Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008268
Wadha Almattar, Atheer Algherairy
Convolutional neural network (CNN) plays a significant role with different tasks in computer vision task, specifically with medical imaging. Most of computer vision tasks achieved superior performance by utilizing CNN-based models in a supervised manner. However, Deep Learning (DL) models learn better with larger dataset which is sometimes hard in most of fields especially when it comes to medical imaging with rare diseases. To overcome data shortage, data augmentation is a solution to increase the amount of data used for training a DL model. This work investigates three commonly used augmentation techniques: horizontal flip, rotation and shearing. Experiments were conducted with three different scenarios using different training data. First scenario, the model is trained with original data without augmentation. Second scenario called mixed method where the training data are half augmented and half original. Third scenario called all method where whole training data augmented with one of the selected augmentation technique. As result, six training-sets were prepared. In this study Covid-19 X-ray images was used as case study. ResNet50 pre-trained architecture was used for a classification task on chest X-ray images to classify them into “Covid-19” or “normal”. The results show that using mixed method is better than all method. Moreover, the horizontal flip technique shows the highest score in the model performance among other techniques.
{"title":"Investigating Image Augmentation for Classification of Chest X-Ray Images","authors":"Wadha Almattar, Atheer Algherairy","doi":"10.1109/CICN56167.2022.10008268","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008268","url":null,"abstract":"Convolutional neural network (CNN) plays a significant role with different tasks in computer vision task, specifically with medical imaging. Most of computer vision tasks achieved superior performance by utilizing CNN-based models in a supervised manner. However, Deep Learning (DL) models learn better with larger dataset which is sometimes hard in most of fields especially when it comes to medical imaging with rare diseases. To overcome data shortage, data augmentation is a solution to increase the amount of data used for training a DL model. This work investigates three commonly used augmentation techniques: horizontal flip, rotation and shearing. Experiments were conducted with three different scenarios using different training data. First scenario, the model is trained with original data without augmentation. Second scenario called mixed method where the training data are half augmented and half original. Third scenario called all method where whole training data augmented with one of the selected augmentation technique. As result, six training-sets were prepared. In this study Covid-19 X-ray images was used as case study. ResNet50 pre-trained architecture was used for a classification task on chest X-ray images to classify them into “Covid-19” or “normal”. The results show that using mixed method is better than all method. Moreover, the horizontal flip technique shows the highest score in the model performance among other techniques.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701721","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008337
Reem Emad Nafiaa, A. Z. Yonis
Wireless Power Transfer (WPT) is a technology which is become an important topic nowadays due to many advantages that have, from the ease of use, safe, reliability, no need wires, and so on, and many scientists are trying to develop this technology to be used for more new smartphone devices, also WPT is considered one of green technology. In this research paper, a wireless power transfer system for the mobile battery charger had been designed and discussed using Mat lab program to get a 10 Watt to charge a mobile device with acceptable distance and efficiency. There are three methods for WPT includes electromagnetic induction (EI), magnetic resonance coupling (MRC), and radio waves (RW) which are categorized depending on the distance that sends the power. Magnetic resonance coupling is the method that has been designed is used for short and medium distances. In the result, the effect of distance system performance has been discussed.
{"title":"Performance Analysis of High-Efficiency WPT for Communication Technologies","authors":"Reem Emad Nafiaa, A. Z. Yonis","doi":"10.1109/CICN56167.2022.10008337","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008337","url":null,"abstract":"Wireless Power Transfer (WPT) is a technology which is become an important topic nowadays due to many advantages that have, from the ease of use, safe, reliability, no need wires, and so on, and many scientists are trying to develop this technology to be used for more new smartphone devices, also WPT is considered one of green technology. In this research paper, a wireless power transfer system for the mobile battery charger had been designed and discussed using Mat lab program to get a 10 Watt to charge a mobile device with acceptable distance and efficiency. There are three methods for WPT includes electromagnetic induction (EI), magnetic resonance coupling (MRC), and radio waves (RW) which are categorized depending on the distance that sends the power. Magnetic resonance coupling is the method that has been designed is used for short and medium distances. In the result, the effect of distance system performance has been discussed.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738675","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008349
Lara Alotaibi, Maria Alabdulrahman, Ahmed Abul Hasanaath, Salahudean B. Tohmeh, Nazeeruddin Mohammad
Virtual Reality (VR) is a subset of computer graphics where computers generate simulated environments. VR allows users to experience a virtual three-dimensional world where scenes and objects seem real. Wearable human machine interfaces (HMI) like helmets, gloves, etc are essential in making the experience feel realistic and interactive. Gloves in particular stimulate the sense of touch, which in some ways, is equally as important as visual stimulation. Developing a glove is a multi-faceted problem. Accurately tracking hand movement and delivering contextually appropriate feedback to the user in response to the events in the VR environment are the two main challenges. This paper proposes a scalable and low-cost haptic VR glove design that can accurately track and convey hand movement and finger flexion data. The design also incorporates the haptic primary colors (HPC), i.e., the design can stimulate the full gamut of touch senses, namely force, vibration, and temperature. The design makes use of flex sensors to track finger flexion. Inertial measurement units (IMUs) were used to track hand movement and rotation. A Bluetooth module communicates with a microcontroller connected to the VR environment. A vibro-thermal unit delivers the sense of vibration as well as temperature change. Servo motors were used to restrict finger movement in order to simulate force feedback. A prototype was built which was based on the proposed design. The prototype was tested in an integrated game development environment. It accurately emulated hand movement and reliably delivered a sense of touch by means of vibration and force feedback from the dedicated motors.
{"title":"Low Cost and Scalable Haptic VR Glove","authors":"Lara Alotaibi, Maria Alabdulrahman, Ahmed Abul Hasanaath, Salahudean B. Tohmeh, Nazeeruddin Mohammad","doi":"10.1109/CICN56167.2022.10008349","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008349","url":null,"abstract":"Virtual Reality (VR) is a subset of computer graphics where computers generate simulated environments. VR allows users to experience a virtual three-dimensional world where scenes and objects seem real. Wearable human machine interfaces (HMI) like helmets, gloves, etc are essential in making the experience feel realistic and interactive. Gloves in particular stimulate the sense of touch, which in some ways, is equally as important as visual stimulation. Developing a glove is a multi-faceted problem. Accurately tracking hand movement and delivering contextually appropriate feedback to the user in response to the events in the VR environment are the two main challenges. This paper proposes a scalable and low-cost haptic VR glove design that can accurately track and convey hand movement and finger flexion data. The design also incorporates the haptic primary colors (HPC), i.e., the design can stimulate the full gamut of touch senses, namely force, vibration, and temperature. The design makes use of flex sensors to track finger flexion. Inertial measurement units (IMUs) were used to track hand movement and rotation. A Bluetooth module communicates with a microcontroller connected to the VR environment. A vibro-thermal unit delivers the sense of vibration as well as temperature change. Servo motors were used to restrict finger movement in order to simulate force feedback. A prototype was built which was based on the proposed design. The prototype was tested in an integrated game development environment. It accurately emulated hand movement and reliably delivered a sense of touch by means of vibration and force feedback from the dedicated motors.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129716204","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008244
A. V. Rao, S. Vishwakarma, Shakti Kundu
Coloring grayscale photos manually or using traditional coloring methods takes extensive user interaction. This may involve applying many colored scribbles, viewing related images, or doing segmentation. Even the most sophisticated software available in this day and age can take up to a month to color an image that was originally black and white. This occurs because the image contains a wide variety of color tones and tints. In this research work, we offer an innovative method for colorizing grayscale photographs that use deep learning techniques. First, we can separate the subject matter and aesthetic of several images and then recombine them into a single image by using a pre-trained convolutional neural network that was first developed for image categorization. Following this, we present an approach that may colorize a black-and-white image by combining the content of the black-and-white image with the style of a color image that has semantic similarities with the black-and-white image.
{"title":"Artificial Intelligent approach for Colorful Image Colorization Using a DCNN","authors":"A. V. Rao, S. Vishwakarma, Shakti Kundu","doi":"10.1109/CICN56167.2022.10008244","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008244","url":null,"abstract":"Coloring grayscale photos manually or using traditional coloring methods takes extensive user interaction. This may involve applying many colored scribbles, viewing related images, or doing segmentation. Even the most sophisticated software available in this day and age can take up to a month to color an image that was originally black and white. This occurs because the image contains a wide variety of color tones and tints. In this research work, we offer an innovative method for colorizing grayscale photographs that use deep learning techniques. First, we can separate the subject matter and aesthetic of several images and then recombine them into a single image by using a pre-trained convolutional neural network that was first developed for image categorization. Following this, we present an approach that may colorize a black-and-white image by combining the content of the black-and-white image with the style of a color image that has semantic similarities with the black-and-white image.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130419910","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}
Pub Date : 2022-12-04DOI: 10.1109/cicn56167.2022.10008336
{"title":"Welcome from CICN 2022 General Chair","authors":"","doi":"10.1109/cicn56167.2022.10008336","DOIUrl":"https://doi.org/10.1109/cicn56167.2022.10008336","url":null,"abstract":"","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"695 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126899007","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008361
Wengyao Jiang, Chen Xuan
Deep learning and machine learning are immensely prevalent and highly interactive in a myriad of fields, typically neural networks is widely used in mathematics. We outline a technique for employing artificial neural networks (ANN) to solve ordinary differential equations. For better illustration, we present the basic logic and formula of ANN and gradient computation, following with one typical first order differential equation as example. In order to research the flexibility and feasibility of our model, we compare several hyperparameters and different optimizer using control variable method. Finally, our neural networks model is applied into the second order differential equations with innovative modification by analogy. In this article, we illustrate the relatively novel method to solve the ordinary differential equations and examine our model through adjustable parameters, then convert into the second order which shows a wide application range.
{"title":"Neural Network for Solving Ordinary Differential Equations","authors":"Wengyao Jiang, Chen Xuan","doi":"10.1109/CICN56167.2022.10008361","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008361","url":null,"abstract":"Deep learning and machine learning are immensely prevalent and highly interactive in a myriad of fields, typically neural networks is widely used in mathematics. We outline a technique for employing artificial neural networks (ANN) to solve ordinary differential equations. For better illustration, we present the basic logic and formula of ANN and gradient computation, following with one typical first order differential equation as example. In order to research the flexibility and feasibility of our model, we compare several hyperparameters and different optimizer using control variable method. Finally, our neural networks model is applied into the second order differential equations with innovative modification by analogy. In this article, we illustrate the relatively novel method to solve the ordinary differential equations and examine our model through adjustable parameters, then convert into the second order which shows a wide application range.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126009136","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}
Summary - Currently in different organizations and public entities, have large amounts of data and many times the tools or technologies that achieve the analysis of such data are unknown. in this context, business intelligence emerged, which allows data to be analyzed and provided information to supports decision- making for senior executives of public entities. The objective of this research is to propose a business intelligence architecture that provides a solution to manage large amount of information. The methodology used was based on scrum as part of a good organization, structure and time control by the implementation of the proposed architecture in any system or tool of the public entities. as part of the review and part of the results, the research of various articles was carried out, and 20 were selected for sample and proof that the proposed architecture is the one indicated for decision making. in the results of part of the research, was found evidence that a business intelligence architecture is not only the most used, but it provides various response mechanisms for the large amount of information possessed and, in many cases, not used in the best way.
{"title":"Business Intelligence Architecture to Improve Decision Making","authors":"Cristhian Villasante Moreno, Ciro Rodríguez, Favio Guevara Puente, Iván Petrlik, Pedro Lezama, Yuri Pomachagua","doi":"10.1109/CICN56167.2022.10008297","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008297","url":null,"abstract":"Summary - Currently in different organizations and public entities, have large amounts of data and many times the tools or technologies that achieve the analysis of such data are unknown. in this context, business intelligence emerged, which allows data to be analyzed and provided information to supports decision- making for senior executives of public entities. The objective of this research is to propose a business intelligence architecture that provides a solution to manage large amount of information. The methodology used was based on scrum as part of a good organization, structure and time control by the implementation of the proposed architecture in any system or tool of the public entities. as part of the review and part of the results, the research of various articles was carried out, and 20 were selected for sample and proof that the proposed architecture is the one indicated for decision making. in the results of part of the research, was found evidence that a business intelligence architecture is not only the most used, but it provides various response mechanisms for the large amount of information possessed and, in many cases, not used in the best way.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126340302","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008236
Fucheng Zhu, Fan Zhang, Yong Liu
In this paper, starting from the perspective of information service, the artificial intelligence, wisdom, archives as the research object, by using the method of literature research and Internet research focuses on the artificial intelligence in the wisdom of the construction of archives, mining analysis existing wisdom archives construction cases at home and abroad, from theory to practice, the wisdom of the comprehensive analysis based on artificial intelligence archives construction facing the opportunities and challenges. It is concluded that the application of artificial intelligence technology makes it more convenient for users to consult archives, and also improves the efficiency of archivists.
{"title":"Intelligent Archive Construction Driven by Artificial Intelligence","authors":"Fucheng Zhu, Fan Zhang, Yong Liu","doi":"10.1109/CICN56167.2022.10008236","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008236","url":null,"abstract":"In this paper, starting from the perspective of information service, the artificial intelligence, wisdom, archives as the research object, by using the method of literature research and Internet research focuses on the artificial intelligence in the wisdom of the construction of archives, mining analysis existing wisdom archives construction cases at home and abroad, from theory to practice, the wisdom of the comprehensive analysis based on artificial intelligence archives construction facing the opportunities and challenges. It is concluded that the application of artificial intelligence technology makes it more convenient for users to consult archives, and also improves the efficiency of archivists.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841022","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008278
Priyanka Gupta, M. Dixit
Road pothole detection is essential to ensure any engineering structures' health. Manual pothole detection and classification is very human-intensive work. Several sensor-based techniques, laser imaging approaches, and image processing techniques have been deployed to less the intervention of humans in road inspections. Still, these approaches have some limitations, such as high cost, less accuracy, and risk during detection, as Machine learning-based approaches require manual feature extraction for the prediction. Therefore, this proposed work aims to use deep learning modes for better pothole detection results. Several pothole datasets are available online, and deep learning-based methods require lots of data for the training; therefore, pothole images are collected from the different datasets and combined into one dataset to train the model. Augmentation is also applied to the dataset for better training, as augmentation provides images with different angles, and by fine-tuning the model consequently, records with about 98 % accuracy.
{"title":"Image-based Road Pothole Detection using Deep Learning Model","authors":"Priyanka Gupta, M. Dixit","doi":"10.1109/CICN56167.2022.10008278","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008278","url":null,"abstract":"Road pothole detection is essential to ensure any engineering structures' health. Manual pothole detection and classification is very human-intensive work. Several sensor-based techniques, laser imaging approaches, and image processing techniques have been deployed to less the intervention of humans in road inspections. Still, these approaches have some limitations, such as high cost, less accuracy, and risk during detection, as Machine learning-based approaches require manual feature extraction for the prediction. Therefore, this proposed work aims to use deep learning modes for better pothole detection results. Several pothole datasets are available online, and deep learning-based methods require lots of data for the training; therefore, pothole images are collected from the different datasets and combined into one dataset to train the model. Augmentation is also applied to the dataset for better training, as augmentation provides images with different angles, and by fine-tuning the model consequently, records with about 98 % accuracy.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128012633","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008351
F. Alhaidari, Rawan Mushref Tammas, Dana Saeed Alghamdi, Reem Aied Alrashedi, Nora Adnan Althani, S. Alsaidan, Malak Alfosail, Rachid Zagrouba, Hussain Alattas
With technology evolving, cyberattacks are increasing massively. Therefore, companies and organizations are obliged to implement high-security measures to prevent, mitigate, and respond to such attacks. If a company faces a cyberattack, it should pass through the post-incident forensics analysis phase. This phase is a significant part of the investigation process since it provides valuable information on how the attack was conducted and where the vulnerability was, allowing the security team to patch it and learn how to defend against future attacks. For that reason, this paper aims to discuss a passive analysis of network traffic and review the current network traffic analysis tools and techniques, summarize, analyze, and compare them based on pre-defined criteria to find the literature gap to address it. The gap found after the analysis is that no tool suffices for all purposes of network traffic passive analysis, in terms of both detecting the presence of attacks as well as to visualizing the traffic flow.
{"title":"A study on Automated Cyberattacks Detection and Visualization","authors":"F. Alhaidari, Rawan Mushref Tammas, Dana Saeed Alghamdi, Reem Aied Alrashedi, Nora Adnan Althani, S. Alsaidan, Malak Alfosail, Rachid Zagrouba, Hussain Alattas","doi":"10.1109/CICN56167.2022.10008351","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008351","url":null,"abstract":"With technology evolving, cyberattacks are increasing massively. Therefore, companies and organizations are obliged to implement high-security measures to prevent, mitigate, and respond to such attacks. If a company faces a cyberattack, it should pass through the post-incident forensics analysis phase. This phase is a significant part of the investigation process since it provides valuable information on how the attack was conducted and where the vulnerability was, allowing the security team to patch it and learn how to defend against future attacks. For that reason, this paper aims to discuss a passive analysis of network traffic and review the current network traffic analysis tools and techniques, summarize, analyze, and compare them based on pre-defined criteria to find the literature gap to address it. The gap found after the analysis is that no tool suffices for all purposes of network traffic passive analysis, in terms of both detecting the presence of attacks as well as to visualizing the traffic flow.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116960933","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}
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