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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008255
Asrar Hussain Alderham, E. S. Jaha
A resume, in general, is a commonly and widely used way for a person to present their competence and qualifications. It is usually written in different personalized methods in a variety of inconsistent styles in various file formats (pdf, txt, doc, etc.). The process of selecting an appropriate candidate based on whether their resume matches a list of job requirements is usually a tedious, difficult, time-consuming, and effort-consuming task. This task is deemed significant for extracting relevant information and useful attributes that are indicative of good candidates. This study aims to assist human resource departments to improve the candidate career matching process in an automated and more efficient manner based on inferring and analyzing comparative semantic resume attributes using machine learning (ML) and natural language processing (NLP) tools. The ranking support vector machine (SVM) algorithm is then used to rank these resumes by attribute using semantic data comparisons. This produces a more accurate ranking able to detect the tiny differences between candidates and give more unique scores to get an enhanced list of candidates ranked from the best to worst match for the vacancy. The experimental results and performance comparison show that the proposed comparative ranking based on semantic descriptions surpasses the standard ranking based on mere regular scores in terms of a distinction between candidates and distribution of resumes across the ranks with accuracy up to 92%.
{"title":"Comparative Semantic Resume Analysis for Improving Candidate-Career Matching","authors":"Asrar Hussain Alderham, E. S. Jaha","doi":"10.1109/CICN56167.2022.10008255","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008255","url":null,"abstract":"A resume, in general, is a commonly and widely used way for a person to present their competence and qualifications. It is usually written in different personalized methods in a variety of inconsistent styles in various file formats (pdf, txt, doc, etc.). The process of selecting an appropriate candidate based on whether their resume matches a list of job requirements is usually a tedious, difficult, time-consuming, and effort-consuming task. This task is deemed significant for extracting relevant information and useful attributes that are indicative of good candidates. This study aims to assist human resource departments to improve the candidate career matching process in an automated and more efficient manner based on inferring and analyzing comparative semantic resume attributes using machine learning (ML) and natural language processing (NLP) tools. The ranking support vector machine (SVM) algorithm is then used to rank these resumes by attribute using semantic data comparisons. This produces a more accurate ranking able to detect the tiny differences between candidates and give more unique scores to get an enhanced list of candidates ranked from the best to worst match for the vacancy. The experimental results and performance comparison show that the proposed comparative ranking based on semantic descriptions surpasses the standard ranking based on mere regular scores in terms of a distinction between candidates and distribution of resumes across the ranks with accuracy up to 92%.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"87 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":"124419301","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/CICN56167.2022.10008352
A. Munir, Zulfi, Rheyuniarto Sahlendar Asthan, F. Oktafiani
This paper presents the use of three-dimensional (3D) printing technology for rapid manufacturing a discone antenna. The advanced 3D printing technology can provide antenna prototypes rapidly manufactured in comparable time frames as conventional antenna prototypes. Here, the 3D printing technology is applied to manufacture the cone part of proposed discone antenna based on polylactic-acid (PLA) material, while the disc part is realized using a copper metal sheet. The proposed discone antenna which is intended as an electromagnetic interference (EMI) sensor is designed to produce a wideband frequency response of 700 MHz - 6000 MHz. The characterization result shows that the manufactured discone antenna has the operating bandwidth for -10 dB reflection coefficient of more than 5300 MHz with the lowest operating frequency of 698 MHz.
{"title":"3D Printing Technology for Rapid Manufacturing Discone Antenna Based on PLA Material","authors":"A. Munir, Zulfi, Rheyuniarto Sahlendar Asthan, F. Oktafiani","doi":"10.1109/CICN56167.2022.10008352","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008352","url":null,"abstract":"This paper presents the use of three-dimensional (3D) printing technology for rapid manufacturing a discone antenna. The advanced 3D printing technology can provide antenna prototypes rapidly manufactured in comparable time frames as conventional antenna prototypes. Here, the 3D printing technology is applied to manufacture the cone part of proposed discone antenna based on polylactic-acid (PLA) material, while the disc part is realized using a copper metal sheet. The proposed discone antenna which is intended as an electromagnetic interference (EMI) sensor is designed to produce a wideband frequency response of 700 MHz - 6000 MHz. The characterization result shows that the manufactured discone antenna has the operating bandwidth for -10 dB reflection coefficient of more than 5300 MHz with the lowest operating frequency of 698 MHz.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"26 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":"114891128","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/CICN56167.2022.10008337
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/CICN56167.2022.10008234
Premasudha B G, Thara D K, Tara K N
India's economy is heavily dependent on rising agricultural yields and agro-industry goods. In this paper, we explore various machine learning techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made. The outcome of the learning process is used by farmers for corrective measures for yield optimization. To anticipate the crop and to suggest fertilizer, also to detect plant disease, sophisticated models were devised and constructed for this proposed system. From a photograph of a leaf, an algorithm determines whether the plant is diseased or not. The Random Forest [RF] model provide suggestions for enhancing soil fertility and to recommend fertilizer depending on the soil's nutrient composition.
{"title":"ML based methods XGBoost and Random Forest for Crop and Fertilizer Prediction","authors":"Premasudha B G, Thara D K, Tara K N","doi":"10.1109/CICN56167.2022.10008234","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008234","url":null,"abstract":"India's economy is heavily dependent on rising agricultural yields and agro-industry goods. In this paper, we explore various machine learning techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made. The outcome of the learning process is used by farmers for corrective measures for yield optimization. To anticipate the crop and to suggest fertilizer, also to detect plant disease, sophisticated models were devised and constructed for this proposed system. From a photograph of a leaf, an algorithm determines whether the plant is diseased or not. The Random Forest [RF] model provide suggestions for enhancing soil fertility and to recommend fertilizer depending on the soil's nutrient composition.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"112 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":"117244478","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/CICN56167.2022.10008360
M. Aljabri, Maryam M. Aldossary, Noor Al-Homeed, Bushra Alhetelah, Malek Althubiany, Ohoud F. Alotaibi, Sara Alsaqer
In many different sorts of businesses, including public and private, government, critical infrastructures, etc., web apps have grown recently. Therefore, securing web applications is a major concern to protect businesses from loss and unauthorized access to sensitive information. Developers use vulnerable thirdparty modules or components or create programming security flaws themselves and occasionally work with tight budgets. These situations frequently cause people to overlook a crucial aspect of development life cycle security. This paper studies and tests the currently available web security and exploitation tools of OWASP's top ten security vulnerabilities. The main aim of this paper is to improve the detection of OWASP's top ten security vulnerabilities by proposing an exploitation and detection tool that combined features of the tools that has been tested in the paper.
{"title":"Testing and Exploiting Tools to Improve OWASP Top Ten Security Vulnerabilities Detection","authors":"M. Aljabri, Maryam M. Aldossary, Noor Al-Homeed, Bushra Alhetelah, Malek Althubiany, Ohoud F. Alotaibi, Sara Alsaqer","doi":"10.1109/CICN56167.2022.10008360","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008360","url":null,"abstract":"In many different sorts of businesses, including public and private, government, critical infrastructures, etc., web apps have grown recently. Therefore, securing web applications is a major concern to protect businesses from loss and unauthorized access to sensitive information. Developers use vulnerable thirdparty modules or components or create programming security flaws themselves and occasionally work with tight budgets. These situations frequently cause people to overlook a crucial aspect of development life cycle security. This paper studies and tests the currently available web security and exploitation tools of OWASP's top ten security vulnerabilities. The main aim of this paper is to improve the detection of OWASP's top ten security vulnerabilities by proposing an exploitation and detection tool that combined features of the tools that has been tested in the paper.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"51 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":"133230224","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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