Pub Date : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00014
S. Oppong, E. Baah, Mathias Agbeko, Justice Nueteh Terkper
In recent times especially in the field of cloud computing, one of the most radical forms and threatening key issue of cyber-attacks has to do with botnets. Botnets with their flexible and dynamic nature together with a botmaster, mastermind their operations, change their codes, and update the bots daily in order to prevent the present detection methods. Despite high-profile efforts to tackle botnets, the number of botnets and infected systems only continues to grow. Early detection and analysis of these increasing number of botnet attack greatly impact the operational activities of any internet-related organization. Machine learning algorithms have played a key role in the detections and analysis of botnet infected packets in attacks such as DDoS attacks. This study, using Principal Component Analysis and an ensemble voting classifier improves the detection of botnet attacks. The results showed increased performance in terms of running time, accuracy, precision, and false-positive.
{"title":"Improved Botnet Attack Detection Using Principal Component Analysis and Ensemble Voting Algorithm","authors":"S. Oppong, E. Baah, Mathias Agbeko, Justice Nueteh Terkper","doi":"10.1109/ICCMA53594.2021.00014","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00014","url":null,"abstract":"In recent times especially in the field of cloud computing, one of the most radical forms and threatening key issue of cyber-attacks has to do with botnets. Botnets with their flexible and dynamic nature together with a botmaster, mastermind their operations, change their codes, and update the bots daily in order to prevent the present detection methods. Despite high-profile efforts to tackle botnets, the number of botnets and infected systems only continues to grow. Early detection and analysis of these increasing number of botnet attack greatly impact the operational activities of any internet-related organization. Machine learning algorithms have played a key role in the detections and analysis of botnet infected packets in attacks such as DDoS attacks. This study, using Principal Component Analysis and an ensemble voting classifier improves the detection of botnet attacks. The results showed increased performance in terms of running time, accuracy, precision, and false-positive.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"32 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114038995","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00009
Thanh Hoang Le Hai, Loc La Hoang, N. Thoai
Utilizing performance is one of the most important and difficult tasks on High-Performance Computing (HPC) systems. In order to balance between simplicity and efficiency, many HPC systems are exploiting First-Come-First-Served schedulers with backfilling policies. Previous researches demonstrated the benefits of walltime approximation approaches on backfilling improvement. However, applying predicted values might affect scheduling guarantees due to the risk of early job termination caused by underestimation. The recent soft walltime feature from OpenPBS has eliminated this concern by restricting system-generated walltime only for the scheduling step.In this work, we present our simple k-nearest neighbors approach to improve the performance of the conservative backfilling algorithm. The inaccurate user estimate of a job is refined using the historic data about its most similar jobs. Then, we exploit this correction safely with the soft walltime scheme and perform simulations on real scheduling logs. Our simulation results highlight that even our simple setup can help to correct user estimates significantly. Moreover, the scheduling performance is also improved on some appropriate conditions.
{"title":"Potential of Applying kNN with Soft Walltime to Improve Scheduling Performance","authors":"Thanh Hoang Le Hai, Loc La Hoang, N. Thoai","doi":"10.1109/ICCMA53594.2021.00009","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00009","url":null,"abstract":"Utilizing performance is one of the most important and difficult tasks on High-Performance Computing (HPC) systems. In order to balance between simplicity and efficiency, many HPC systems are exploiting First-Come-First-Served schedulers with backfilling policies. Previous researches demonstrated the benefits of walltime approximation approaches on backfilling improvement. However, applying predicted values might affect scheduling guarantees due to the risk of early job termination caused by underestimation. The recent soft walltime feature from OpenPBS has eliminated this concern by restricting system-generated walltime only for the scheduling step.In this work, we present our simple k-nearest neighbors approach to improve the performance of the conservative backfilling algorithm. The inaccurate user estimate of a job is refined using the historic data about its most similar jobs. Then, we exploit this correction safely with the soft walltime scheme and perform simulations on real scheduling logs. Our simulation results highlight that even our simple setup can help to correct user estimates significantly. Moreover, the scheduling performance is also improved on some appropriate conditions.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126204718","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00024
Sanskar Jethi, A. Choudhary, Yash Gupta
We have seen significant advancements in Machine Learning and Artificial Intelligence in the 21st century. It has enabled a new technology where we can have a human-like conversation with the machines. Now, computers are much more than just being advanced calculators. Judging to check if a computer can only think will be a major understatement to the modern computer’s ability. Analogous to a human, it is not enough to only be intelligent, a human needs to communicate well to be perceived as “intelligent”. In this paper, we will create a test to judge if a machine can communicate.
{"title":"Can machines Communicate?","authors":"Sanskar Jethi, A. Choudhary, Yash Gupta","doi":"10.1109/ICCMA53594.2021.00024","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00024","url":null,"abstract":"We have seen significant advancements in Machine Learning and Artificial Intelligence in the 21st century. It has enabled a new technology where we can have a human-like conversation with the machines. Now, computers are much more than just being advanced calculators. Judging to check if a computer can only think will be a major understatement to the modern computer’s ability. Analogous to a human, it is not enough to only be intelligent, a human needs to communicate well to be perceived as “intelligent”. In this paper, we will create a test to judge if a machine can communicate.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128030338","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00010
D. K. Dake, D. Essel, Justice Edem Agbodaze
COVID-19 pandemic has affected various sectors of the global economy including the abrupt closure of schools in March 2020 in Ghana. This sudden closure has led to a revamp in online teaching and learning across most institutions with learners submitting their assignments and taking their assessments on various learning management systems while at home.In this study, we used classification algorithms to investigate features and predict the academic performance of students during the pandemic. We collected data from students in the Department of ICT Education of the University of Education, Winneba during the COVID-19 period using carefully selected attributes that could affect their exams score. The results detailed dominant attributes that affected students’ performance with Random Forest, Random Tree, Naïve Bayes and J48 Decision Tree algorithms further analysed for accuracy, confusion matrix and the ROC Curve. After detailed analysis, we observed that the accuracy of a classifier alone is not indicative enough of its performance.
{"title":"Using Machine Learning to Predict Students’ Academic Performance During Covid-19","authors":"D. K. Dake, D. Essel, Justice Edem Agbodaze","doi":"10.1109/ICCMA53594.2021.00010","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00010","url":null,"abstract":"COVID-19 pandemic has affected various sectors of the global economy including the abrupt closure of schools in March 2020 in Ghana. This sudden closure has led to a revamp in online teaching and learning across most institutions with learners submitting their assignments and taking their assessments on various learning management systems while at home.In this study, we used classification algorithms to investigate features and predict the academic performance of students during the pandemic. We collected data from students in the Department of ICT Education of the University of Education, Winneba during the COVID-19 period using carefully selected attributes that could affect their exams score. The results detailed dominant attributes that affected students’ performance with Random Forest, Random Tree, Naïve Bayes and J48 Decision Tree algorithms further analysed for accuracy, confusion matrix and the ROC Curve. After detailed analysis, we observed that the accuracy of a classifier alone is not indicative enough of its performance.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"479 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115876261","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00028
Lubomír Štěpánek, Filip Habarta, I. Malá, L. Marek
When having more initial data than needed, an appropriate selection of a subpopulation from the given dataset, i. e. subsampling, follows, usually intending to ensure all categorical variables’ levels are nearly equally covered and all numerical variables are all well balanced. If a response variable in the original data is missing, popular propensity scoring cannot be performed.This study addresses the subsampling with the missing response variable about to be collected later, using a COVID-19 dataset (N = 698) with 18 variables of interest, reduced to a subdataset (n = 400). The quality of subpopulation selecting was measured using the minimization of a sum of squares of each variable’s category frequency and averaged over all variables.An exhaustive method selecting all possible combinations of n = 400 observations from initial N = 698 observations was used as a benchmark. Choosing the subsample from a random set of them that minimized the metric returned similar results as a “forward” subselection reducing the original dataset an observation-by-observation per each step by permanently lowering the metric. Finally, k-means clustering (with a random number of clusters) of the original dataset’s observations and choosing a subsample from each cluster, proportionally to its size, also lowered the metric compared to the random subsampling.All four latter approaches showed better results than single random subsampling, considering the metric minimization. However, while the exhaustive sampling is very greedy and time-consuming, the forward one-by-one reducing the original dataset, picking up the subsample minimizing the metric, and subsampling the clusters, are feasible for selecting a well-balanced subdataset, ready for ongoing response variable collecting.
{"title":"“Great in, great out” is the new “garbage in, garbage out”: subsampling from data with no response variable using various approaches, including unsupervised learning","authors":"Lubomír Štěpánek, Filip Habarta, I. Malá, L. Marek","doi":"10.1109/ICCMA53594.2021.00028","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00028","url":null,"abstract":"When having more initial data than needed, an appropriate selection of a subpopulation from the given dataset, i. e. subsampling, follows, usually intending to ensure all categorical variables’ levels are nearly equally covered and all numerical variables are all well balanced. If a response variable in the original data is missing, popular propensity scoring cannot be performed.This study addresses the subsampling with the missing response variable about to be collected later, using a COVID-19 dataset (N = 698) with 18 variables of interest, reduced to a subdataset (n = 400). The quality of subpopulation selecting was measured using the minimization of a sum of squares of each variable’s category frequency and averaged over all variables.An exhaustive method selecting all possible combinations of n = 400 observations from initial N = 698 observations was used as a benchmark. Choosing the subsample from a random set of them that minimized the metric returned similar results as a “forward” subselection reducing the original dataset an observation-by-observation per each step by permanently lowering the metric. Finally, k-means clustering (with a random number of clusters) of the original dataset’s observations and choosing a subsample from each cluster, proportionally to its size, also lowered the metric compared to the random subsampling.All four latter approaches showed better results than single random subsampling, considering the metric minimization. However, while the exhaustive sampling is very greedy and time-consuming, the forward one-by-one reducing the original dataset, picking up the subsample minimizing the metric, and subsampling the clusters, are feasible for selecting a well-balanced subdataset, ready for ongoing response variable collecting.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133125008","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 : 2021-07-01DOI: 10.1109/iccma53594.2021.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/iccma53594.2021.00003","DOIUrl":"https://doi.org/10.1109/iccma53594.2021.00003","url":null,"abstract":"","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129804725","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00033
Isaac Baffour Senkyire, Q. Kester
The current age permits for legal, official, sensitive, and confidential documents to be exchanged using digital channels among stakeholders. In this digital age, new advances in technology are tremendously vital creating more sophisticated and intelligent tools in areas like informatics, electronics, and telecommunication. In the wake of these new advances, any individual can digitize any kind of document, and modern computing tools can alter all these using computational drawing tools, such as GIMP or Paint Shop Pro, without causing any distortions hence, tampered documents can be presented with the same quality as the original documents. Documents tampered, when used or distributed illegally can cause economic, political, legal and moral damages to individuals and organizations. In this paper, we propose to detect the tampering of documents using SHA-384 hash function..
{"title":"A Cryptographic Tamper Detection Approach for Storage and Preservation of Forensic Digital Data Based on SHA 384 Hash Function","authors":"Isaac Baffour Senkyire, Q. Kester","doi":"10.1109/ICCMA53594.2021.00033","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00033","url":null,"abstract":"The current age permits for legal, official, sensitive, and confidential documents to be exchanged using digital channels among stakeholders. In this digital age, new advances in technology are tremendously vital creating more sophisticated and intelligent tools in areas like informatics, electronics, and telecommunication. In the wake of these new advances, any individual can digitize any kind of document, and modern computing tools can alter all these using computational drawing tools, such as GIMP or Paint Shop Pro, without causing any distortions hence, tampered documents can be presented with the same quality as the original documents. Documents tampered, when used or distributed illegally can cause economic, political, legal and moral damages to individuals and organizations. In this paper, we propose to detect the tampering of documents using SHA-384 hash function..","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130780422","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00015
Samuel Afoakwa, Crentsil Kwayie, Joseph Owusu
The application of natural language processing (NLP) methods to designing conversational frameworks for health diagnosis improves patients’ access to medical information. An Android application based on fuzzy logic rules and fuzzy inference was created in this research. In Ghana, the service assesses the symptoms of diseases. The android application is built with the Support Vector Machine learning technique, with the aim of improving the model’s accuracy and performance. Natural Language Processing is often used by the machine to achieve the conversational style of asking the users for their symptoms. People can spend less time in hospitals and get low-cost or free care by using this technique, which is mainly used in Ghana’s rural areas.
{"title":"An Android Application for Clinical Diagnosis Using NLP and Fuzzy Logic","authors":"Samuel Afoakwa, Crentsil Kwayie, Joseph Owusu","doi":"10.1109/ICCMA53594.2021.00015","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00015","url":null,"abstract":"The application of natural language processing (NLP) methods to designing conversational frameworks for health diagnosis improves patients’ access to medical information. An Android application based on fuzzy logic rules and fuzzy inference was created in this research. In Ghana, the service assesses the symptoms of diseases. The android application is built with the Support Vector Machine learning technique, with the aim of improving the model’s accuracy and performance. Natural Language Processing is often used by the machine to achieve the conversational style of asking the users for their symptoms. People can spend less time in hospitals and get low-cost or free care by using this technique, which is mainly used in Ghana’s rural areas.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132258058","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00031
Nana Agyeman-Prempeh, P. Acheampong, Emmanuel Freeman, Eric Ekobor-Ackah Mochiah, R. Abubakar, Louis David Jnr. Annor
This paper seeks to assess a sustainable waste management practices in a higher education institution of Ghana. The driving force for this research is the increase in the volumes of daily waste on university campuses as a result of increasing number of students’ enrolment and other university events and services. The mixed method approach was used for this study. A total sample of 214 students from Ghana Communication Technology University (GCTU) was used for the study. The research aimed at collating the requisite information from students from Level 100 to the graduate level on their assessments on solid waste management practices at GCTU. Findings from the studies revealed that the various types of solid waste generated by most students was paper which was not surprising in a school setting, followed by plastics waste and then organic waste. It was also discovered that students’ perception and awareness of solid waste management practices at GCTU was good and commendable. Recommendations was directed to all stakeholders for need to adopt a zero-waste concept. Thus, a recommendation was made towards an establishment of a policy to aid in protecting the environment to enhance a serene environment for teaching and learning.
{"title":"Sustainable Waste Management Practices in a Higher Education Institution of Ghana","authors":"Nana Agyeman-Prempeh, P. Acheampong, Emmanuel Freeman, Eric Ekobor-Ackah Mochiah, R. Abubakar, Louis David Jnr. Annor","doi":"10.1109/ICCMA53594.2021.00031","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00031","url":null,"abstract":"This paper seeks to assess a sustainable waste management practices in a higher education institution of Ghana. The driving force for this research is the increase in the volumes of daily waste on university campuses as a result of increasing number of students’ enrolment and other university events and services. The mixed method approach was used for this study. A total sample of 214 students from Ghana Communication Technology University (GCTU) was used for the study. The research aimed at collating the requisite information from students from Level 100 to the graduate level on their assessments on solid waste management practices at GCTU. Findings from the studies revealed that the various types of solid waste generated by most students was paper which was not surprising in a school setting, followed by plastics waste and then organic waste. It was also discovered that students’ perception and awareness of solid waste management practices at GCTU was good and commendable. Recommendations was directed to all stakeholders for need to adopt a zero-waste concept. Thus, a recommendation was made towards an establishment of a policy to aid in protecting the environment to enhance a serene environment for teaching and learning.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127785531","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 : 2021-07-01DOI: 10.1109/ICCMA53594.2021.00021
Meriem Kherbache, D. Espès, Kamal Amroun
The development of an anomaly-based Intrusion Detection System (IDS) is of primary importance in networks because it reinforces security. Unlike supervised methods, unsupervised methods are not widely used although they are fast and efficient. In this paper, we propose an unsupervised approach based on the K-means method to show the efficacy of these models over the supervised methods. The proposed model improves the K-means method using the Caliniski Harabasz indicator to find the appropriate number of clusters required for clustering by computing the intra-cluster to inter-cluster ratio. Above all, the proposed model is applied to two datasets, the well-known NSL-KDD and the newest CICIDS2017. The experimental results show that the proposed model exceeds largely the traditional K-means method. Additionally, it is also very efficient both in detection and time consuming compared to the SVM classifier that is a supervised classifier.
{"title":"An Enhanced approach of the K-means clustering for Anomaly-based intrusion detection systems*","authors":"Meriem Kherbache, D. Espès, Kamal Amroun","doi":"10.1109/ICCMA53594.2021.00021","DOIUrl":"https://doi.org/10.1109/ICCMA53594.2021.00021","url":null,"abstract":"The development of an anomaly-based Intrusion Detection System (IDS) is of primary importance in networks because it reinforces security. Unlike supervised methods, unsupervised methods are not widely used although they are fast and efficient. In this paper, we propose an unsupervised approach based on the K-means method to show the efficacy of these models over the supervised methods. The proposed model improves the K-means method using the Caliniski Harabasz indicator to find the appropriate number of clusters required for clustering by computing the intra-cluster to inter-cluster ratio. Above all, the proposed model is applied to two datasets, the well-known NSL-KDD and the newest CICIDS2017. The experimental results show that the proposed model exceeds largely the traditional K-means method. Additionally, it is also very efficient both in detection and time consuming compared to the SVM classifier that is a supervised classifier.","PeriodicalId":131082,"journal":{"name":"2021 International Conference on Computing, Computational Modelling and Applications (ICCMA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126747145","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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