Pub Date : 2023-01-01DOI: 10.12720/jait.14.4.821-829
Ruchi Sharma, P. Shrinath
—There has been an exponential increase in usage of social informatics in recent years. This makes opinion mining more complex, especially for unstructured data available online. Although a substantial amount of research has been conducted on the COVID pandemic, post-pandemic research is lacking. Our research focuses on design and implementation of opinion mining framework for unstructured data input for business intelligence dealing with post pandemic work environment in industries. In this paper, we implement opinion mining algorithm in combination with machine learning approaches providing a hybrid approach. Transformer architecture Bidirectional Encoder Representations from Transformers language model is implemented to obtain sentence level feature vector of the document corpus and t-distributed stochastic neighbor embedding is implemented for clustering experimental evaluation. In this work, performance evaluation is undertaken using the Intertopic Distance map. By applying a hybrid strategy of natural language processing and machine learning, the results of this study indicate efficient framework development and anticipated to contribute to the improvement of efficacy of opinion mining models compared to existing approaches. This research is significant and will benefit businesses in gaining valuable insights that will lead to improved decision-making and business insights.
{"title":"Improved Opinion Mining for Unstructured Data Using Machine Learning Enabling Business Intelligence","authors":"Ruchi Sharma, P. Shrinath","doi":"10.12720/jait.14.4.821-829","DOIUrl":"https://doi.org/10.12720/jait.14.4.821-829","url":null,"abstract":"—There has been an exponential increase in usage of social informatics in recent years. This makes opinion mining more complex, especially for unstructured data available online. Although a substantial amount of research has been conducted on the COVID pandemic, post-pandemic research is lacking. Our research focuses on design and implementation of opinion mining framework for unstructured data input for business intelligence dealing with post pandemic work environment in industries. In this paper, we implement opinion mining algorithm in combination with machine learning approaches providing a hybrid approach. Transformer architecture Bidirectional Encoder Representations from Transformers language model is implemented to obtain sentence level feature vector of the document corpus and t-distributed stochastic neighbor embedding is implemented for clustering experimental evaluation. In this work, performance evaluation is undertaken using the Intertopic Distance map. By applying a hybrid strategy of natural language processing and machine learning, the results of this study indicate efficient framework development and anticipated to contribute to the improvement of efficacy of opinion mining models compared to existing approaches. This research is significant and will benefit businesses in gaining valuable insights that will lead to improved decision-making and business insights.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66333622","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 : 2023-01-01DOI: 10.12720/jait.14.6.1151-1158
Abdellah El Zaar, Rachida Assawab, Ayoub Aoulalay, Nabil Benaya, Toufik Bakir, Smain Femmam, Abderrahim El Allati
—Artificial Intelligence and Deep Learning applications are well-developed as a part of human life. In the field of object recognition, Convolutional Neural Network (CNN) based methods are getting more and more important and challenging. However, existing CNN methods do not perform well on datasets that exhibit high similarities, resulting in confusion between different classes. In this study, we propose a new Deep Learning approach for recognizing date fruit categories based on the Deep Convolutional Neural Network (DCNN). The modified fine-tuning (MFTs-Net) approach can recognize with high accuracy the different date fruit categories. In order to train and to test the robustness of our proposed method, we have collected a dataset that takes into account different date fruit categories. The presented dataset is challenging as it contains classes of a unique object and presents high similarities concerning the shape, color and texture of date fruit. We show that the MFTs-Net CNN we implemented, trained and tested using the collected dataset can recognize with high accuracy the different date categories compared with state-of-the-arts works. The presented methodology works perfectly with very small datasets, which is one of the main strengths of the proposed method. Our MFTs-Net architecture performs perfectly on test data with an accuracy of 98%. 1
{"title":"MFTs-Net: A Deep Learning Approach for High Similarity Date Fruit Recognition","authors":"Abdellah El Zaar, Rachida Assawab, Ayoub Aoulalay, Nabil Benaya, Toufik Bakir, Smain Femmam, Abderrahim El Allati","doi":"10.12720/jait.14.6.1151-1158","DOIUrl":"https://doi.org/10.12720/jait.14.6.1151-1158","url":null,"abstract":"—Artificial Intelligence and Deep Learning applications are well-developed as a part of human life. In the field of object recognition, Convolutional Neural Network (CNN) based methods are getting more and more important and challenging. However, existing CNN methods do not perform well on datasets that exhibit high similarities, resulting in confusion between different classes. In this study, we propose a new Deep Learning approach for recognizing date fruit categories based on the Deep Convolutional Neural Network (DCNN). The modified fine-tuning (MFTs-Net) approach can recognize with high accuracy the different date fruit categories. In order to train and to test the robustness of our proposed method, we have collected a dataset that takes into account different date fruit categories. The presented dataset is challenging as it contains classes of a unique object and presents high similarities concerning the shape, color and texture of date fruit. We show that the MFTs-Net CNN we implemented, trained and tested using the collected dataset can recognize with high accuracy the different date categories compared with state-of-the-arts works. The presented methodology works perfectly with very small datasets, which is one of the main strengths of the proposed method. Our MFTs-Net architecture performs perfectly on test data with an accuracy of 98%. 1","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"46 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":"135610240","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 : 2023-01-01DOI: 10.12720/jait.14.5.876-882
Yota Kurokawa, Masaru Fukushi
—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.
{"title":"A Simple and Effective Evaluation Method for Fault-Tolerant Routing Methods in Network-on-Chips","authors":"Yota Kurokawa, Masaru Fukushi","doi":"10.12720/jait.14.5.876-882","DOIUrl":"https://doi.org/10.12720/jait.14.5.876-882","url":null,"abstract":"—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"3 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":"135649055","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 : 2023-01-01DOI: 10.12720/jait.14.1.94-101
Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh
Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.
{"title":"An Automated Deep Learning Framework for Human Identity and Gender Detection","authors":"Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh","doi":"10.12720/jait.14.1.94-101","DOIUrl":"https://doi.org/10.12720/jait.14.1.94-101","url":null,"abstract":"Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329821","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 : 2023-01-01DOI: 10.12720/jait.14.1.77-84
Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan
Many types of digital signature schemes have been researched and published in recent years. In this paper, we propose two new types of collective signature schemes, namely i) the collective signature for several signing groups and ii) the collective signature for several individual signings and several signing groups. And then we used two difficult problems factoring and discrete logarithm to construct these schemes. To create a combination of these two difficult problems we use the prime module p with a special structure: p = Nn + 1 with n = rq, N is an even number, r and q are prime numbers of at least 512 bit. Schnorr’s digital signature scheme and the RSA key generation algorithm are used to construct related basic schemes such as the single signature scheme, the collective signature scheme, and the group signature scheme. The proposed collective signature schemes are built from these basic schemes. The correctness, security level and performance of the proposed schemes have also been presented in this paper.
近年来，人们研究和发表了许多类型的数字签名方案。本文提出了两种新的集体签名方案，即i)多个签名组的集体签名方案和ii)多个个人签名和多个签名组的集体签名方案。然后我们用两个难题分解和离散对数来构造这些格式。为了创建这两个难题的组合，我们使用具有特殊结构的素数模块p: p = Nn + 1, n = rq, n是偶数，r和q是至少512位的素数。使用Schnorr的数字签名方案和RSA密钥生成算法构建了相关的基本方案，如单个签名方案、集体签名方案和组签名方案。提出的集体签名方案是在这些基本方案的基础上构建的。本文还介绍了所提方案的正确性、安全性和性能。
{"title":"The New Collective Signature Schemes Based on Two Hard Problems Using Schnorr's Signature Standard","authors":"Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan","doi":"10.12720/jait.14.1.77-84","DOIUrl":"https://doi.org/10.12720/jait.14.1.77-84","url":null,"abstract":"Many types of digital signature schemes have been researched and published in recent years. In this paper, we propose two new types of collective signature schemes, namely i) the collective signature for several signing groups and ii) the collective signature for several individual signings and several signing groups. And then we used two difficult problems factoring and discrete logarithm to construct these schemes. To create a combination of these two difficult problems we use the prime module p with a special structure: p = Nn + 1 with n = rq, N is an even number, r and q are prime numbers of at least 512 bit. Schnorr’s digital signature scheme and the RSA key generation algorithm are used to construct related basic schemes such as the single signature scheme, the collective signature scheme, and the group signature scheme. The proposed collective signature schemes are built from these basic schemes. The correctness, security level and performance of the proposed schemes have also been presented in this paper.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330044","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 : 2023-01-01DOI: 10.12720/jait.14.2.178-184
Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid
—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.
{"title":"Comparison of Machine Learning Algorithms for Spam Detection","authors":"Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid","doi":"10.12720/jait.14.2.178-184","DOIUrl":"https://doi.org/10.12720/jait.14.2.178-184","url":null,"abstract":"—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330518","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 : 2023-01-01DOI: 10.12720/jait.14.3.463-471
T. S. Hlalele, Yanxia Sun, Zenghui Wang
— The incorporation of distributed energy resources in the distribution networks changes the fault current level and makes the fault detection be more complex. There are several challenges brought by these heterogenous energy systems including power quality, voltage stability, reliability and protection. In this paper, a fault detection based on reinforcement learning approach is proposed. The heart of this approach is a Q learning approach which uses a non-adaptive multi-agent reinforcement learning algorithm to detect and identify nonlinear system faults, and the algorithm learns the policy by telling an agent what actions to take under what circumstances. Moreover, the Discrete Wavelet Transform (DWT) is utilized to extract coefficient values from the captured one-fourth cycle of the three-phase current signal during fault which occurs during the transient stage. The simulations and signal analysis for different faults are used to validate the proposed fault detection method in MATLAB environment. The simulation results show that different types of faults such as CA, AB, ABC and ABCG can be detected and the best correlation coefficient achieved is 0.87851.
{"title":"Intelligent Fault Detection Based on Reinforcement Learning Technique on Distribution Networks","authors":"T. S. Hlalele, Yanxia Sun, Zenghui Wang","doi":"10.12720/jait.14.3.463-471","DOIUrl":"https://doi.org/10.12720/jait.14.3.463-471","url":null,"abstract":"— The incorporation of distributed energy resources in the distribution networks changes the fault current level and makes the fault detection be more complex. There are several challenges brought by these heterogenous energy systems including power quality, voltage stability, reliability and protection. In this paper, a fault detection based on reinforcement learning approach is proposed. The heart of this approach is a Q learning approach which uses a non-adaptive multi-agent reinforcement learning algorithm to detect and identify nonlinear system faults, and the algorithm learns the policy by telling an agent what actions to take under what circumstances. Moreover, the Discrete Wavelet Transform (DWT) is utilized to extract coefficient values from the captured one-fourth cycle of the three-phase current signal during fault which occurs during the transient stage. The simulations and signal analysis for different faults are used to validate the proposed fault detection method in MATLAB environment. The simulation results show that different types of faults such as CA, AB, ABC and ABCG can be detected and the best correlation coefficient achieved is 0.87851.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"39 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331130","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 : 2023-01-01DOI: 10.12720/jait.14.3.444-453
Foram Suthar, Nimisha Patel
—The internet is an obvious target for a cyberattack nowadays. The population on the internet globally is increasing from 3 billion in 2014 to 4.5 billion in 2020, resulting into nearly 59% of the total world population. The attacker is always looking for loopholes and vulnerabilities of internet-connected devices. It has been noticed from the last decade, there are more Denial-of-Service Attack (DoS) or DoS attacks and their variant Distributed Denial-of-Service (DDoS) or DDoS attacks performed by the attacker. This creates a serious problem for the network administrator to secure the infrastructure. The attacker mainly targets reputed organization/ industries and try to violate the major parameter of cyber security— Availability. The most commonly performed attack by the attacker is a Transmission Control Protocol (TCP) Synonym (SYN) DDoS attack, caused due to the design issue of the TCP algorithm. The attacker floods the packets in the network causing the server to crash. Hence, it is important to understand the source of the DDoS attack. Therefore, a real-life and accurate TCP SYN detection mechanism is required. Numerous techniques have been used for preventing and detecting various DDoS flooding attacks, some of which are covered in the literature review. The paper highlights the strengths and weaknesses of the available defense mechanism. To understand the performance status of the system we have implemented a DoS by the hping3 tool. This gives us better clarity in shortlisting and analyzing the parameters for the detection of DDoS attacks. Also, we try to analyze the impact of TCP SYN attack on the network in DDoS attacks.
互联网是当今网络攻击的明显目标。全球互联网人口将从2014年的30亿增加到2020年的45亿，占世界总人口的近59%。攻击者总是在寻找联网设备的漏洞和漏洞。从过去的十年中已经注意到，有更多的拒绝服务攻击(DoS)或DoS攻击及其变体分布式拒绝服务(DDoS)或DDoS攻击由攻击者执行。这给网络管理员保护基础设施带来了严重的问题。攻击者主要针对知名组织/行业，并试图破坏网络安全的主要参数-可用性。攻击者最常见的攻击是TCP (Transmission Control Protocol) SYN (Transmission Control Protocol Synonym) DDoS攻击，这是由于TCP算法的设计问题造成的。攻击者使报文在网络中泛滥，导致服务器崩溃。因此，了解DDoS攻击的来源非常重要。因此，需要一种真实、准确的TCP SYN检测机制。许多技术已被用于预防和检测各种DDoS洪水攻击，其中一些在文献综述中有介绍。本文重点分析了现有防御机制的优缺点。为了了解系统的性能状况，我们通过hping3工具实现了一个DoS。这使我们更清楚地列出和分析检测DDoS攻击的参数。同时，我们尝试分析TCP SYN攻击在DDoS攻击中对网络的影响。
{"title":"A Survey on DDoS Detection and Prevention Mechanism","authors":"Foram Suthar, Nimisha Patel","doi":"10.12720/jait.14.3.444-453","DOIUrl":"https://doi.org/10.12720/jait.14.3.444-453","url":null,"abstract":"—The internet is an obvious target for a cyberattack nowadays. The population on the internet globally is increasing from 3 billion in 2014 to 4.5 billion in 2020, resulting into nearly 59% of the total world population. The attacker is always looking for loopholes and vulnerabilities of internet-connected devices. It has been noticed from the last decade, there are more Denial-of-Service Attack (DoS) or DoS attacks and their variant Distributed Denial-of-Service (DDoS) or DDoS attacks performed by the attacker. This creates a serious problem for the network administrator to secure the infrastructure. The attacker mainly targets reputed organization/ industries and try to violate the major parameter of cyber security— Availability. The most commonly performed attack by the attacker is a Transmission Control Protocol (TCP) Synonym (SYN) DDoS attack, caused due to the design issue of the TCP algorithm. The attacker floods the packets in the network causing the server to crash. Hence, it is important to understand the source of the DDoS attack. Therefore, a real-life and accurate TCP SYN detection mechanism is required. Numerous techniques have been used for preventing and detecting various DDoS flooding attacks, some of which are covered in the literature review. The paper highlights the strengths and weaknesses of the available defense mechanism. To understand the performance status of the system we have implemented a DoS by the hping3 tool. This gives us better clarity in shortlisting and analyzing the parameters for the detection of DDoS attacks. Also, we try to analyze the impact of TCP SYN attack on the network in DDoS attacks.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331376","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 : 2023-01-01DOI: 10.12720/jait.14.3.559-570
Samin Ahsan Tausif, Aysha Gazi Mouri, Ishfaq Rahman, Nilufar Hossain, H. M. Z. Haque
—The expanded use of smartphones and the Internet of Things have enabled the usage of mobile crowdsensing technologies to improve public health care in clinical sciences. Mobile crowdsensing enlightens a new sensing pattern that can reliably differentiate individuals based on their cognitive fitness. In previous studies on this domain, the visual correlation has not been illustrated between physiological functions and the mental fitness of human beings. Therefore, there exists potential gaps in providing mathematical evidence of correlation between physical activities & cognitive health. Moreover, empirical analysis of autonomous smartphone sensing to assess mental health is yet to be researched on a large scale, showing the correspondence between ubiquitous mobile sensors data and Patient Health Questionnaire-9 (PHQ-9) depression scales. This research systematically collects mobile sensors’ data along with standard PHQ-9 questionnaire data and utilizes traditional machine learning techniques (Supervised and Unsupervised) for performing necessary analysis. Moreover, we have conducted statistical t-tests to find similarities or to differentiate between people of distinct cognitive fitness levels. This research has successfully demonstrated the numerical evidence of correlations between physiological activities and the cognitive fitness of human beings. The Fine-tuned regression models built for the purpose of predicting users’ cognitive fitness score, perform accurately to a certain extent. In this analysis, crowdsensing is perceived to differentiate several people’s cognitive fitness levels comprehensively. Furthermore, our study has addressed a significant insights to assessing people’s mental fitness by relying upon their smartphone usage.
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Pub Date : 2023-01-01DOI: 10.12720/jait.14.3.606-615
Fatoumata Wongbé Rosalie Tokpa, B. H. Kamagaté, Vincent Monsan, S. Oumtanaga
— Social media refers to communication channels on Internet that enable the creation and publication of content generated by the user and interaction between users. Given the accessibility to these means of communication and their rapidity, people resort more to them comparatively to the traditional media including radio, television and newspapers. However, dubious pieces of information such as fake news are often disseminated for malicious purposes. The proliferation of fake news has a strong negative impact on a society such as damage to the reputation of a personality, an organization or the aggravation of conflicts between its members. Due to the proliferation of fake news on these websites, the notion of veracity of information becomes a crucial issue. Research based on machine learning is promising. However, one of the main limitations is the efficiency of predictions. As a solution to detect fake news, we have proposed two models based on hybrid deep learning and evaluated our models on the two real datasets, namely ISOT and FA-KES. An experience of the proposed models to detect fake news, allowed to obtain on ISOT an accuracy of 99% for both models and on FA-KES, we obtain an accuracy of 68% for one the models and an accuracy of 63% for other. Other experiments in generalizing models on these data sets have proposed. The results obtained are better than other machine learning models.
- Social media是指在互联网上实现用户生成内容的创作和发布以及用户之间互动的传播渠道。考虑到这些传播手段的可及性和快速性，与传统媒体包括广播、电视和报纸相比，人们更多地使用它们。然而，虚假新闻等可疑信息往往出于恶意目的而传播。假新闻的泛滥对一个社会有很强的负面影响，比如损害一个人的声誉，一个组织或其成员之间的冲突加剧。由于这些网站上假新闻的泛滥，信息真实性的概念成为一个至关重要的问题。基于机器学习的研究很有前途。然而，主要的限制之一是预测的效率。作为假新闻检测的解决方案，我们提出了两个基于混合深度学习的模型，并在两个真实数据集ISOT和FA-KES上对我们的模型进行了评估。所提出的模型检测假新闻的经验，允许在ISOT和FA-KES上获得99%的准确率，我们获得一个模型的准确率为68%，另一个模型的准确率为63%。已经提出了在这些数据集上推广模型的其他实验。所得结果优于其他机器学习模型。
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