Bashar Mohammad Abdallah Qasaimeh, A. Abdallah, S. Ratté
Depression is very common among patients with Alzheimer's while identifying depression in patients with Alzheimer's can be difficult, since dementia can cause some of the same symptoms. The related work in deep learning and machine learning proposed classification models that assist in detecting depression. However, classifying Alzheimer patients into depressive and non-depressive is not an easy task. Therefore, the objective of this research paper is to establish a starting point to use Artificial Neural Networks (ANN) to classify Alzheimer patients into depressive and non-depressive using speech analysis. The research paper proposes an analysis of the performance rates (accuracy, recall, precision) for ANN. The analysis performs three experiments and compare the performance rates among selected audio features. Our classification model shows promising classification results: the classification accuracy is ranged between 72.5% and 77.1%. This result provides a positive indication that ANN can assist the medical communities in future research. This could be accomplished by developing the feature extraction process, choosing the appropriate data and audio features, and developing the classification methods.
{"title":"Detecting Depression in Alzheimer and MCI Using Artificial Neural Networks (ANN)","authors":"Bashar Mohammad Abdallah Qasaimeh, A. Abdallah, S. Ratté","doi":"10.1145/3460620.3460765","DOIUrl":"https://doi.org/10.1145/3460620.3460765","url":null,"abstract":"Depression is very common among patients with Alzheimer's while identifying depression in patients with Alzheimer's can be difficult, since dementia can cause some of the same symptoms. The related work in deep learning and machine learning proposed classification models that assist in detecting depression. However, classifying Alzheimer patients into depressive and non-depressive is not an easy task. Therefore, the objective of this research paper is to establish a starting point to use Artificial Neural Networks (ANN) to classify Alzheimer patients into depressive and non-depressive using speech analysis. The research paper proposes an analysis of the performance rates (accuracy, recall, precision) for ANN. The analysis performs three experiments and compare the performance rates among selected audio features. Our classification model shows promising classification results: the classification accuracy is ranged between 72.5% and 77.1%. This result provides a positive indication that ANN can assist the medical communities in future research. This could be accomplished by developing the feature extraction process, choosing the appropriate data and audio features, and developing the classification methods.","PeriodicalId":36824,"journal":{"name":"Data","volume":"33 7 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82776216","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}
This study provides a review of solar energy in Iraq, as Iraq is one of the oil-rich countries that are considered more intelligent in the field of alternative energy for the post-oil era, especially as it is located near the solar belt, which makes the solar radiation of high intensity and brightness a period of the year. We must replace non-renewable energy resources (traditional or fossil fuels) with renewable (sustainable) energy resources. This renewable energy (solar energy) is possible, clean, unlimited and environmentally friendly, and it can be used in many applications of lighting, water heating and heating in the winter, and this Reduces the electricity needed during winter for this application. And if we talk about Mosul, the climate of Mosul It’s marked by high summer and cold temperatures .degrees Celsius in the middle of the day, and in December, January and February, temperatures range from -1 degrees Celsius to 8 degrees Celsius. As Iraq is suffering from electricity shortages during this time, so the Iraqi government and the local government of Mosul in particular must take serious decisions and steps to confront and overcome these challenges, and develop developed strategies, and programs of specialized and expert people to meet the increases in demands on electric power. Renewable energies such as solar and wind energy which could play a significant role in Iraq’s future , especially the solar energy covered in this study.
{"title":"Reviews of using solar energy to cover the energy deficit after the recent war in Mosul city","authors":"Zozan Hussain, Z. Dallalbashi, Shaymaa Alhayali","doi":"10.1145/3460620.3460766","DOIUrl":"https://doi.org/10.1145/3460620.3460766","url":null,"abstract":"This study provides a review of solar energy in Iraq, as Iraq is one of the oil-rich countries that are considered more intelligent in the field of alternative energy for the post-oil era, especially as it is located near the solar belt, which makes the solar radiation of high intensity and brightness a period of the year. We must replace non-renewable energy resources (traditional or fossil fuels) with renewable (sustainable) energy resources. This renewable energy (solar energy) is possible, clean, unlimited and environmentally friendly, and it can be used in many applications of lighting, water heating and heating in the winter, and this Reduces the electricity needed during winter for this application. And if we talk about Mosul, the climate of Mosul It’s marked by high summer and cold temperatures .degrees Celsius in the middle of the day, and in December, January and February, temperatures range from -1 degrees Celsius to 8 degrees Celsius. As Iraq is suffering from electricity shortages during this time, so the Iraqi government and the local government of Mosul in particular must take serious decisions and steps to confront and overcome these challenges, and develop developed strategies, and programs of specialized and expert people to meet the increases in demands on electric power. Renewable energies such as solar and wind energy which could play a significant role in Iraq’s future , especially the solar energy covered in this study.","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82985365","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}
Arun Nagaraja, Soumya K N, Anubhav Sinha, Jain Vinay RAJENDRA KUMAR, Prajwal S Nayak
The paper is about the detection of unauthenticated news using Machine-learning methods with different algorithms. There is lot of scope to check the reality of the news received from various sources like websites, blogs, e-content. To identify the fake news, there is a need of some application in real time. Many methods were proposed earlier to observe fake news such as style-based, propagation-based and user-based. Automatic fake news detection application can be generated using natural language processing, information retrieval techniques, as well as graph theory. Language modeling is used to predict the missing or next word in a sentence based on the context. It is believed that mainstream media platforms are publishing fake news to grasp the attention of readers; most likely, it is done to increase the number of visitors on that particular page so that with an increasing number of visitors the page could claim more advertisement. This paper proposes an efficient method to detect fake news with better accuracy by using the available data set to detect the news is FAKE or REAL. Various methods are used for collecting the data and the data mining techniques are applied to clean and visualize it. Data mining helps to differentiate between the qualities of data depending upon its properties. The performance of detecting news only from the body of news is not sufficient but also social engagements should be considered. The objective of the work is to provide end-users with a robust solution so that they can figure out phishy and misguiding information. This technique combines the title and the body of the news to predict fake news more efficiently. The application is concerned with finding a result that could be used to identify fake news to help users.
{"title":"Fake News Detection Using Machine Learning Methods","authors":"Arun Nagaraja, Soumya K N, Anubhav Sinha, Jain Vinay RAJENDRA KUMAR, Prajwal S Nayak","doi":"10.1145/3460620.3460753","DOIUrl":"https://doi.org/10.1145/3460620.3460753","url":null,"abstract":"The paper is about the detection of unauthenticated news using Machine-learning methods with different algorithms. There is lot of scope to check the reality of the news received from various sources like websites, blogs, e-content. To identify the fake news, there is a need of some application in real time. Many methods were proposed earlier to observe fake news such as style-based, propagation-based and user-based. Automatic fake news detection application can be generated using natural language processing, information retrieval techniques, as well as graph theory. Language modeling is used to predict the missing or next word in a sentence based on the context. It is believed that mainstream media platforms are publishing fake news to grasp the attention of readers; most likely, it is done to increase the number of visitors on that particular page so that with an increasing number of visitors the page could claim more advertisement. This paper proposes an efficient method to detect fake news with better accuracy by using the available data set to detect the news is FAKE or REAL. Various methods are used for collecting the data and the data mining techniques are applied to clean and visualize it. Data mining helps to differentiate between the qualities of data depending upon its properties. The performance of detecting news only from the body of news is not sufficient but also social engagements should be considered. The objective of the work is to provide end-users with a robust solution so that they can figure out phishy and misguiding information. This technique combines the title and the body of the news to predict fake news more efficiently. The application is concerned with finding a result that could be used to identify fake news to help users.","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82999789","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}
Arun Nagaraja, U. Boregowda, V. Radhakrishna, R. Gunupudi
Identifying intrusion in networks is one of the important concerns in computer networks. The task of dimensionality reduction and choice of classifier plays an important role in network intrusion detection. Dimensionality reduction should make sure that the efficacy of classifier on reduced dimensionality data is atleast retained if not improved. In this paper, we suggest a similarity function which can be used to find similarity between any two network elements expressed as vectors. The similarity measure is designed to make sure that the attribute distribution is taken into account for finding similarity value.
{"title":"Design of Gaussian Similarity Measure for Network Anomaly Detection","authors":"Arun Nagaraja, U. Boregowda, V. Radhakrishna, R. Gunupudi","doi":"10.1145/3460620.3460759","DOIUrl":"https://doi.org/10.1145/3460620.3460759","url":null,"abstract":"Identifying intrusion in networks is one of the important concerns in computer networks. The task of dimensionality reduction and choice of classifier plays an important role in network intrusion detection. Dimensionality reduction should make sure that the efficacy of classifier on reduced dimensionality data is atleast retained if not improved. In this paper, we suggest a similarity function which can be used to find similarity between any two network elements expressed as vectors. The similarity measure is designed to make sure that the attribute distribution is taken into account for finding similarity value.","PeriodicalId":36824,"journal":{"name":"Data","volume":"62 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83105021","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}
Amal Saif, Hamzeh Al-Kilani, Malik Qasaimeh, Abdullah Al-Refai
As Android is one of the most popular mobile open-source platforms, it is very important to ensure the security and privacy of Android apps. Android has an authorization system that allows developers to announce their applications requiring essential services, and when running such applications, users need to comply with these requirements. Users frequently download applications, giving them infinite permissions easily without thinking about the impact on their privacy. In this paper, we analyze 222 applications manually to grant these permissions to see how they are compatible with user privacy depending on many criteria.
{"title":"Analysis of Android Applications Permissions","authors":"Amal Saif, Hamzeh Al-Kilani, Malik Qasaimeh, Abdullah Al-Refai","doi":"10.1145/3460620.3460764","DOIUrl":"https://doi.org/10.1145/3460620.3460764","url":null,"abstract":"As Android is one of the most popular mobile open-source platforms, it is very important to ensure the security and privacy of Android apps. Android has an authorization system that allows developers to announce their applications requiring essential services, and when running such applications, users need to comply with these requirements. Users frequently download applications, giving them infinite permissions easily without thinking about the impact on their privacy. In this paper, we analyze 222 applications manually to grant these permissions to see how they are compatible with user privacy depending on many criteria.","PeriodicalId":36824,"journal":{"name":"Data","volume":"17 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81972080","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}
Dimensionality reduction is usually obtained by applying some of the most well unknown methods such as principal component analysis, singular value decomposition, feature selection algorithms which are based on information gain, Gini index etc. The objective behind achievement of dimensionality reduction is reducing computational complexity and at the same time aiming to attain better performance by learning algorithms which may perform supervised or unsupervised learning. In this paper, we present a feature clustering similarity function for dimensionality reduction so that the eventual reduced dataset may be used to reduce the computational complexity and also result better classifier evaluation results interms of accuracy, precision etc.
{"title":"Fuzzy Feature Similarity Functions for Feature Clustering and Dimensionality Reduction","authors":"Arun Nagaraja, U. Boregowda, V. Radhakrishna","doi":"10.1145/3460620.3460758","DOIUrl":"https://doi.org/10.1145/3460620.3460758","url":null,"abstract":"Dimensionality reduction is usually obtained by applying some of the most well unknown methods such as principal component analysis, singular value decomposition, feature selection algorithms which are based on information gain, Gini index etc. The objective behind achievement of dimensionality reduction is reducing computational complexity and at the same time aiming to attain better performance by learning algorithms which may perform supervised or unsupervised learning. In this paper, we present a feature clustering similarity function for dimensionality reduction so that the eventual reduced dataset may be used to reduce the computational complexity and also result better classifier evaluation results interms of accuracy, precision etc.","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82348202","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}
The reduction of bipartite clique enumeration problem into a clique enumeration problem is a well-known approach for extracting maximal bipartite cliques. In this approach, the graph inflation is used to transform a bipartite graph to a general graph, then any maximal clique enumeration algorithm can be used. However, between every two vertices (in the same set), the traditional inflation algorithm adds a new edge. Therefore incurring high computation overhead, which is impractical and cannot be scaled up to handle large graphs. This paper proposes a new algorithm for extracting maximal bipartite cliques based on an efficient graph inflation algorithm. The proposed algorithm adds the minimal number of edges that are required to convert all maximal bipartite cliques to maximal cliques. The proposed algorithm has been evaluated, using different real world benchmark graphs, according to the correctness of the algorithm, running time (in the inflation and enumeration steps), and according to the overhead of the inflation algorithm on the size of the generated general graph. The empirical evaluation proves that the proposed algorithm is accurate, efficient, effective, and applicable to real world graphs more than the traditional algorithm.
{"title":"An Effective Algorithm for Extracting Maximal Bipartite Cliques","authors":"Raghda Fawzey Hriez, Ghazi Al-Naymat, A. Awajan","doi":"10.1145/3460620.3460735","DOIUrl":"https://doi.org/10.1145/3460620.3460735","url":null,"abstract":"The reduction of bipartite clique enumeration problem into a clique enumeration problem is a well-known approach for extracting maximal bipartite cliques. In this approach, the graph inflation is used to transform a bipartite graph to a general graph, then any maximal clique enumeration algorithm can be used. However, between every two vertices (in the same set), the traditional inflation algorithm adds a new edge. Therefore incurring high computation overhead, which is impractical and cannot be scaled up to handle large graphs. This paper proposes a new algorithm for extracting maximal bipartite cliques based on an efficient graph inflation algorithm. The proposed algorithm adds the minimal number of edges that are required to convert all maximal bipartite cliques to maximal cliques. The proposed algorithm has been evaluated, using different real world benchmark graphs, according to the correctness of the algorithm, running time (in the inflation and enumeration steps), and according to the overhead of the inflation algorithm on the size of the generated general graph. The empirical evaluation proves that the proposed algorithm is accurate, efficient, effective, and applicable to real world graphs more than the traditional algorithm.","PeriodicalId":36824,"journal":{"name":"Data","volume":"422 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84929347","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}
M. S. Tabares, Paola Vallejo-Correa, Alex Montoya, Jose D. Sanchez, Daniel Correa
Understanding learner behavior is the key to the success of any learning process. The more we know about learners, the more likely we are to personalize learning experiences and provide successful feedback. This paper presents a feedback rules model called SECA: (i) Scenario, that defines the context behavior in a microlearning environment, (ii) Event, provided by a predictive model, (iii) Condition, that evaluates the events, and (iv) Action, that provides the learner’s feedback. The proposal is achieved through a controlled experiment in which a microlearning environment is available to collect data from a ubiquitous context, and predictive analytics are applied to guide the definition of a set of feedback rules intended to support the learner’s learning process. In the end, we presented an exemplified set of feedback rules, which could be used to provide automatic recommendations and improve the learner experience. Thus, the experiment allows us to analyze the learner behavior in a ubiquitous microlearning context from a feedback perspective.
{"title":"SECA: A Feedback Rules Model in a Ubiquitous Microlearning Context","authors":"M. S. Tabares, Paola Vallejo-Correa, Alex Montoya, Jose D. Sanchez, Daniel Correa","doi":"10.1145/3460620.3460745","DOIUrl":"https://doi.org/10.1145/3460620.3460745","url":null,"abstract":"Understanding learner behavior is the key to the success of any learning process. The more we know about learners, the more likely we are to personalize learning experiences and provide successful feedback. This paper presents a feedback rules model called SECA: (i) Scenario, that defines the context behavior in a microlearning environment, (ii) Event, provided by a predictive model, (iii) Condition, that evaluates the events, and (iv) Action, that provides the learner’s feedback. The proposal is achieved through a controlled experiment in which a microlearning environment is available to collect data from a ubiquitous context, and predictive analytics are applied to guide the definition of a set of feedback rules intended to support the learner’s learning process. In the end, we presented an exemplified set of feedback rules, which could be used to provide automatic recommendations and improve the learner experience. Thus, the experiment allows us to analyze the learner behavior in a ubiquitous microlearning context from a feedback perspective.","PeriodicalId":36824,"journal":{"name":"Data","volume":"31 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74916095","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}
With the influence of diverse architectures like ImageNet, VGGNet, ResNet for detection of objects in images, we are proposing a novel architecture for detection of text in video. It is challenging to detect text candidates due to its nature of properties that varies from normal objects in terms of contours, connectionist, size, scaling to motion occlusion, color contrast, poor illumination, etc. Also, it is not possible to apply the existing architecture for the proposed anatomy with incompatibility in targets, parameters. Hence, working on video takes different path of learning and validation. The proposed architecture reads the temporal data to train the sequence of learning features. These features are fed to periodic connectionist to learn successive features to obtain the text candidate. Later, representation of the features are fed to regional proposal network to obtain the regions of interest by comparing with the ground-truth data followed by pooling the text regions with bounding box and finding the probability of their occurrence. The proposed structure evaluated on an ICDAR 2013 “Text in Video” dataset of different indoor and outdoor videos achieves high detection rates and performed better than labeled features.
{"title":"Detection of Text from Video with Customized Trained Anatomy","authors":"Manasa Devi Mortha, S. Maddala, V. Raju","doi":"10.1145/3460620.3460623","DOIUrl":"https://doi.org/10.1145/3460620.3460623","url":null,"abstract":"With the influence of diverse architectures like ImageNet, VGGNet, ResNet for detection of objects in images, we are proposing a novel architecture for detection of text in video. It is challenging to detect text candidates due to its nature of properties that varies from normal objects in terms of contours, connectionist, size, scaling to motion occlusion, color contrast, poor illumination, etc. Also, it is not possible to apply the existing architecture for the proposed anatomy with incompatibility in targets, parameters. Hence, working on video takes different path of learning and validation. The proposed architecture reads the temporal data to train the sequence of learning features. These features are fed to periodic connectionist to learn successive features to obtain the text candidate. Later, representation of the features are fed to regional proposal network to obtain the regions of interest by comparing with the ground-truth data followed by pooling the text regions with bounding box and finding the probability of their occurrence. The proposed structure evaluated on an ICDAR 2013 “Text in Video” dataset of different indoor and outdoor videos achieves high detection rates and performed better than labeled features.","PeriodicalId":36824,"journal":{"name":"Data","volume":"22 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85566133","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}
Abdul Hanan K. Mohammed, Hrag-Harout Jebamikyous, Dina Nawara, R. Kashef
A cyber-attack is precautious manipulation of computer systems and networks using malware to conciliate data or restrict processes or operations. These types of attacks are vastly growing over the years. This increase in structure and complexity calls for advanced innovation in defensive strategies and detection. Traditional approaches for detecting cyber-attacks suffer from low efficiency, especially with the high demands of increasing security threats. With the substitutional increase of computational power, machine learning and deep learning methods are considered significant solutions for defending and detecting those threats or attacks. In this paper, we performed a comparative analysis of IoT cyberattack detection methods. We utilized six different algorithms including, Random Forest, Logistic Regression, SVM, NB, KNN, and MLP. Each model is evaluated using precision, recall, F-score, and ROC.
{"title":"IoT Cyber-Attack Detection: A Comparative Analysis","authors":"Abdul Hanan K. Mohammed, Hrag-Harout Jebamikyous, Dina Nawara, R. Kashef","doi":"10.1145/3460620.3460742","DOIUrl":"https://doi.org/10.1145/3460620.3460742","url":null,"abstract":"A cyber-attack is precautious manipulation of computer systems and networks using malware to conciliate data or restrict processes or operations. These types of attacks are vastly growing over the years. This increase in structure and complexity calls for advanced innovation in defensive strategies and detection. Traditional approaches for detecting cyber-attacks suffer from low efficiency, especially with the high demands of increasing security threats. With the substitutional increase of computational power, machine learning and deep learning methods are considered significant solutions for defending and detecting those threats or attacks. In this paper, we performed a comparative analysis of IoT cyberattack detection methods. We utilized six different algorithms including, Random Forest, Logistic Regression, SVM, NB, KNN, and MLP. Each model is evaluated using precision, recall, F-score, and ROC.","PeriodicalId":36824,"journal":{"name":"Data","volume":"40 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81842816","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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