Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099731
Mustafa Mahmoud Ibrahim, F. S. Mubarek
Many problems and accidents are becoming increasingly occurring due to the increased number of vehicles on the streets. Therefore, much research has been submitted to help reduce vehicle problems such as accidents, congestion, and others, such as predicting taxi requests in the regions. Taxis are currently a high percentage of the street's number of vehicles, and if they are directed correctly to their target (passengers), this will contribute to reducing the congestion in the streets. Relying on developed technology such as Vehicular Social networks (VSN) can provide the necessary data for drivers to update their data continuously when there is a network connection. Some previous related works are criticized according to this task. This paper suggests improving taxi demand prediction in the regions based on data preprocessing. This study focuses on a comparison among four machine learning algorithms used for taxi request prediction and finding the best one in terms of execution time and error rates. Finally, Recent data was used for the first three months of 2021 and 2022, where 70% for training and 30% for testing for the year 2021, while the year 2022 is all data for testing. The results show that the Random Forest model outperforms LSTM, ANN, and linear regression in terms of error rates, and it obtained MSE 4.3 * 10−4 and RMSE 2.09 * 10−2.
{"title":"Improving Prediction for taxi demand by using Machine Learning","authors":"Mustafa Mahmoud Ibrahim, F. S. Mubarek","doi":"10.1109/DeSE58274.2023.10099731","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099731","url":null,"abstract":"Many problems and accidents are becoming increasingly occurring due to the increased number of vehicles on the streets. Therefore, much research has been submitted to help reduce vehicle problems such as accidents, congestion, and others, such as predicting taxi requests in the regions. Taxis are currently a high percentage of the street's number of vehicles, and if they are directed correctly to their target (passengers), this will contribute to reducing the congestion in the streets. Relying on developed technology such as Vehicular Social networks (VSN) can provide the necessary data for drivers to update their data continuously when there is a network connection. Some previous related works are criticized according to this task. This paper suggests improving taxi demand prediction in the regions based on data preprocessing. This study focuses on a comparison among four machine learning algorithms used for taxi request prediction and finding the best one in terms of execution time and error rates. Finally, Recent data was used for the first three months of 2021 and 2022, where 70% for training and 30% for testing for the year 2021, while the year 2022 is all data for testing. The results show that the Random Forest model outperforms LSTM, ANN, and linear regression in terms of error rates, and it obtained MSE 4.3 * 10−4 and RMSE 2.09 * 10−2.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124357914","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099689
Palash Aich, Ali Al Ataby, M. Mahyoub, J. Mustafina, Y. Upadhyay
The United States is the second largest producer of apples in the world with an estimated $21 billion downstream revenue. Since agriculture in the USA is highly mechanized, it is critical that latest advancements in technology are always integrated to the agricultural sector to not only improve efficiency but also improve quality, quantity, and to ensure faster distribution. Crop disease hampers the overall agricultural productivity and for a temperature-controlled crop like apple trees, identification of diseases at beginning stage is of paramount importance. There are two ways to identify and rectify issues relating to apple tree diseases, firstly by engaging expert biologists and secondly via automated identification through image processing. The biggest challenges with identification of diseases via biologist are accuracy, time constraints in case of bigger farms and budgetary limits. This research proposes the use of Machine Learning (ML) technique to aid and assist in automated disease detection and identification, and hence, making it affordable. It proposes the use of an ensemble (via weighted average) over single models, thereby improving performance and robustness by utilizing augmentations (positional and colour) which were not present in earlier studies. The proposed work surely creates an impact on the current plant disease diagnosis field by making the classification mode accurate and robust since it reaches accuracy of ~95% for all the classes.
{"title":"Automated Plant Disease Diagnosis in Apple Trees Based on Supervised Machine Learning Model","authors":"Palash Aich, Ali Al Ataby, M. Mahyoub, J. Mustafina, Y. Upadhyay","doi":"10.1109/DeSE58274.2023.10099689","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099689","url":null,"abstract":"The United States is the second largest producer of apples in the world with an estimated $21 billion downstream revenue. Since agriculture in the USA is highly mechanized, it is critical that latest advancements in technology are always integrated to the agricultural sector to not only improve efficiency but also improve quality, quantity, and to ensure faster distribution. Crop disease hampers the overall agricultural productivity and for a temperature-controlled crop like apple trees, identification of diseases at beginning stage is of paramount importance. There are two ways to identify and rectify issues relating to apple tree diseases, firstly by engaging expert biologists and secondly via automated identification through image processing. The biggest challenges with identification of diseases via biologist are accuracy, time constraints in case of bigger farms and budgetary limits. This research proposes the use of Machine Learning (ML) technique to aid and assist in automated disease detection and identification, and hence, making it affordable. It proposes the use of an ensemble (via weighted average) over single models, thereby improving performance and robustness by utilizing augmentations (positional and colour) which were not present in earlier studies. The proposed work surely creates an impact on the current plant disease diagnosis field by making the classification mode accurate and robust since it reaches accuracy of ~95% for all the classes.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121649585","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}
Predicting the criminals' behaviour is a difficult task to accomplish. It is unexpected in most cases and can possibly transpire at any time, which is challenging for police agencies and victims being affected by the offences. The proposed work presents a crime prediction model using the stop & search dataset and the demographic of those charged with possession of a weapon. The study is first of its kind using multiple publicly available datasets to predict the effectiveness of ‘stop & search’ interventions by the police. We employ multiple machine learning algorithms to predict whether a ‘further action’ is required following the stop & search by the police. We utilise several data science techniques mainly including pre-processing, feature engineering and appropriate use of model selection. The proposed model produced 93.20% accuracy using Random Forest classifier. The outcomes of this research can be useful by relevant authorities to anticipate the crime at a specific time and location through the analysis of patterns that will support decision-making and help on deterrent effective strategies to lower offences being committed.
{"title":"Predicting the Effectiveness of ‘Stop and Search’ Police Interventions Using Advanced Data Analytics","authors":"Bradley Marimbire, Abdulaziz Al-Nahari, Waris Khan Ahmadzai, D. Al-Jumeily, Wasiq Khan","doi":"10.1109/DeSE58274.2023.10100242","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100242","url":null,"abstract":"Predicting the criminals' behaviour is a difficult task to accomplish. It is unexpected in most cases and can possibly transpire at any time, which is challenging for police agencies and victims being affected by the offences. The proposed work presents a crime prediction model using the stop & search dataset and the demographic of those charged with possession of a weapon. The study is first of its kind using multiple publicly available datasets to predict the effectiveness of ‘stop & search’ interventions by the police. We employ multiple machine learning algorithms to predict whether a ‘further action’ is required following the stop & search by the police. We utilise several data science techniques mainly including pre-processing, feature engineering and appropriate use of model selection. The proposed model produced 93.20% accuracy using Random Forest classifier. The outcomes of this research can be useful by relevant authorities to anticipate the crime at a specific time and location through the analysis of patterns that will support decision-making and help on deterrent effective strategies to lower offences being committed.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984113","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099735
Mohammad Al-Ameen A. Hameed, Khalid Shaker, H. A. Khalaf
Sentiment analysis extracts people's feelings and attitudes about a certain subject. It has recently received a lot of interest in a variety of applications. In general, the sentiment analysis of healthcare, especially of drug experiences of users, might give substantial importance to how to enhance public health and make sound judgments. In this paper, new approaches have been developed that are based on patient reviews to predict sentiment to improve data analysis. Then, use Term Frequency-Inverse Document Frequency (TF-IDF) to extract the features. The experimental findings show that the Random Forest Classifier (RFC) beats all results of other existing models from the literature in terms of Precision, Recall, F1-Score, and Accuracy of 93 % accuracy.
{"title":"Sentiment Classification of Drug Reviews Using Machine Learning Techniques","authors":"Mohammad Al-Ameen A. Hameed, Khalid Shaker, H. A. Khalaf","doi":"10.1109/DeSE58274.2023.10099735","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099735","url":null,"abstract":"Sentiment analysis extracts people's feelings and attitudes about a certain subject. It has recently received a lot of interest in a variety of applications. In general, the sentiment analysis of healthcare, especially of drug experiences of users, might give substantial importance to how to enhance public health and make sound judgments. In this paper, new approaches have been developed that are based on patient reviews to predict sentiment to improve data analysis. Then, use Term Frequency-Inverse Document Frequency (TF-IDF) to extract the features. The experimental findings show that the Random Forest Classifier (RFC) beats all results of other existing models from the literature in terms of Precision, Recall, F1-Score, and Accuracy of 93 % accuracy.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115885648","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100248
Tan Wen Zheng Ashley, Lim Jo Han, Derrick, Kowit Tan, Rong Kai Tech Avin, Ashlinder Kaur, Sahar Al-Sudani, Zhengkui Wang
The worldwide gaming peripheral market is expanding significantly due to the increasing popularity of online games, and it is predicted that this would increase demand for gaming peripherals. Brand recognition is just the start of the process because many sectors are vying to stand out and wrest mindshare away from rivals. In this paper, we presented a tool named BrandTrend, which enables automated insight discovery for game trending, gaming influencers, and gaming product promotion. The data used in this tool is gathered from social media platforms to analyse gaming contents to match gaming content creators with gaming peripheral brands to promote their brand products via social media. Utilizing data analysis and incorporating evidence from data to build predictions and develop strategies can unambiguously address the issue of distinguish oneself from other rivals and get recognition.
{"title":"BrandTrend: Understanding the Trending Games and Gaming Influencers for Better Gaming Peripheral Promotion","authors":"Tan Wen Zheng Ashley, Lim Jo Han, Derrick, Kowit Tan, Rong Kai Tech Avin, Ashlinder Kaur, Sahar Al-Sudani, Zhengkui Wang","doi":"10.1109/DeSE58274.2023.10100248","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100248","url":null,"abstract":"The worldwide gaming peripheral market is expanding significantly due to the increasing popularity of online games, and it is predicted that this would increase demand for gaming peripherals. Brand recognition is just the start of the process because many sectors are vying to stand out and wrest mindshare away from rivals. In this paper, we presented a tool named BrandTrend, which enables automated insight discovery for game trending, gaming influencers, and gaming product promotion. The data used in this tool is gathered from social media platforms to analyse gaming contents to match gaming content creators with gaming peripheral brands to promote their brand products via social media. Utilizing data analysis and incorporating evidence from data to build predictions and develop strategies can unambiguously address the issue of distinguish oneself from other rivals and get recognition.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125608041","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100274
M. Mahyoub, F. Natalia, S. Sudirman, P. Liatsis, A. Al-Jumaily
Automatic car damage detection and assessment are very useful in alleviating the burden of manual inspection associated with car insurance claims. This will help filter out any frivolous claims that can take up time and money to process. This problem falls into the image classification category and there has been significant progress in this field using deep learning. However, deep learning models require a large number of images for training and oftentimes this is hampered because of the lack of datasets of suitable images. This research investigates data augmentation techniques using Generative Adversarial Networks to increase the size and improve the class balance of a dataset used for training deep learning models for car damage detection and classification. We compare the performance of such an approach with one that uses a conventional data augmentation technique and with another that does not use any data augmentation. Our experiment shows that this approach has a significant improvement compared to another that does not use data augmentation and has a slight improvement compared to one that uses conventional data augmentation.
{"title":"Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection","authors":"M. Mahyoub, F. Natalia, S. Sudirman, P. Liatsis, A. Al-Jumaily","doi":"10.1109/DeSE58274.2023.10100274","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100274","url":null,"abstract":"Automatic car damage detection and assessment are very useful in alleviating the burden of manual inspection associated with car insurance claims. This will help filter out any frivolous claims that can take up time and money to process. This problem falls into the image classification category and there has been significant progress in this field using deep learning. However, deep learning models require a large number of images for training and oftentimes this is hampered because of the lack of datasets of suitable images. This research investigates data augmentation techniques using Generative Adversarial Networks to increase the size and improve the class balance of a dataset used for training deep learning models for car damage detection and classification. We compare the performance of such an approach with one that uses a conventional data augmentation technique and with another that does not use any data augmentation. Our experiment shows that this approach has a significant improvement compared to another that does not use data augmentation and has a slight improvement compared to one that uses conventional data augmentation.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134398254","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099629
Anagha Anil Khaparde, Rik Das, Rupal Bhargava
Mental health of a person plays equivalent significant role in ensuring their wellbeing as their physical health. A great deal of work and e ffort has gone into increasing awareness of this issue. One su ch effort is made by the discipline of computer science, whic h makes use of social media data to give more information in identifying these mental illnesses. People are increasingly usi ng internet platforms to voice our suicide ideas as technology advances quickly. The purpose of the study is to identify a person's indicators of depression based on their social media postings, where users express their feelings and emotions. The goal of this study is to develop three models-Naive Bayes, Pre-Trained Model BERT, and XLNET-and compare their performance in identifying depression from messages on Twitter. These models are pre-processed using the Tweet preprocessor and BERT embeddings, and then the pretrained models are fine-tuned. With an accuracy of 0.9942, it was found that Bert performed better than the other two models.
{"title":"Transformer Based Approach for Depression Detection","authors":"Anagha Anil Khaparde, Rik Das, Rupal Bhargava","doi":"10.1109/DeSE58274.2023.10099629","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099629","url":null,"abstract":"Mental health of a person plays equivalent significant role in ensuring their wellbeing as their physical health. A great deal of work and e ffort has gone into increasing awareness of this issue. One su ch effort is made by the discipline of computer science, whic h makes use of social media data to give more information in identifying these mental illnesses. People are increasingly usi ng internet platforms to voice our suicide ideas as technology advances quickly. The purpose of the study is to identify a person's indicators of depression based on their social media postings, where users express their feelings and emotions. The goal of this study is to develop three models-Naive Bayes, Pre-Trained Model BERT, and XLNET-and compare their performance in identifying depression from messages on Twitter. These models are pre-processed using the Tweet preprocessor and BERT embeddings, and then the pretrained models are fine-tuned. With an accuracy of 0.9942, it was found that Bert performed better than the other two models.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123506863","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100200
Tshun Kong Chan, I. F. Kamsin, S. Amin, N. Zainal
Tamper-proof log files has always been desired in any business settings as it is usually the prime target of bad actors to eliminate their presence in a cyber-attack, while the current log files solutions are mostly insufficient when it comes to practicality and efficiency. The research aims to propose a complete log files solution to prevent hackers from tampering with a system log record using blockchain technology and minimizes the scalability issues of current blockchain-based log files solution with anomaly detection frameworks. The research will focus on gathering data using purposive sampling method by distributing surveys to carefully selected populations to draw conclusions based on the information gathered. In conclusion, the proposed system will feature a blockchain-based log files security solution with anomaly detection built on top to minimize the scalability issues of blockchain technology and to act as a secondary intrusion detection system to achieve defense-in-depth. Future recommendations for the proposed system involve the use of a better anomaly detection framework or more efficient blockchain technology.
{"title":"A Complete Log Files Security Solution Using Anomaly Detection and Blockchain Technology","authors":"Tshun Kong Chan, I. F. Kamsin, S. Amin, N. Zainal","doi":"10.1109/DeSE58274.2023.10100200","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100200","url":null,"abstract":"Tamper-proof log files has always been desired in any business settings as it is usually the prime target of bad actors to eliminate their presence in a cyber-attack, while the current log files solutions are mostly insufficient when it comes to practicality and efficiency. The research aims to propose a complete log files solution to prevent hackers from tampering with a system log record using blockchain technology and minimizes the scalability issues of current blockchain-based log files solution with anomaly detection frameworks. The research will focus on gathering data using purposive sampling method by distributing surveys to carefully selected populations to draw conclusions based on the information gathered. In conclusion, the proposed system will feature a blockchain-based log files security solution with anomaly detection built on top to minimize the scalability issues of blockchain technology and to act as a secondary intrusion detection system to achieve defense-in-depth. Future recommendations for the proposed system involve the use of a better anomaly detection framework or more efficient blockchain technology.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124775598","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100124
Wael Doghri, A. Saddoud, L. Chaari
The concept of structural health monitoring (SHM), which ensures maintenance and conservation of the built environment, is progressively growing in importance. SHM offers the building's historical and cultural value in addition to its safety. Nowadays days, Wireless Sensor Networks (WSN) are frequently employed for SHM and offer a strong contender to address a number of problems, including sensor location. A sensor placement approach is therefore needed considering fragility and significance of the historic structures. In this paper, we propose sensors placement methods applied on the historical monument Aqueduct of Carthage of Tunisia. Our method is based on the Finite Element Modeling (FEM) to carry out the mesh model of the structure arches and to identify two types of the arch zones; stressed and unstressed zones. Based on FEM results, we determine the optimal sensor positions to maximize the covered surface, given a limited number of sensor.
{"title":"Optimal Sensor Placement Strategy for Structural Health Monitoring with Application of the Aqueduct El Hnaya of Carthage","authors":"Wael Doghri, A. Saddoud, L. Chaari","doi":"10.1109/DeSE58274.2023.10100124","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100124","url":null,"abstract":"The concept of structural health monitoring (SHM), which ensures maintenance and conservation of the built environment, is progressively growing in importance. SHM offers the building's historical and cultural value in addition to its safety. Nowadays days, Wireless Sensor Networks (WSN) are frequently employed for SHM and offer a strong contender to address a number of problems, including sensor location. A sensor placement approach is therefore needed considering fragility and significance of the historic structures. In this paper, we propose sensors placement methods applied on the historical monument Aqueduct of Carthage of Tunisia. Our method is based on the Finite Element Modeling (FEM) to carry out the mesh model of the structure arches and to identify two types of the arch zones; stressed and unstressed zones. Based on FEM results, we determine the optimal sensor positions to maximize the covered surface, given a limited number of sensor.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124292235","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099623
Achref Haddaji, S. Ayed, L. Chaari
With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learning-based intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection.
{"title":"A Transfer Learning Based Intrusion Detection System for Internet of Vehicles","authors":"Achref Haddaji, S. Ayed, L. Chaari","doi":"10.1109/DeSE58274.2023.10099623","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099623","url":null,"abstract":"With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learning-based intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122149753","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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