Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581963
Amani Yahyaoui, N. Yumusak
Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method.
{"title":"Deep And Machine Learning Towards Pneumonia And Asthma Detection","authors":"Amani Yahyaoui, N. Yumusak","doi":"10.1109/3ICT53449.2021.9581963","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581963","url":null,"abstract":"Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123816588","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581905
Hitesh Mohapatra
The smart city concept is a solution to many problems that we are facing in our day-to-day life. Many countries have been started adopting many prototypes to solve daily challenges. Still, the smart city term does not have any universally accepted definition. The smart city is a ubiquitous term whose definition varies from person to person, city to city, and country to country. The lack of a common definition remains the term smart city is in a chaotic state. This paper has presented the socio-technical challenges during the implementation of smart city plans. It has analyzed the smart city execution problems in the context of developed and underdeveloped countries.
{"title":"Socio-technical Challenges in the Implementation of Smart City","authors":"Hitesh Mohapatra","doi":"10.1109/3ICT53449.2021.9581905","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581905","url":null,"abstract":"The smart city concept is a solution to many problems that we are facing in our day-to-day life. Many countries have been started adopting many prototypes to solve daily challenges. Still, the smart city term does not have any universally accepted definition. The smart city is a ubiquitous term whose definition varies from person to person, city to city, and country to country. The lack of a common definition remains the term smart city is in a chaotic state. This paper has presented the socio-technical challenges during the implementation of smart city plans. It has analyzed the smart city execution problems in the context of developed and underdeveloped countries.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133876544","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581591
Insaf Achour, S. Ayed, H. Idoudi
Access control is a main component in Blockchain systems. It is usually implemented in smart contracts and defines the security policy, in other words, it determines who can access a protected resource in the network. In this paper, we present a review of the major implementations of access control in Ethereum platform. The latter is based on RBAC model (Role-Based Access Control). Implementations require to take into account the whole RBAC process, that is, user role assignment and permission assignment. Three implementations currently exist and are described and compared in this work according to several features: RBAC-SC, Smart policies and OpenZepplin contracts.
{"title":"On the Implementation of Access Control in Ethereum Blockchain","authors":"Insaf Achour, S. Ayed, H. Idoudi","doi":"10.1109/3ICT53449.2021.9581591","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581591","url":null,"abstract":"Access control is a main component in Blockchain systems. It is usually implemented in smart contracts and defines the security policy, in other words, it determines who can access a protected resource in the network. In this paper, we present a review of the major implementations of access control in Ethereum platform. The latter is based on RBAC model (Role-Based Access Control). Implementations require to take into account the whole RBAC process, that is, user role assignment and permission assignment. Three implementations currently exist and are described and compared in this work according to several features: RBAC-SC, Smart policies and OpenZepplin contracts.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116133469","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582052
Soon Hwai Ing, A. Abdullah, Shigehiko Kanaya
The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvelous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19.3 machine learning algorithms, which are, Linear Regression (LR), Light Gradient Boosting Machine (LGBM) and Random Forest (RF) are proposed for 12 models development. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that the LGBM-based model which is trained along with auxiliary bpps features and encoded with method 1 label encoding performs the best (RMSE = 0.24466), followed by the same criteria LGBM-based model but encoded with label encoding method 2, with a difference in 0.00003 in tow the topnotch model. The RF-based model with applaudable performance (RMSE = 0.25302) even without the ubieties of the riddled bpps features in contradistinction to the training and encoding criteria of the superb mellowed LGBM-based model is worth being further cultivated for the prediction study on COVID-19 mRNA vaccines' degradation rate.
{"title":"Development of COVID-19 mRNA Vaccine Degradation Prediction System","authors":"Soon Hwai Ing, A. Abdullah, Shigehiko Kanaya","doi":"10.1109/3ICT53449.2021.9582052","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582052","url":null,"abstract":"The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvelous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19.3 machine learning algorithms, which are, Linear Regression (LR), Light Gradient Boosting Machine (LGBM) and Random Forest (RF) are proposed for 12 models development. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that the LGBM-based model which is trained along with auxiliary bpps features and encoded with method 1 label encoding performs the best (RMSE = 0.24466), followed by the same criteria LGBM-based model but encoded with label encoding method 2, with a difference in 0.00003 in tow the topnotch model. The RF-based model with applaudable performance (RMSE = 0.25302) even without the ubieties of the riddled bpps features in contradistinction to the training and encoding criteria of the superb mellowed LGBM-based model is worth being further cultivated for the prediction study on COVID-19 mRNA vaccines' degradation rate.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502102","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581462
Brahim Mefgouda, H. Idoudi
Network Interface Selection (NIS) aims to connect the user equipment to the best available network in the context of heterogeneous wireless networks environments (HWN). NIS is one of the main current issues in HWNs that raised great scientific interest in the last few years. Multi-attribute decision-making (MADM) are the most common approaches applied to solve the NIS problem as they are easy to understand, they can be used in real scenarios, and they perform fast networks' ranking. In this paper, we apply, for the first time, the Combined Compromise Solution (COCOSO) to model and solve the network interface selection problem. Simulation results showed that our proposed approach outperforms the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) in terms of reducing the rank reversal problem and meeting QoS requirements.
{"title":"COCOSO-based Network Interface Selection Algorithm for Heterogeneous Wireless Networks","authors":"Brahim Mefgouda, H. Idoudi","doi":"10.1109/3ICT53449.2021.9581462","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581462","url":null,"abstract":"Network Interface Selection (NIS) aims to connect the user equipment to the best available network in the context of heterogeneous wireless networks environments (HWN). NIS is one of the main current issues in HWNs that raised great scientific interest in the last few years. Multi-attribute decision-making (MADM) are the most common approaches applied to solve the NIS problem as they are easy to understand, they can be used in real scenarios, and they perform fast networks' ranking. In this paper, we apply, for the first time, the Combined Compromise Solution (COCOSO) to model and solve the network interface selection problem. Simulation results showed that our proposed approach outperforms the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) in terms of reducing the rank reversal problem and meeting QoS requirements.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124709625","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582060
Kameron Bains, Adebamigbe Fasanmade, J. Morden, A. Al-Bayatti, M. S. Sharif, A. S. Alfakeeh
Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.
{"title":"Defeating the Credit Card Scams Through Machine Learning Algorithms","authors":"Kameron Bains, Adebamigbe Fasanmade, J. Morden, A. Al-Bayatti, M. S. Sharif, A. S. Alfakeeh","doi":"10.1109/3ICT53449.2021.9582060","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582060","url":null,"abstract":"Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123060860","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581846
M. F. Adak, S. Ercan
Nowadays, online education has become widespread, and the search for new techniques has begun to increase. The high number of quotas in university education in Turkey increases the number of students per instructor. It is not at the desired level for the student to receive a good education in the presence of an advisor and choose the appropriate course for his / her field due to a large number of students. In this study, a suggestion system is proposed by analyzing the previous courses taken by university students in directing the elective course. In this study, which courses would be beneficial to choose and which would be useless are presented with a web interface in which Support Vector Machine and decision trees are used. In the pilot study that the model developed conducted in the Computer Engineering department, an average of 76% success was achieved in test data sets. This success shows that the student can examine the compulsory courses and suggest elective courses suitable for his/her field and that he/she will like.
{"title":"Support Vector Machine and Decision Tree-Based Elective Course Suggestion System: A Case Study","authors":"M. F. Adak, S. Ercan","doi":"10.1109/3ICT53449.2021.9581846","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581846","url":null,"abstract":"Nowadays, online education has become widespread, and the search for new techniques has begun to increase. The high number of quotas in university education in Turkey increases the number of students per instructor. It is not at the desired level for the student to receive a good education in the presence of an advisor and choose the appropriate course for his / her field due to a large number of students. In this study, a suggestion system is proposed by analyzing the previous courses taken by university students in directing the elective course. In this study, which courses would be beneficial to choose and which would be useless are presented with a web interface in which Support Vector Machine and decision trees are used. In the pilot study that the model developed conducted in the Computer Engineering department, an average of 76% success was achieved in test data sets. This success shows that the student can examine the compulsory courses and suggest elective courses suitable for his/her field and that he/she will like.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121599897","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581352
Alhadi A. Klaib, A. Milad, Mustafa Almahdi Algaet
Extensible Markup Language (XML) has become a key technique for transferring data through the internet. Updating and retrieving a large amount of XML data is a very active research field. The XML labelling schemes play an important role in handling XML data efficiently and robustly. Thus, many labelling schemes have been proposed. Nevertheless, these labelling schemes have limitations. Therefore, in this paper, a new method for labelling XML documents is developed. In addition, this approach used the idea of clustering-based XML data and dividing the nodes of an XML document into groups and labelling them accordingly. Two existing labelling schemes were chosen to label the clusters and their nodes as well. The level-based labelling scheme (LLS) and Dewey labelling scheme were used to label the nodes and clusters. The data model of this scheme has been developed. The mechanism of the proposed scheme also has been developed. Finally, this proposed scheme and the other two labelling schemes that used to build the proposed scheme have been implemented.
{"title":"A New Approach for Labelling XML Data","authors":"Alhadi A. Klaib, A. Milad, Mustafa Almahdi Algaet","doi":"10.1109/3ICT53449.2021.9581352","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581352","url":null,"abstract":"Extensible Markup Language (XML) has become a key technique for transferring data through the internet. Updating and retrieving a large amount of XML data is a very active research field. The XML labelling schemes play an important role in handling XML data efficiently and robustly. Thus, many labelling schemes have been proposed. Nevertheless, these labelling schemes have limitations. Therefore, in this paper, a new method for labelling XML documents is developed. In addition, this approach used the idea of clustering-based XML data and dividing the nodes of an XML document into groups and labelling them accordingly. Two existing labelling schemes were chosen to label the clusters and their nodes as well. The level-based labelling scheme (LLS) and Dewey labelling scheme were used to label the nodes and clusters. The data model of this scheme has been developed. The mechanism of the proposed scheme also has been developed. Finally, this proposed scheme and the other two labelling schemes that used to build the proposed scheme have been implemented.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126166831","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581590
A. Akib, S. Mahmud, M. Mridha
The paper focuses on the Future Micro Hydro Power: generation of hydroelectricity and its monitoring system. The world is moving towards technological advancement day by day. For this reason, the energy need will surge further in the coming days. But we could not yet ensure the proper electricity needs in the poor or developing country. Now it's an essential needy thing to survive this 4.0 industry's time. This ‘Future Micro Hydro Power’ device will generate energy by exploiting the small water sources (i.e., Washroom, Kitchen, Etc.) in the multi steroid buildings. A massive amount of water is used in the house every day. Water taps are used not only in homes but in all modern buildings. We have demonstrated how hydropower will generate from these tiny water sources and how this power can run a house. Here the user will be able to monitor the amount of energy produced and use it if desired. The cost of the devices will be much lower, and their performance will be much higher. After the experimental installation, we got some data that proves its outstanding efficiency.
{"title":"Future Micro Hydro Power: Generation of Hydroelectricity and IoT based Monitoring System","authors":"A. Akib, S. Mahmud, M. Mridha","doi":"10.1109/3ICT53449.2021.9581590","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581590","url":null,"abstract":"The paper focuses on the Future Micro Hydro Power: generation of hydroelectricity and its monitoring system. The world is moving towards technological advancement day by day. For this reason, the energy need will surge further in the coming days. But we could not yet ensure the proper electricity needs in the poor or developing country. Now it's an essential needy thing to survive this 4.0 industry's time. This ‘Future Micro Hydro Power’ device will generate energy by exploiting the small water sources (i.e., Washroom, Kitchen, Etc.) in the multi steroid buildings. A massive amount of water is used in the house every day. Water taps are used not only in homes but in all modern buildings. We have demonstrated how hydropower will generate from these tiny water sources and how this power can run a house. Here the user will be able to monitor the amount of energy produced and use it if desired. The cost of the devices will be much lower, and their performance will be much higher. After the experimental installation, we got some data that proves its outstanding efficiency.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631077","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581625
Ikram Ben abdel ouahab, Lotfi Elaachak, Yasser A. Alluhaidan, M. Bouhorma
In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.
{"title":"A new approach to detect next generation of malware based on machine learning","authors":"Ikram Ben abdel ouahab, Lotfi Elaachak, Yasser A. Alluhaidan, M. Bouhorma","doi":"10.1109/3ICT53449.2021.9581625","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581625","url":null,"abstract":"In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130642630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：