{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:04:37Z","timestamp":1743138277358,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819712731"},{"type":"electronic","value":"9789819712748"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-1274-8_5","type":"book-chapter","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:20:02Z","timestamp":1710271202000},"page":"63-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FRAD: Front-Running Attacks Detection on\u00a0Ethereum Using Ternary Classification Model"],"prefix":"10.1007","author":[{"given":"Yuheng","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5213-2871","authenticated-orcid":false,"given":"Pin","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-4182","authenticated-orcid":false,"given":"Guojun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peiqiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wanyi","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Houji","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xuelei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jinyao","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"87790","DOI":"10.1109\/ACCESS.2023.3305325","volume":"11","author":"Y Abdulrahman","year":"2023","unstructured":"Abdulrahman, Y., et al.: AI and blockchain synergy in aerospace engineering: an impact survey on operational efficiency and technological challenges. IEEE Access 11, 87790\u201387804 (2023)","journal-title":"IEEE Access"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Daian, P., et al.: Flash boys 2.0: frontrunning in decentralized exchanges, miner extractable value, and consensus instability. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 910\u2013927. IEEE (2020)","DOI":"10.1109\/SP40000.2020.00040"},{"key":"5_CR3","unstructured":"Piet, J., Fairoze, J., Weaver, N.: Extracting godl [sic] from the salt mines: ethereum miners extracting value. arXiv preprint arXiv:2203.15930 (2022)"},{"key":"5_CR4","unstructured":"Zhang, Z., et al.: Your exploit is mine: instantly synthesizing counterattack smart contract. In: 32nd USENIX Security Symposium (USENIX Security 2023), pp. 1757\u20131774 (2023)"},{"key":"5_CR5","unstructured":"Cernera, F., et al.: Token spammers, rug pulls, and sniper bots: an analysis of the ecosystem of tokens in ethereum and in the Binance smart chain (BNB). In: 32nd USENIX Security Symposium (USENIX Security 2023), pp. 3349\u20133366 (2023)"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Impact and user perception of sandwich attacks in the DeFi ecosystem. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1\u201315 (2022)","DOI":"10.1145\/3491102.3517585"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Ferreira, M.V.X., Parkes, D.C.: Credible decentralized exchange design via verifiable sequencing rules. In: Proceedings of the 55th Annual ACM Symposium on Theory of Computing, pp. 723\u2013736 (2023)","DOI":"10.1145\/3564246.3585233"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Bentov, I., et al.: Tesseract: real-time cryptocurrency exchange using trusted hardware. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1521\u20131538 (2019)","DOI":"10.1145\/3319535.3363221"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Zhou, L., et al.: SoK: decentralized finance (DeFi) attacks. In: 2023 IEEE Symposium on Security and Privacy (SP), pp. 2444\u20132461. IEEE (2023)","DOI":"10.1109\/SP46215.2023.10179435"},{"issue":"11","key":"5_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3570639","volume":"55","author":"J Xu","year":"2023","unstructured":"Xu, J., et al.: SoK: decentralized exchanges (DEX) with automated market maker (AMM) protocols. ACM Comput. Surv. 55(11), 1\u201350 (2023)","journal-title":"ACM Comput. Surv."},{"key":"5_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-030-43725-1_13","volume-title":"Financial Cryptography and Data Security","author":"S Eskandari","year":"2020","unstructured":"Eskandari, S., Moosavi, S., Clark, J.: SoK: transparent dishonesty: front-running attacks on blockchain. In: Bracciali, A., Clark, J., Pintore, F., R\u00f8nne, P.B., Sala, M. (eds.) FC 2019. LNCS, vol. 11599, pp. 170\u2013189. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43725-1_13"},{"key":"5_CR12","unstructured":"Stucke, Z., Constantinides, T., Cartlidge, J.: Simulation of front-running attacks and privacy mitigations in ethereum blockchain. In: 34th European Modeling and Simulation Symposium, EMSS 2022, p. 041. Caltek (2022)"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Weintraub, B., et al.: A flash (bot) in the pan: measuring maximal extractable value in private pools. In: Proceedings of the 22nd ACM Internet Measurement Conference, pp. 458\u2013471 (2022)","DOI":"10.1145\/3517745.3561448"},{"key":"5_CR14","unstructured":"Torres, C.F., Camino, R., et al.: Frontrunner jones and the raiders of the dark forest: an empirical study of frontrunning on the ethereum blockchain. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 1343\u20131359 (2021)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Varun, M., Palanisamy, B., Sural, S.: Mitigating frontrunning attacks in ethereum. In: Proceedings of the Fourth ACM International Symposium on Blockchain and Secure Critical Infrastructure, pp. 115\u2013124 (2022)","DOI":"10.1145\/3494106.3528682"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Struchkov, I., et al.: Agent-Based modeling of blockchain decentralized financial protocols. In: 2021 29th Conference of Open Innovations Association (FRUCT), pp. 337\u2013343. IEEE (2021)","DOI":"10.23919\/FRUCT52173.2021.9435601"},{"key":"5_CR17","unstructured":"Z\u00fcst, P., Nadahalli, T., Wattenhofer, Y.W.R.: Analyzing and preventing sandwich attacks in ethereum. ETH Z\u00fcrich (2021)"},{"key":"5_CR18","unstructured":"Capponi, A., Jia, R., Wang, Y.: The evolution of blockchain: from lit to dark. arXiv preprint arXiv:2202.05779 (2022)"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: Proceedings of the 2018 World Wide Web Conference, pp. 1409\u20131418 (2018)","DOI":"10.1145\/3178876.3186046"},{"key":"5_CR20","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-981-99-0272-9_4","volume-title":"Ubiquitous Security","author":"W Gu","year":"2022","unstructured":"Gu, W., et al.: Detecting unknown vulnerabilities in smart contracts with multi-label classification model using CNN-BiLSTM. In: Wang, G., Choo, K.K.R., Wu, J., Damiani, E. (eds.) UbiSec 2022. CCIS, vol. 1768, pp. 52\u201363. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-99-0272-9_4"},{"key":"5_CR21","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-981-99-0272-9_12","volume-title":"Ubiquitous Security","author":"X Li","year":"2022","unstructured":"Li, X., et al.: Detecting unknown vulnerabilities in smart contracts with binary classification model using machine learning. In: Wang, G., Choo, K.K.R., Wu, J., Damiani, E. (eds.) UbiSec 2022. CCIS, vol. 1768, pp. 179\u2013192. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-99-0272-9_12"},{"issue":"1","key":"5_CR22","first-page":"26","volume":"17","author":"J Wu","year":"2019","unstructured":"Wu, J., et al.: Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electron. Sci. Technol. 17(1), 26\u201340 (2019)","journal-title":"J. Electron. Sci. Technol."},{"issue":"1","key":"5_CR23","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1111\/1759-7714.13204","volume":"11","author":"D Yu","year":"2020","unstructured":"Yu, D., et al.: Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier. Thorac. Cancer 11(1), 95\u2013102 (2020)","journal-title":"Thorac. Cancer"},{"key":"5_CR24","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/978-981-13-1498-8_57","volume-title":"Emerging Technologies in Data Mining and Information Security","author":"N Chakrabarty","year":"2019","unstructured":"Chakrabarty, N., et al.: Flight arrival delay prediction using gradient boosting classifier. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol. 813, pp. 651\u2013659. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-1498-8_57"},{"issue":"1","key":"5_CR25","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/01431160412331269698","volume":"26","author":"M Pal","year":"2005","unstructured":"Pal, M.: Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26(1), 217\u2013222 (2005)","journal-title":"Int. J. Remote Sens."},{"issue":"5","key":"5_CR26","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1109\/TNN.2006.875979","volume":"17","author":"T Windeat","year":"2006","unstructured":"Windeat, T.: Accuracy\/diversity and ensemble MLP classifier design. IEEE Trans. Neural Netw. 17(5), 1194\u20131211 (2006)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"5_CR27","first-page":"120","volume":"710","author":"S Visa","year":"2011","unstructured":"Visa, S., et al.: Confusion matrix-based feature selection. Maics 710(1), 120\u2013127 (2011)","journal-title":"Maics"}],"container-title":["Communications in Computer and Information Science","Ubiquitous Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1274-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T05:15:17Z","timestamp":1731561317000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1274-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819712731","9789819712748"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1274-8_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UbiSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Ubiquitous Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Exeter","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ubisec2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hpcn.exeter.ac.uk\/ubisec2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"MyReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}