Evaluation of Federated Learning in Phishing Email Detection

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 sensit...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 23; číslo 9; s. 4346
Hlavní autoři:	Thapa, Chandra, Tang, Jun Wen, Abuadbba, Alsharif, Gao, Yansong, Camtepe, Seyit, Nepal, Surya, Almashor, Mahathir, Zheng, Yifeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 27.04.2023 MDPI
Témata:	Accuracy Analysis Artificial intelligence Asymmetry bidirectional encoder representations from transformers (BERT) Communication Computational linguistics Cybercrime Data integrity Datasets Deep learning Electronic mail systems federated learning Gas transmission industry Identity theft Language processing Machine learning Natural language interfaces Neural networks Phishing phishing email detection Privacy recurrent neural network United States bidirectional encoder representations from transformers (BERT) phishing email detection recurrent neural network federated learning
ISSN:	1424-8220, 1424-8220
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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.
AbstractList	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. 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.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.
Audience	Academic
Author	Thapa, Chandra Tang, Jun Wen Gao, Yansong Almashor, Mahathir Zheng, Yifeng Camtepe, Seyit Abuadbba, Alsharif Nepal, Surya
AuthorAffiliation	3 Cyber Security Cooperative Research Centre, Australian Capital Territory 2604, Australia 1 Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia 2 School of Chemical Engineering, The University of New South Wales, Sydney 2052, Australia 4 Harbin Institute of Technology, Harbin 150001, China
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Cites_doi	10.1109/JSAC.2022.3213341 10.1109/TDSC.2018.2864993 10.18653/v1/2022.findings-naacl.13 10.1109/ICASSP.2019.8683546 10.1038/s41746-020-00323-1 10.1109/SP.2017.12 10.1109/ACCESS.2019.2913705 10.1007/978-3-319-72395-2_5 10.1007/978-3-030-23551-2_2 10.1109/SP.2019.00065 10.1609/aaai.v29i1.9513 10.1109/SP.2019.00031 10.3233/JCS-2010-0371 10.1016/j.neucom.2019.01.037 10.1109/COMST.2019.2957750 10.1109/TDSC.2022.3208706 10.1561/0400000042 10.1145/1536414.1536440 10.1145/1299015.1299021 10.1145/3359789.3359790 10.1016/j.heliyon.2019.e01802 10.2478/popets-2019-0035 10.1007/s11280-017-0524-3 10.1016/j.dss.2018.01.001 10.1007/978-3-642-33167-1_47
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References	ref_50 ref_13 ref_57 ref_12 ref_56 ref_11 ref_55 ref_10 ref_54 ref_53 ref_52 ref_19 ref_17 ref_16 ref_15 ref_59 ref_61 ref_60 Li (ref_58) 2020; 2 Das (ref_8) 2020; 22 ref_25 ref_24 ref_68 ref_23 ref_67 ref_22 ref_21 ref_65 ref_20 ref_64 ref_63 ref_62 Gao (ref_9) 2018; 21 Dwork (ref_69) 2014; 9 McMahan (ref_14) 2017; 18 ref_29 ref_28 ref_27 ref_26 Fang (ref_6) 2019; 7 Wagh (ref_51) 2019; 2019 ref_36 ref_35 ref_34 ref_33 ref_32 Meng (ref_38) 2019; 337 Zhao (ref_66) 2020; 18 ref_31 Bergholz (ref_40) 2010; 18 ref_30 ref_39 ref_37 Gutierrez (ref_43) 2018; 15 Rieke (ref_18) 2020; 3 Zhang (ref_70) 2022; 40 ref_47 ref_46 ref_44 ref_42 ref_41 ref_1 ref_3 ref_2 Dada (ref_4) 2019; 5 ref_49 ref_48 ref_5 ref_7 Smadi (ref_45) 2018; 107
References_xml	– volume: 40 start-page: 3343 year: 2022 ident: ref_70 article-title: FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2022.3213341 – ident: ref_49 – ident: ref_5 – ident: ref_32 – ident: ref_55 – ident: ref_26 – volume: 15 start-page: 988 year: 2018 ident: ref_43 article-title: Learning from the Ones that Got Away: Detecting New Forms of Phishing Attacks publication-title: IEEE Trans. Dependable Secur. Comput. doi: 10.1109/TDSC.2018.2864993 – volume: 2 start-page: 429 year: 2020 ident: ref_58 article-title: Federated optimization in heterogeneous networks publication-title: Proc. Mach. Learn. Syst. – ident: ref_16 – ident: ref_19 doi: 10.18653/v1/2022.findings-naacl.13 – ident: ref_54 doi: 10.1109/ICASSP.2019.8683546 – volume: 3 start-page: 119 year: 2020 ident: ref_18 article-title: The Future of Digital Health with Federated Learning publication-title: NPJ Digit. Med. doi: 10.1038/s41746-020-00323-1 – ident: ref_42 – ident: ref_61 – ident: ref_1 – ident: ref_35 – ident: ref_50 doi: 10.1109/SP.2017.12 – ident: ref_23 – volume: 7 start-page: 56329 year: 2019 ident: ref_6 article-title: Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2913705 – ident: ref_31 – ident: ref_56 – ident: ref_27 – ident: ref_52 – ident: ref_48 – ident: ref_10 – ident: ref_46 doi: 10.1007/978-3-319-72395-2_5 – ident: ref_17 doi: 10.1007/978-3-030-23551-2_2 – ident: ref_13 – volume: 18 start-page: 2029 year: 2020 ident: ref_66 article-title: Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection publication-title: IEEE Trans. Dependable Secur. Comput. – ident: ref_62 doi: 10.1109/SP.2019.00065 – ident: ref_25 doi: 10.1609/aaai.v29i1.9513 – ident: ref_20 – ident: ref_59 – ident: ref_64 doi: 10.1109/SP.2019.00031 – volume: 18 start-page: 7 year: 2010 ident: ref_40 article-title: New filtering approaches for phishing email publication-title: J. Comput. Secur. doi: 10.3233/JCS-2010-0371 – ident: ref_7 – ident: ref_28 – ident: ref_53 – ident: ref_30 – volume: 337 start-page: 46 year: 2019 ident: ref_38 article-title: Convergence analysis of distributed stochastic gradient descent with shuffling publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.01.037 – ident: ref_3 – ident: ref_24 – ident: ref_34 – volume: 22 start-page: 671 year: 2020 ident: ref_8 article-title: SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective publication-title: Commun. Surv. Tuts. doi: 10.1109/COMST.2019.2957750 – ident: ref_47 – ident: ref_11 – ident: ref_67 doi: 10.1109/TDSC.2022.3208706 – volume: 9 start-page: 211 year: 2014 ident: ref_69 article-title: The Algorithmic Foundations of Differential Privacy publication-title: Found. Trends Theor. Comput. Sci. doi: 10.1561/0400000042 – ident: ref_68 doi: 10.1145/1536414.1536440 – ident: ref_39 doi: 10.1145/1299015.1299021 – ident: ref_65 doi: 10.1145/3359789.3359790 – ident: ref_37 – ident: ref_63 – ident: ref_44 – ident: ref_21 – volume: 5 start-page: e01802 year: 2019 ident: ref_4 article-title: Machine learning for email spam filtering: Review, approaches and open research problems publication-title: Heliyon doi: 10.1016/j.heliyon.2019.e01802 – volume: 18 start-page: 90:1 year: 2017 ident: ref_14 article-title: A survey of Algorithms and Analysis for Adaptive Online Learning publication-title: J. Mach. Learn. Res. – ident: ref_29 – ident: ref_33 – ident: ref_2 – volume: 2019 start-page: 26 year: 2019 ident: ref_51 article-title: SecureNN: 3-Party Secure Computation for Neural Network Training publication-title: PoPETs doi: 10.2478/popets-2019-0035 – ident: ref_12 – volume: 21 start-page: 1759 year: 2018 ident: ref_9 article-title: Resisting re-identification mining on social graph data publication-title: World Wide Web doi: 10.1007/s11280-017-0524-3 – volume: 107 start-page: 88 year: 2018 ident: ref_45 article-title: Detection of online phishing email using dynamic evolving neural network based on reinforcement learning publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2018.01.001 – ident: ref_15 – ident: ref_36 – ident: ref_60 – ident: ref_22 – ident: ref_57 – ident: ref_41 doi: 10.1007/978-3-642-33167-1_47
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SubjectTerms	Accuracy Analysis Artificial intelligence Asymmetry bidirectional encoder representations from transformers (BERT) Communication Computational linguistics Cybercrime Data integrity Datasets Deep learning Electronic mail systems federated learning Gas transmission industry Identity theft Language processing Machine learning Natural language interfaces Neural networks Phishing phishing email detection Privacy recurrent neural network
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Title	Evaluation of Federated Learning in Phishing Email Detection
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