Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664772
Zainab Hussam Abdaljabar, O. Ucan, Khattab M. Ali Alheeti
The Internet of Things (IoT) has grown rapidly in recent years, intending to affect everything from everyday life to large industrial systems. Regrettably, this has attracted the attention of hackers, who have turned the Internet of Things into a target for malicious activity, exposing end nodes to attack. IoT devices’ sheer volume and diversity make protecting the IoT infrastructure with a traditional intrusion detection system difficult. So to protect IoT devices, the data flow was investigated in an IoT context to protect these devices from hackers. We used two machine learning classifiers in this work: KNN (K-Nearest Neighbors) and DT (Decision Tree). We calculated the Error Rate, Accuracy, Precision, Recall, and F1 score for each method. When we combined these two classifiers, we obtained outstanding results (100 %). We have a high rate of detection of attacks. The findings are summarized.
{"title":"An Intrusion Detection System for IoT Using KNN and Decision-Tree Based Classification","authors":"Zainab Hussam Abdaljabar, O. Ucan, Khattab M. Ali Alheeti","doi":"10.1109/MTICTI53925.2021.9664772","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664772","url":null,"abstract":"The Internet of Things (IoT) has grown rapidly in recent years, intending to affect everything from everyday life to large industrial systems. Regrettably, this has attracted the attention of hackers, who have turned the Internet of Things into a target for malicious activity, exposing end nodes to attack. IoT devices’ sheer volume and diversity make protecting the IoT infrastructure with a traditional intrusion detection system difficult. So to protect IoT devices, the data flow was investigated in an IoT context to protect these devices from hackers. We used two machine learning classifiers in this work: KNN (K-Nearest Neighbors) and DT (Decision Tree). We calculated the Error Rate, Accuracy, Precision, Recall, and F1 score for each method. When we combined these two classifiers, we obtained outstanding results (100 %). We have a high rate of detection of attacks. The findings are summarized.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133708173","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 : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664774
Bassam Arkok, A. Zeki
This paper aims to classify the Quranic topics that differ in their number of verses by applying the SMOTE technique. SMOTE is used to rebalance samples of minority classes in these Quranic topics. Moreover, SMOTE is combined with many classifiers to choose the best technique for the Quranic classification. Also, the k-values of SMOTE were studied to select the best values for the Quranic datasets and improve the performance of imbalanced classification. The SMOTE was implemented with many classifiers to choose the best one. The results showed that the Voted Perceptron classifier was the best technique when implemented with the SMOTE method to classify the Quranic topics. Also, it is concluded that the best range of K numbers in SMOTE method is [1, 10], to obtain the higher performance of Quranic classification.
{"title":"Classification of Quranic Topics Using SMOTE Technique","authors":"Bassam Arkok, A. Zeki","doi":"10.1109/MTICTI53925.2021.9664774","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664774","url":null,"abstract":"This paper aims to classify the Quranic topics that differ in their number of verses by applying the SMOTE technique. SMOTE is used to rebalance samples of minority classes in these Quranic topics. Moreover, SMOTE is combined with many classifiers to choose the best technique for the Quranic classification. Also, the k-values of SMOTE were studied to select the best values for the Quranic datasets and improve the performance of imbalanced classification. The SMOTE was implemented with many classifiers to choose the best one. The results showed that the Voted Perceptron classifier was the best technique when implemented with the SMOTE method to classify the Quranic topics. Also, it is concluded that the best range of K numbers in SMOTE method is [1, 10], to obtain the higher performance of Quranic classification.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"98-100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114097438","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 : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664783
Mohammed Al-Dowail, A. Al-Hashedi
The emergency department is the most critical in the hospital. It has a high level of complexity because of the admission of patients with a wide range of diseases and various urgent cases, resulting in a variety of issues such as overcrowding, extended waiting periods, and inefficient resources utilization. Process mining is a new business intelligence framework that focuses on analyzing processes by extracting knowledge from the event log. This paper aims to introduce a method for analyzing emergency department processes using process mining techniques. It is an extension and based on previous methods, with additional phases that suit the complexity of the emergency environment, as well as involve the stakeholder in most phases. It will help you understand the varied patient pathways taken by different groups of patients as well as offer insight into bottlenecks. As a result, the procedures become more efficient.
{"title":"Stakeholders-Driven Process Mining Method for Analyzing Emergency Department Processes","authors":"Mohammed Al-Dowail, A. Al-Hashedi","doi":"10.1109/MTICTI53925.2021.9664783","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664783","url":null,"abstract":"The emergency department is the most critical in the hospital. It has a high level of complexity because of the admission of patients with a wide range of diseases and various urgent cases, resulting in a variety of issues such as overcrowding, extended waiting periods, and inefficient resources utilization. Process mining is a new business intelligence framework that focuses on analyzing processes by extracting knowledge from the event log. This paper aims to introduce a method for analyzing emergency department processes using process mining techniques. It is an extension and based on previous methods, with additional phases that suit the complexity of the emergency environment, as well as involve the stakeholder in most phases. It will help you understand the varied patient pathways taken by different groups of patients as well as offer insight into bottlenecks. As a result, the procedures become more efficient.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184646","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 : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664776
Z. Abbood, D. Atilla, Ç. Aydin, Mahmoud Shuker Mahmoud
This advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.
{"title":"A Survey on Intrusion Detection System in Ad Hoc Networks Based on Machine Learning","authors":"Z. Abbood, D. Atilla, Ç. Aydin, Mahmoud Shuker Mahmoud","doi":"10.1109/MTICTI53925.2021.9664776","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664776","url":null,"abstract":"This advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116824569","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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