Pub Date : 2023-01-01DOI: 10.12720/jait.14.4.701-717
O. Stitini, I. García-Magariño, S. Kaloun, O. Bencharef
.
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{"title":"Towards Ideal and Efficient Recommendation Systems Based on the Five Evaluation Concepts Promoting Serendipity","authors":"O. Stitini, I. García-Magariño, S. Kaloun, O. Bencharef","doi":"10.12720/jait.14.4.701-717","DOIUrl":"https://doi.org/10.12720/jait.14.4.701-717","url":null,"abstract":".","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66333392","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.14.4.830-837
Izzati Mohaimin, R. Apong, A. R. Damit
—As online information increases over the years, text mining researchers developed Natural Language Processing tools to extract relevant and useful information from textual data such as online news articles. The Malay language is widely spoken, especially in the Southeast Asian region, but there is a lack of Natural Language Processing (NLP) tools such as Malay corpora and Part-of-Speech (POS) taggers. Existing NLP tools are mainly based on Standard Malay of Malaysia and Indonesian language, but there is none for the Bruneian Malay. We addressed this issue by designing a Standard Brunei Malay corpus consisting of over 114,000 lexical tokens, annotated using 17 Malay POS tagsets. Furthermore, we implemented two commonly used POS tagging techniques, Conditional Random Field (CRF) and Bi-directional Long Short-Term Memory (BLSTM), to develop Bruneian POS taggers and compared their performances. The results showed that both CRF and BLSTM models performed well in predicting POS tags on Bruneian texts. However, CRF models outperform BLSTM, where CRF using all features achieved an F-Measure of 92.06% on news articles and 90.71% of F-Measure on crime articles. Adding a batch normalization layer to the BLSTM model architecture increased the performance by 7.13%. To further improve the BLSTM models, we suggested increasing the training data and experimenting with different hyperparameter settings. The findings also indicated that modelling BLSTM with fastText has improved the POS prediction of Bruneian words.
{"title":"Part-of-Speech (POS) Tagging for Standard Brunei Malay: A Probabilistic and Neural-Based Approach","authors":"Izzati Mohaimin, R. Apong, A. R. Damit","doi":"10.12720/jait.14.4.830-837","DOIUrl":"https://doi.org/10.12720/jait.14.4.830-837","url":null,"abstract":"—As online information increases over the years, text mining researchers developed Natural Language Processing tools to extract relevant and useful information from textual data such as online news articles. The Malay language is widely spoken, especially in the Southeast Asian region, but there is a lack of Natural Language Processing (NLP) tools such as Malay corpora and Part-of-Speech (POS) taggers. Existing NLP tools are mainly based on Standard Malay of Malaysia and Indonesian language, but there is none for the Bruneian Malay. We addressed this issue by designing a Standard Brunei Malay corpus consisting of over 114,000 lexical tokens, annotated using 17 Malay POS tagsets. Furthermore, we implemented two commonly used POS tagging techniques, Conditional Random Field (CRF) and Bi-directional Long Short-Term Memory (BLSTM), to develop Bruneian POS taggers and compared their performances. The results showed that both CRF and BLSTM models performed well in predicting POS tags on Bruneian texts. However, CRF models outperform BLSTM, where CRF using all features achieved an F-Measure of 92.06% on news articles and 90.71% of F-Measure on crime articles. Adding a batch normalization layer to the BLSTM model architecture increased the performance by 7.13%. To further improve the BLSTM models, we suggested increasing the training data and experimenting with different hyperparameter settings. The findings also indicated that modelling BLSTM with fastText has improved the POS prediction of Bruneian words.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66333721","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.14.5.980-990
Hajar Alla, Lahcen Moumoun, Youssef Balouki
—The basic objective of this study is to develop a model that analyzes and predicts the occurrence of flight arrival delays in the United States. Macroscopic and microscopic delay factors are discussed. In this research, we proposed new features that, to the best of our knowledge, were never used in previous studies, namely departure Part and Arrival Part of the day (Mornings, Afternoons, Evenings, Nights) and type of aircraft. U.S. domestic flight data for the year 2018, extracted from the Bureau of Transportation Statistics (BTS), were adopted in order to train the predictive model. We used efficient Machine Learning classifiers such as Decision Trees, K-Nearest Neighbors, Random Forest and Multilayer Perceptron. To overcome the issue of imbalanced data, sampling techniques were performed. We chose Grid Search technique for best parameters selection. The performance of each classifier was compared in terms of evaluation metrics, parameters tuning, data sampling and features selection. The experimental results showed that tuning and sampling techniques have successfully generated the best classifier which is Multilayer Perceptron (MLP) with an accuracy of 98.72% and a higher number of correctly classified flights.
{"title":"Towards Flight Delays Reduction: The Effect of Aircraft Type and Part of Day on Arrival Delays Prediction","authors":"Hajar Alla, Lahcen Moumoun, Youssef Balouki","doi":"10.12720/jait.14.5.980-990","DOIUrl":"https://doi.org/10.12720/jait.14.5.980-990","url":null,"abstract":"—The basic objective of this study is to develop a model that analyzes and predicts the occurrence of flight arrival delays in the United States. Macroscopic and microscopic delay factors are discussed. In this research, we proposed new features that, to the best of our knowledge, were never used in previous studies, namely departure Part and Arrival Part of the day (Mornings, Afternoons, Evenings, Nights) and type of aircraft. U.S. domestic flight data for the year 2018, extracted from the Bureau of Transportation Statistics (BTS), were adopted in order to train the predictive model. We used efficient Machine Learning classifiers such as Decision Trees, K-Nearest Neighbors, Random Forest and Multilayer Perceptron. To overcome the issue of imbalanced data, sampling techniques were performed. We chose Grid Search technique for best parameters selection. The performance of each classifier was compared in terms of evaluation metrics, parameters tuning, data sampling and features selection. The experimental results showed that tuning and sampling techniques have successfully generated the best classifier which is Multilayer Perceptron (MLP) with an accuracy of 98.72% and a higher number of correctly classified flights.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136202086","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.14.5.1117-1123
Nguyen Minh Quy, Abdellah Chehri, Pham Duc Khai, Dao Manh Linh, Dang Van Anh
.
{"title":"Performance Analysis of ERS Techniques for Next-Generation Opportunistic Networks","authors":"Nguyen Minh Quy, Abdellah Chehri, Pham Duc Khai, Dao Manh Linh, Dang Van Anh","doi":"10.12720/jait.14.5.1117-1123","DOIUrl":"https://doi.org/10.12720/jait.14.5.1117-1123","url":null,"abstract":".","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135211756","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.14.5.1096-1102
Abdulrahman Alshayea, Mohammad Ali H. Eljinini
—Denial of Service (DoS) attacks can cost online and web service providers money and damage their reputations. The lack of security protection in web services creates a vulnerability attackers can exploit. A new XDoS attack targeting web services has recently emerged, using XML rather than plain old HTML as the attack vector. This paper proposes a middleware tool for detecting and preventing web service XDoS and HTTP flooding attacks. A rule-based technique classifies requests as benign or malicious to detect XDoS attacks. According to the middleware tool’s trial findings, rule-based technology has successfully recognized and blocked XDoS and HTTP flooding assaults such as large payloads, forceful parsing, and external XML elements in near-real time, such as 0.006s across web services. Middleware protects web services from XDoS and distributed XDoS attacks by ensuring nearly 100% service availability for routine requests (DXDoS).
{"title":"Reducing the Effect of Denial of Service in Web Service Environment","authors":"Abdulrahman Alshayea, Mohammad Ali H. Eljinini","doi":"10.12720/jait.14.5.1096-1102","DOIUrl":"https://doi.org/10.12720/jait.14.5.1096-1102","url":null,"abstract":"—Denial of Service (DoS) attacks can cost online and web service providers money and damage their reputations. The lack of security protection in web services creates a vulnerability attackers can exploit. A new XDoS attack targeting web services has recently emerged, using XML rather than plain old HTML as the attack vector. This paper proposes a middleware tool for detecting and preventing web service XDoS and HTTP flooding attacks. A rule-based technique classifies requests as benign or malicious to detect XDoS attacks. According to the middleware tool’s trial findings, rule-based technology has successfully recognized and blocked XDoS and HTTP flooding assaults such as large payloads, forceful parsing, and external XML elements in near-real time, such as 0.006s across web services. Middleware protects web services from XDoS and distributed XDoS attacks by ensuring nearly 100% service availability for routine requests (DXDoS).","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135211758","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.14.5.928-933
Eric Blancaflor, Angelo Dominic D. Abat, Kyle Matthew A. Degrano, Ma. Cassandra M. Lindio, Andrei Daniel A. Pamoso
—Zero Trust Security is an architecture that, as the name implies, trusts no one. This type of architecture is used by several firms globally due to its robust security. The Zero Trust security implementation in the Philippines is very low and it shows by looking at the number of cyberattacks that Philippine companies experience. A prominent form of cyberattack is ransomware that endangers that sensitive information that most companies hold. Ransomware attacks are common, and this is where attackers would lock certain files and will only be unlocked when the victim would pay the appropriate ransom for the information. The Philippines has been deemed by international firms as a risky venture since the cybersecurity levels are low. There are also reports that major companies in the Philippines are victims of large-scale ransomware attacks. This study aims to give an in-depth explanation of Zero Trust and see its fundamental aspects that makes it a better option. This exploratory study considers the possible capabilities of the said architecture to combat ransomware in the context of the Philippines.
{"title":"Implementation of Zero Trust Security to Reduce Ransomware Attacks in the Philippines: A Literature Review","authors":"Eric Blancaflor, Angelo Dominic D. Abat, Kyle Matthew A. Degrano, Ma. Cassandra M. Lindio, Andrei Daniel A. Pamoso","doi":"10.12720/jait.14.5.928-933","DOIUrl":"https://doi.org/10.12720/jait.14.5.928-933","url":null,"abstract":"—Zero Trust Security is an architecture that, as the name implies, trusts no one. This type of architecture is used by several firms globally due to its robust security. The Zero Trust security implementation in the Philippines is very low and it shows by looking at the number of cyberattacks that Philippine companies experience. A prominent form of cyberattack is ransomware that endangers that sensitive information that most companies hold. Ransomware attacks are common, and this is where attackers would lock certain files and will only be unlocked when the victim would pay the appropriate ransom for the information. The Philippines has been deemed by international firms as a risky venture since the cybersecurity levels are low. There are also reports that major companies in the Philippines are victims of large-scale ransomware attacks. This study aims to give an in-depth explanation of Zero Trust and see its fundamental aspects that makes it a better option. This exploratory study considers the possible capabilities of the said architecture to combat ransomware in the context of the Philippines.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135649644","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.14.6.1169-1176
Kishanprasad G. Gunale, Prachi Mukherji, Sumitra N. Motade
.
{"title":"Convolutional Neural Network-Based Fall Detection for the Elderly Person Monitoring","authors":"Kishanprasad G. Gunale, Prachi Mukherji, Sumitra N. Motade","doi":"10.12720/jait.14.6.1169-1176","DOIUrl":"https://doi.org/10.12720/jait.14.6.1169-1176","url":null,"abstract":".","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135609318","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.14.5.1073-1081
Herry Sujaini, Samuel Cahyawijaya, Arif B. Putra
—Several previous studies have suggested using statistical machine translation instead of neural machine translation for extremely low-resource languages. We could translate texts from 12 different regional languages into Indonesian using machine translation experiments. We increased the accuracy of machine translation for 12 extremely low-resource languages by using several monolingual corpus sizes on the language model’s target side. Since many Indonesian sources are available, we added this corpus to improve the model’s performance. Our study aims to analyze and evaluate the impact of different language models trained on various monolingual corpus on the accuracy of machine translation. The increase in accuracy when enlarging the monolingual corpus is not observed every time, according to our experiments. Therefore, it is necessary to perform several experiments to determine the monolingual corpus to optimize the quality. Experiments showed that Melayu Pontianak achieved the highest bilingual evaluation understudy improvement point. Specifically, we found that by adding a monolingual corpus of 50–100K, they performed a bilingual evaluation understudy improvement point of 2.15, the highest improvement point they reached for any of the twelve languages tested.
{"title":"Analysis of Language Model Role in Improving Machine Translation Accuracy for Extremely Low Resource Languages","authors":"Herry Sujaini, Samuel Cahyawijaya, Arif B. Putra","doi":"10.12720/jait.14.5.1073-1081","DOIUrl":"https://doi.org/10.12720/jait.14.5.1073-1081","url":null,"abstract":"—Several previous studies have suggested using statistical machine translation instead of neural machine translation for extremely low-resource languages. We could translate texts from 12 different regional languages into Indonesian using machine translation experiments. We increased the accuracy of machine translation for 12 extremely low-resource languages by using several monolingual corpus sizes on the language model’s target side. Since many Indonesian sources are available, we added this corpus to improve the model’s performance. Our study aims to analyze and evaluate the impact of different language models trained on various monolingual corpus on the accuracy of machine translation. The increase in accuracy when enlarging the monolingual corpus is not observed every time, according to our experiments. Therefore, it is necessary to perform several experiments to determine the monolingual corpus to optimize the quality. Experiments showed that Melayu Pontianak achieved the highest bilingual evaluation understudy improvement point. Specifically, we found that by adding a monolingual corpus of 50–100K, they performed a bilingual evaluation understudy improvement point of 2.15, the highest improvement point they reached for any of the twelve languages tested.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135052618","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.14.1.20-25
E. Blancaflor, Eli Christ Paula C. Castillo, Jan Miguel N. Coretico, Geremie B. Rubiano, Angela Marie D. Tobias
Free Wi-Fi networks are widely implemented in public areas to provide benefits for the people. This paper focuses on the vulnerabilities around Wi-Fi networks that people are unaware of. Discussing the risks of using free internet is viable as public places in the Philippines have started implementing free Wi-Fi networks. Along with this, the Department of Information and Communication Technology (DICT) of the Philippines is expanding the number of free Wi-Fi to help the Filipinos adjust to the new normal caused by the pandemic. With the increase of internet access, Filipinos should be informed of the risks they may have. In this study, the security of free Wi-Fi has been exploited through various technical methods of Wi-Fi penetration testing. The study simulated penetration testing using the Kali Linux in a virtual environment from consented Wi-Fi owners. The overall result of the study shows that free Wi-Fi networks in public areas may not be safe. Free Wi-Fi users must be aware of the risk; hackers accessing their devices and inevitably stealing their private personal information.
{"title":"Philippines' Free Wi-Fi Roll-out Project: Safe or Not?","authors":"E. Blancaflor, Eli Christ Paula C. Castillo, Jan Miguel N. Coretico, Geremie B. Rubiano, Angela Marie D. Tobias","doi":"10.12720/jait.14.1.20-25","DOIUrl":"https://doi.org/10.12720/jait.14.1.20-25","url":null,"abstract":"Free Wi-Fi networks are widely implemented in public areas to provide benefits for the people. This paper focuses on the vulnerabilities around Wi-Fi networks that people are unaware of. Discussing the risks of using free internet is viable as public places in the Philippines have started implementing free Wi-Fi networks. Along with this, the Department of Information and Communication Technology (DICT) of the Philippines is expanding the number of free Wi-Fi to help the Filipinos adjust to the new normal caused by the pandemic. With the increase of internet access, Filipinos should be informed of the risks they may have. In this study, the security of free Wi-Fi has been exploited through various technical methods of Wi-Fi penetration testing. The study simulated penetration testing using the Kali Linux in a virtual environment from consented Wi-Fi owners. The overall result of the study shows that free Wi-Fi networks in public areas may not be safe. Free Wi-Fi users must be aware of the risk; hackers accessing their devices and inevitably stealing their private personal information.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329196","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.14.1.153-159
Trong-Minh Hoang, Trang-Linh Le Thi, N. M. Quy
To meet the growing number and variety of IoT devices in 5G and 6G network environments, the development of edge computing technology is a powerful strategy for offloading processes in data servers by processing at the network and nearby the user. Besides its benefits, several challenges related to decentralized operations for improving performance or security tasks have been identified. A new research direction for distributed operating solutions has emerged from these issues, leading to applying Distributed Machine Learning (DML) techniques for edge computing. It takes advantage of the capacity of edge devices to handle increased data volumes, reduce connection bottlenecks, and enhance data privacy. The designs of DML architectures have to use optimized algorithms (e.g., high accuracy and rapid convergence) and effectively use hardware resources to overcome large-scale problems. However, the trade-off between accuracy and data set volume is always the biggest challenge for practical scenarios. Hence, this paper proposes a novel attack detection model based on the DML technique to detect attacks at network edge devices. A modified voting algorithm is applied to core logic operation between sever and workers in a partition learning fashion. The results of numerical simulations on the UNSW-NB15 dataset have proved that our proposed model is suitable for edge computing and gives better attack detection results than other state of the art solutions.
{"title":"A Novel Distributed Machine Learning Model to Detect Attacks on Edge Computing Network","authors":"Trong-Minh Hoang, Trang-Linh Le Thi, N. M. Quy","doi":"10.12720/jait.14.1.153-159","DOIUrl":"https://doi.org/10.12720/jait.14.1.153-159","url":null,"abstract":"To meet the growing number and variety of IoT devices in 5G and 6G network environments, the development of edge computing technology is a powerful strategy for offloading processes in data servers by processing at the network and nearby the user. Besides its benefits, several challenges related to decentralized operations for improving performance or security tasks have been identified. A new research direction for distributed operating solutions has emerged from these issues, leading to applying Distributed Machine Learning (DML) techniques for edge computing. It takes advantage of the capacity of edge devices to handle increased data volumes, reduce connection bottlenecks, and enhance data privacy. The designs of DML architectures have to use optimized algorithms (e.g., high accuracy and rapid convergence) and effectively use hardware resources to overcome large-scale problems. However, the trade-off between accuracy and data set volume is always the biggest challenge for practical scenarios. Hence, this paper proposes a novel attack detection model based on the DML technique to detect attacks at network edge devices. A modified voting algorithm is applied to core logic operation between sever and workers in a partition learning fashion. The results of numerical simulations on the UNSW-NB15 dataset have proved that our proposed model is suitable for edge computing and gives better attack detection results than other state of the art solutions.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329514","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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