Pub Date : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043201
S. Idris, Usman Mohammed, Jaafaru Sanusi, Sadiq Thomas
The fifth-generation (5G) mobile network is the next paradigm shift in the revolutionary era of the wireless communication technologies that will break the backward compatibility of today’s communication systems. Visible Light Communication (VLC) and Light Fidelity (LiFi) technologies are among the potential candidates that are expected to be utilized in the future 5G networks due to their indoor energy-efficient communications. Realized by Light Emitting Diodes (LEDs), VLC and LiFi possesses a number of prominent features to meet the highly demanding requirements of ultrahigh-speed, massive Multiple-Input Multiple-Output (MIMO) device connectivity, ultra-low-latency, ultra-high reliable and low energy consumption for 5G networks. This paper provides an overview contributions of VLC and LiFi towards 5G networks. Furthermore, we explain how VLC and LiFi can successfully provide effective solutions for the emerging 5G networks.
{"title":"Visible Light Communication: A potential 5G and beyond Communication Technology","authors":"S. Idris, Usman Mohammed, Jaafaru Sanusi, Sadiq Thomas","doi":"10.1109/ICECCO48375.2019.9043201","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043201","url":null,"abstract":"The fifth-generation (5G) mobile network is the next paradigm shift in the revolutionary era of the wireless communication technologies that will break the backward compatibility of today’s communication systems. Visible Light Communication (VLC) and Light Fidelity (LiFi) technologies are among the potential candidates that are expected to be utilized in the future 5G networks due to their indoor energy-efficient communications. Realized by Light Emitting Diodes (LEDs), VLC and LiFi possesses a number of prominent features to meet the highly demanding requirements of ultrahigh-speed, massive Multiple-Input Multiple-Output (MIMO) device connectivity, ultra-low-latency, ultra-high reliable and low energy consumption for 5G networks. This paper provides an overview contributions of VLC and LiFi towards 5G networks. Furthermore, we explain how VLC and LiFi can successfully provide effective solutions for the emerging 5G networks.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958920","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043257
Onyedikachi Vincent Okereke, Fatima Aliyu, Jonathan Dangwaran, Sadiq Thomas, Biliyok Akawu Shekari, Hussein U. Suleiman
As the world develops it looks for a greener way to produce energy. Here we take a look at the present and previous ways in which Nigeria produces energy and we compare with a particular alternative renewable source, solar photovoltaic system. Solar photovoltaic system uses a method of photoelectric effect in order to convert the energy from the sun into electricity by absorbing and utilizing it. We go further in this project by reviewing some calculations to see how solar energy compares to other forms of electricity supply over a period of 20 years. Finally, reasons were given why it is preferable to use solar PV systems as compared to other forms.
{"title":"Using Solar Photovoltaic Systems to Significantly Reduce Power Production Problems in Nigeria and Create a Greener Environment","authors":"Onyedikachi Vincent Okereke, Fatima Aliyu, Jonathan Dangwaran, Sadiq Thomas, Biliyok Akawu Shekari, Hussein U. Suleiman","doi":"10.1109/ICECCO48375.2019.9043257","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043257","url":null,"abstract":"As the world develops it looks for a greener way to produce energy. Here we take a look at the present and previous ways in which Nigeria produces energy and we compare with a particular alternative renewable source, solar photovoltaic system. Solar photovoltaic system uses a method of photoelectric effect in order to convert the energy from the sun into electricity by absorbing and utilizing it. We go further in this project by reviewing some calculations to see how solar energy compares to other forms of electricity supply over a period of 20 years. Finally, reasons were given why it is preferable to use solar PV systems as compared to other forms.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788562","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043283
F. I. Lawan, L. Ismaila, Steve A. Adeshina, H. I. Muhammed, L. Csató
In effort to productively utilize the exponential growth of image analysis and learning capability of Neural Networks (NN), we present our work which is dedicated to developing and training a deep neural network to extract meaningful patterns from a set of labeled data i.e. making generalizations. We show that Deep Neural Networks (DNNs) can learn feature representations that can be successfully applied in a wide spectrum of application domains. We showed how DNNs are applied to classification problems, grading of fresh tomato fruits based on their physical qualities using supervised learning approach. We achieved a result of about 60% accuracy using our local dataset which is quiet reasonable than using other standardized dataset as in the case of other researchers. Additionally, we are very sure of getting better result by fine-tuning some of our parameters because out network learns to generalize as the number iterations increases and so also the accuracy of predictions.
{"title":"Deep Learning Methods for Filter Extraction in Tomato fruits","authors":"F. I. Lawan, L. Ismaila, Steve A. Adeshina, H. I. Muhammed, L. Csató","doi":"10.1109/ICECCO48375.2019.9043283","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043283","url":null,"abstract":"In effort to productively utilize the exponential growth of image analysis and learning capability of Neural Networks (NN), we present our work which is dedicated to developing and training a deep neural network to extract meaningful patterns from a set of labeled data i.e. making generalizations. We show that Deep Neural Networks (DNNs) can learn feature representations that can be successfully applied in a wide spectrum of application domains. We showed how DNNs are applied to classification problems, grading of fresh tomato fruits based on their physical qualities using supervised learning approach. We achieved a result of about 60% accuracy using our local dataset which is quiet reasonable than using other standardized dataset as in the case of other researchers. Additionally, we are very sure of getting better result by fine-tuning some of our parameters because out network learns to generalize as the number iterations increases and so also the accuracy of predictions.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"1645 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115832774","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043291
Abubakar Umar Turaki, Gokhan Koyunlu, Nyangwarimam Obadiah Ali, Abubakar Idrissa, G. Sani, Omotayo Oshiga
Atmospheric propagation faces signal degradation in satellite communication services operating in frequencies of Ku-band, Ka-band and above. This effect is caused by rain, storms, and other unfavorable atmospheric conditions that bring about losses along the entire link path from space to earth. This study examined the impact of rain and predicts its induced attenuation on broadband satellite links in Abuja Nigeria. The point rainfall data was collected for a period of four years, and 1-min rainfall rate extracted. Annual rainfall rate was quantified to fall within 120mm/h and the effect of rain on broadband satellite link operating on Ku band frequency was evaluated to an average induced attenuation of17 dB.
{"title":"Rain Induced Attenuation Prediction in the Ku Band of Nigerian Communication Satellite over Abuja Earth Station","authors":"Abubakar Umar Turaki, Gokhan Koyunlu, Nyangwarimam Obadiah Ali, Abubakar Idrissa, G. Sani, Omotayo Oshiga","doi":"10.1109/ICECCO48375.2019.9043291","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043291","url":null,"abstract":"Atmospheric propagation faces signal degradation in satellite communication services operating in frequencies of Ku-band, Ka-band and above. This effect is caused by rain, storms, and other unfavorable atmospheric conditions that bring about losses along the entire link path from space to earth. This study examined the impact of rain and predicts its induced attenuation on broadband satellite links in Abuja Nigeria. The point rainfall data was collected for a period of four years, and 1-min rainfall rate extracted. Annual rainfall rate was quantified to fall within 120mm/h and the effect of rain on broadband satellite link operating on Ku band frequency was evaluated to an average induced attenuation of17 dB.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132929017","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043276
Sunday Barde Danladi, Faruku Umar Ambursa
Over the years congestion has been a major issue affecting the internet leading to an increase in packet loss and delay. Researchers have proposed different algorithms to address the issue of congestion from Drop Tail, Early Random Drop to Active Queue Management (AQM). Random Early Detection (RED) is the first Active Queue Management (AQM) technique that was developed to support transport-layer congestion and decrease the impacts of network congestion on the router buffer. The idea behind RED is to sense and detect incipient congestion early and notify connections of congestion either by dropping packets arriving or by reducing its sending rate. Although various other AQM techniques have been proposed by researchers, RED is still the most commonly used algorithm for congestion avoidance and researches is still ongoing to enhance the performance of RED. In this paper, we have developed an extension to RED to address the limitation of RED and the algorithm is then compared with RED under various network scenarios. The results of the evaluation shows that the new method has outperformed RED.
{"title":"DyRED: An Enhanced Random Early Detection Based on a new Adaptive Congestion Control","authors":"Sunday Barde Danladi, Faruku Umar Ambursa","doi":"10.1109/ICECCO48375.2019.9043276","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043276","url":null,"abstract":"Over the years congestion has been a major issue affecting the internet leading to an increase in packet loss and delay. Researchers have proposed different algorithms to address the issue of congestion from Drop Tail, Early Random Drop to Active Queue Management (AQM). Random Early Detection (RED) is the first Active Queue Management (AQM) technique that was developed to support transport-layer congestion and decrease the impacts of network congestion on the router buffer. The idea behind RED is to sense and detect incipient congestion early and notify connections of congestion either by dropping packets arriving or by reducing its sending rate. Although various other AQM techniques have been proposed by researchers, RED is still the most commonly used algorithm for congestion avoidance and researches is still ongoing to enhance the performance of RED. In this paper, we have developed an extension to RED to address the limitation of RED and the algorithm is then compared with RED under various network scenarios. The results of the evaluation shows that the new method has outperformed RED.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302565","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043288
Joseph Yisa Ndagi, J. Alhassan
Exponential growth experienced in Internet usage has paved the way to exploit users of the Internet, a phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false-positive rate and low accuracy have been a setback in phishing detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas and Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model Tree (LMT), Forest by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quite several classifiers also exist which if properly explored will yield more accurate results for phishing detection. RandomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false-positive rate for phishing classification which suggests that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.
{"title":"Machine Learning Classification Algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis","authors":"Joseph Yisa Ndagi, J. Alhassan","doi":"10.1109/ICECCO48375.2019.9043288","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043288","url":null,"abstract":"Exponential growth experienced in Internet usage has paved the way to exploit users of the Internet, a phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false-positive rate and low accuracy have been a setback in phishing detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas and Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model Tree (LMT), Forest by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quite several classifiers also exist which if properly explored will yield more accurate results for phishing detection. RandomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false-positive rate for phishing classification which suggests that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116316654","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043183
N. C. Onyemachi, O. Nonyelum
The amount of data being generated in the healthcare industry is growing at a very fast rate. This has generated immense interest in leveraging the availability of healthcare data to improve health outcomes and reduce costs. Big data analytics has earned a remarkable interest in the health sector as it could be used in the diagnosis and prediction of diseases. This paper is a review of current big data analytics techniques in healthcare, their applications, challenges and solutions to those challenges.
{"title":"Big Data Analytics in Healthcare: A Review","authors":"N. C. Onyemachi, O. Nonyelum","doi":"10.1109/ICECCO48375.2019.9043183","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043183","url":null,"abstract":"The amount of data being generated in the healthcare industry is growing at a very fast rate. This has generated immense interest in leveraging the availability of healthcare data to improve health outcomes and reduce costs. Big data analytics has earned a remarkable interest in the health sector as it could be used in the diagnosis and prediction of diseases. This paper is a review of current big data analytics techniques in healthcare, their applications, challenges and solutions to those challenges.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116985334","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043199
Cemil Turan, A. Aitimov, B. Kynabay, Aimoldir Aldabergen
One of the most popular tool implemented in face recognition issues is Principal Component Analysis (PCA) which is successfully used in machine learning and data analysis. However, if the images are not regular with some factors that affect the image recognition accuracy such as variation of facial expressions, different poses or lighting problems, this technique may show some deficiencies. In this work, different kinds of methods were implemented by combining different preprocessing techniques to evaluate and compare them under different lighting conditions of images. In order to have the same lighting conditions for every image, the methods were applied to them after PCA processing. As a result, the face recognition accuracy was improved by means of implementing the techniques separately or in combination.
{"title":"An Enhanced Face Recognition Method for Lighting Problem","authors":"Cemil Turan, A. Aitimov, B. Kynabay, Aimoldir Aldabergen","doi":"10.1109/ICECCO48375.2019.9043199","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043199","url":null,"abstract":"One of the most popular tool implemented in face recognition issues is Principal Component Analysis (PCA) which is successfully used in machine learning and data analysis. However, if the images are not regular with some factors that affect the image recognition accuracy such as variation of facial expressions, different poses or lighting problems, this technique may show some deficiencies. In this work, different kinds of methods were implemented by combining different preprocessing techniques to evaluate and compare them under different lighting conditions of images. In order to have the same lighting conditions for every image, the methods were applied to them after PCA processing. As a result, the face recognition accuracy was improved by means of implementing the techniques separately or in combination.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123467664","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043235
R. Jantayev, Y. Amirgaliyev
One of the essential problems in Computer Vision is identification and classification of important objects. While exhaustive work done on image processing for computation and accuracy performance it is still limited by ambiguity. In current work we compared traditional machine learning method versus Deep Learning model, namely Convolutional Neural Network(CNN), on Handwritten Digit Recognition using MNIST dataset. We showed that CNN algorithm reaches higher recognition accuracy than Support Vector Machine(SVM).
{"title":"Improved Handwritten Digit Recognition method using Deep Learning Algorithm","authors":"R. Jantayev, Y. Amirgaliyev","doi":"10.1109/ICECCO48375.2019.9043235","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043235","url":null,"abstract":"One of the essential problems in Computer Vision is identification and classification of important objects. While exhaustive work done on image processing for computation and accuracy performance it is still limited by ambiguity. In current work we compared traditional machine learning method versus Deep Learning model, namely Convolutional Neural Network(CNN), on Handwritten Digit Recognition using MNIST dataset. We showed that CNN algorithm reaches higher recognition accuracy than Support Vector Machine(SVM).","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129598435","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043262
Rukayya Umar, M. Olalere, I. Idris, Raji Abdullahi Egigogo, G. Bolarin
As this paper has expounded, the techniques against DDoS attacks borrow greatly from the already tested traditional techniques. However, no technique has proven to be perfect towards the full detection and prevention of DDoS attacks. Intrusion detection system (IDS) using machine learning approach is one of the implemented solutions against harmful attacks. However, achieving high detection accuracy with minimum false positive rate remains issue that still need to be addressed. Consequently, this study carried out an experimental evaluation on various machine learning algorithms such as Random forest J48, Naïve Bayes, IBK and Multilayer perception on HTTP DDoS attack dataset. The dataset has a total number of 17512 instances which constituted normal (10256) and HTTP DDoS (7256) attack with 21 features. The implemented Performance evaluation revealed that Random Forest algorithm performed best with an accuracy of 99.94% and minimum false positive rate of 0.001%.
{"title":"Performance Evaluation of Machine Learning Algorithms for Hypertext Transfer Protocol Distributed Denial of Service Intrusion Detection","authors":"Rukayya Umar, M. Olalere, I. Idris, Raji Abdullahi Egigogo, G. Bolarin","doi":"10.1109/ICECCO48375.2019.9043262","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043262","url":null,"abstract":"As this paper has expounded, the techniques against DDoS attacks borrow greatly from the already tested traditional techniques. However, no technique has proven to be perfect towards the full detection and prevention of DDoS attacks. Intrusion detection system (IDS) using machine learning approach is one of the implemented solutions against harmful attacks. However, achieving high detection accuracy with minimum false positive rate remains issue that still need to be addressed. Consequently, this study carried out an experimental evaluation on various machine learning algorithms such as Random forest J48, Naïve Bayes, IBK and Multilayer perception on HTTP DDoS attack dataset. The dataset has a total number of 17512 instances which constituted normal (10256) and HTTP DDoS (7256) attack with 21 features. The implemented Performance evaluation revealed that Random Forest algorithm performed best with an accuracy of 99.94% and minimum false positive rate of 0.001%.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130056487","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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