Search

Your search keyword '"Tang, Jun Wen"' showing total 9 results
Start Over Author "Tang, Jun Wen"
9 results on '"Tang, Jun Wen"'

2. Evaluation of Federated Learning in Phishing Email Detection

Author

Thapa, Chandra, Tang, Jun Wen, Abuadbba, Alsharif, Gao, Yansong, Camtepe, Seyit, Nepal, Surya, Almashor, Mahathir, and Zheng, Yifeng

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and legal issues. Moreover, organizations are loathed to share emails, given the risk of leakage of commercially sensitive information. So, it is uncommon to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly Federated Learning (FL), is a desideratum. Already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein is the first to investigate the use of FL in email anti-phishing. This paper builds upon a deep neural network model, particularly RNN and BERT for phishing email detection. It analyzes the FL-entangled learning performance under various settings, including balanced and asymmetrical data distribution. Our results corroborate comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets, and low organization counts. Moreover, we observe a variation in performance when increasing organizational counts. For a fixed total email dataset, the global RNN based model suffers by a 1.8% accuracy drop when increasing organizational counts from 2 to 10. In contrast, BERT accuracy rises by 0.6% when going from 2 to 5 organizations. However, if we allow increasing the overall email dataset with the introduction of new organizations in the FL framework, the organizational level performance is improved by achieving a faster convergence speed. Besides, FL suffers in its overall global model performance due to highly unstable outputs if the email dataset distribution is highly asymmetric., Comment: Submitted for journal publication

Published
2020

6. Evaluation of Federated Learning in Phishing Email Detection.

Author

Thapa, Chandra, Tang, Jun Wen, Abuadbba, Alsharif, Gao, Yansong, Camtepe, Seyit, Nepal, Surya, Almashor, Mahathir, and Zheng, Yifeng

Subjects
*LANGUAGE models, *CONVOLUTIONAL neural networks, *PHISHING, *EMAIL security, *EMAIL, *RECURRENT neural networks, *ARTIFICIAL intelligence
Abstract

The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

7. MACHINE LEARNING FOR SHIP VESSEL CLASSIFICATIONS AUGMENTED WITH SYNTHETIC IMAGES

Author

Tang, Jun Wen, Cristi, Roberto, Fargues, Monique P., and Electrical and Computer Engineering (ECE)

Subjects
Kanade Lucas Tomasi, machine learning, ship vessels classification, synthetic hazy images, KLT
Abstract

Haze conditions have reportedly reduced visibility to about 3km in some of the busiest shipping lanes in the world. Haze conditions, including inclement weather conditions, are identified as a key challenge for autonomous vehicle operations. However, field data on poor weather conditions and ship images under hazy conditions may not be readily available to support research work aimed toward overcoming such challenges for autonomous vehicles. In this thesis, synthetic ship images are rendered under hazy conditions to augment a baseline dataset of haze-free ship images, in order to support our research on ship vessel classifications in a hazy environment using machine learning. The proposed feature extraction involves the counting of corner points detected using the Kanade Lucas Tomasi (KLT) technique to characterize the pattern of specific ship classes and computing of higher-order moments on the color planes on the ship structure detected in the images. Results show that the average ship classification accuracy rate is about 40% higher when the model is trained using a dataset augmented with synthetic hazy ship images; the classifier can classify for ship classes such as container ships, cargo ships, and sailing vessels, with an 80% average accuracy rate. Major, Republic of Singapore Navy Approved for public release. distribution is unlimited

Published
2020

8. MACHINE LEARNING FOR SHIP VESSEL CLASSIFICATIONS AUGMENTED WITH SYNTHETIC IMAGES

Author

Cristi, Roberto, Fargues, Monique P., Electrical and Computer Engineering (ECE), Tang, Jun Wen, Cristi, Roberto, Fargues, Monique P., Electrical and Computer Engineering (ECE), and Tang, Jun Wen

Abstract

Haze conditions have reportedly reduced visibility to about 3km in some of the busiest shipping lanes in the world. Haze conditions, including inclement weather conditions, are identified as a key challenge for autonomous vehicle operations. However, field data on poor weather conditions and ship images under hazy conditions may not be readily available to support research work aimed toward overcoming such challenges for autonomous vehicles. In this thesis, synthetic ship images are rendered under hazy conditions to augment a baseline dataset of haze-free ship images, in order to support our research on ship vessel classifications in a hazy environment using machine learning. The proposed feature extraction involves the counting of corner points detected using the Kanade Lucas Tomasi (KLT) technique to characterize the pattern of specific ship classes and computing of higher-order moments on the color planes on the ship structure detected in the images. Results show that the average ship classification accuracy rate is about 40% higher when the model is trained using a dataset augmented with synthetic hazy ship images; the classifier can classify for ship classes such as container ships, cargo ships, and sailing vessels, with an 80% average accuracy rate.

Published
2020

9. MACHINE LEARNING FOR SHIP VESSEL CLASSIFICATIONS AUGMENTED WITH SYNTHETIC IMAGES

Author

Cristi, Roberto, Fargues, Monique P., Electrical and Computer Engineering (ECE), Tang, Jun Wen, Cristi, Roberto, Fargues, Monique P., Electrical and Computer Engineering (ECE), and Tang, Jun Wen

Abstract

Haze conditions have reportedly reduced visibility to about 3km in some of the busiest shipping lanes in the world. Haze conditions, including inclement weather conditions, are identified as a key challenge for autonomous vehicle operations. However, field data on poor weather conditions and ship images under hazy conditions may not be readily available to support research work aimed toward overcoming such challenges for autonomous vehicles. In this thesis, synthetic ship images are rendered under hazy conditions to augment a baseline dataset of haze-free ship images, in order to support our research on ship vessel classifications in a hazy environment using machine learning. The proposed feature extraction involves the counting of corner points detected using the Kanade Lucas Tomasi (KLT) technique to characterize the pattern of specific ship classes and computing of higher-order moments on the color planes on the ship structure detected in the images. Results show that the average ship classification accuracy rate is about 40% higher when the model is trained using a dataset augmented with synthetic hazy ship images; the classifier can classify for ship classes such as container ships, cargo ships, and sailing vessels, with an 80% average accuracy rate., Major, Republic of Singapore Navy, Approved for public release. distribution is unlimited

Catalog

Books, media, physical & digital resources
See catalog results