Abstract With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.
{"title":"Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks","authors":"Firas Mohammed Aswad, Ali Ahmed, N. A. M. Alhammadi, Bashar Ahmad Khalaf, S. Mostafa","doi":"10.1515/jisys-2022-0155","DOIUrl":"https://doi.org/10.1515/jisys-2022-0155","url":null,"abstract":"Abstract With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"30 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82967479","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：