乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:10:28Z","timestamp":1730326228987,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,24]]},"DOI":"10.1145\/3551661.3561374","type":"proceedings-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T03:36:44Z","timestamp":1666150604000},"page":"63-70","source":"Crossref","is-referenced-by-count":6,"title":["Classification of Botnet Attacks in IoT Using a Convolutional Neural Network"],"prefix":"10.1145","author":[{"given":"Andressa A.","family":"Cunha","sequence":"first","affiliation":[{"name":"Federal University of Minas Gerais, Belo Horizonte, Brazil"}]},{"given":"Jo\u00e3o B.","family":"Borges","sequence":"additional","affiliation":[{"name":"Federal University of Rio Grande do Norte, Caic\u00f3, Brazil"}]},{"given":"Antonio A. F.","family":"Loureiro","sequence":"additional","affiliation":[{"name":"Federal University of Minas Gerais, Belo Horizonte, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"27","article-title":"Intrusion detection in iot with logistic regression and artificial neural network: further investigations on n-baiot dataset devices","volume":"8","author":"Abbasi Fereshteh","year":"2021","unstructured":"Fereshteh Abbasi , Marjan Naderan , and Seyed Enayatallah Alavi . 2021 . Intrusion detection in iot with logistic regression and artificial neural network: further investigations on n-baiot dataset devices . Journal of Computing and Security , 8 , 27 -- 42 . doi: 10.22108\/jcs.2021.129807.1077. 10.22108\/jcs.2021.129807.1077 Fereshteh Abbasi, Marjan Naderan, and Seyed Enayatallah Alavi. 2021. Intrusion detection in iot with logistic regression and artificial neural network: further investigations on n-baiot dataset devices. Journal of Computing and Security, 8, 27--42. doi: 10.22108\/jcs.2021.129807.1077.","journal-title":"Journal of Computing and Security"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20216336"},{"key":"e_1_3_2_1_3_1","volume-title":"Ibtehal Nafea, Fuad A. Ghaleb, Faisal Saeed, and Maged Nasser.","author":"Alsoufi Muaadh A.","year":"2021","unstructured":"Muaadh A. Alsoufi , Shukor Razak , Maheyzah Md Siraj , Ibtehal Nafea, Fuad A. Ghaleb, Faisal Saeed, and Maged Nasser. 2021 . Anomaly-based intrusion detection systems in iot using deep learning: a systematic literature review. Applied Sciences (Switzerland) , 11. https:\/\/doi.org\/10.3390\/app11188383. 10.3390\/app11188383 Muaadh A. Alsoufi, Shukor Razak, Maheyzah Md Siraj, Ibtehal Nafea, Fuad A. Ghaleb, Faisal Saeed, and Maged Nasser. 2021. Anomaly-based intrusion detection systems in iot using deep learning: a systematic literature review. Applied Sciences (Switzerland), 11. https:\/\/doi.org\/10.3390\/app11188383."},{"key":"e_1_3_2_1_4_1","volume-title":"Marta Cimitile, and Riccardo Pecori.","author":"Aversano Lerina","year":"2021","unstructured":"Lerina Aversano , Mario Luca Bernardi , Marta Cimitile, and Riccardo Pecori. 2021 . ''a systematic review on deep learning approaches for iot. Computer Science Review , 40. https:\/\/doi.org\/10.1016\/j.cosrev.2021.100389. 10.1016\/j.cosrev.2021.100389 Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, and Riccardo Pecori. 2021. ''a systematic review on deep learning approaches for iot. Computer Science Review, 40. https:\/\/doi.org\/10.1016\/j.cosrev.2021.100389."},{"volume-title":"Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems","author":"Geron Aur\u00e9lien","key":"e_1_3_2_1_5_1","unstructured":"Aur\u00e9lien Geron . 2019. Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems . O'Reilly Media, Inc. Aur\u00e9lien Geron. 2019. Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc."},{"key":"#cr-split#-e_1_3_2_1_6_1.1","doi-asserted-by":"crossref","unstructured":"T. Gopalakrishnan D. Ruby Fadi Al-Turjman Deepak Gupta Irina V. Pustokhina Denis A. Pustokhin and K. Shankar. 2020. Deep learning enabled data offloading with cyber attack detection model in mobile edge computing systems. IEEE Access 8. doi: 10.1109\/ACCESS.2020.3030726. 10.1109\/ACCESS.2020.3030726","DOI":"10.1109\/ACCESS.2020.3030726"},{"key":"#cr-split#-e_1_3_2_1_6_1.2","doi-asserted-by":"crossref","unstructured":"T. Gopalakrishnan D. Ruby Fadi Al-Turjman Deepak Gupta Irina V. Pustokhina Denis A. Pustokhin and K. Shankar. 2020. Deep learning enabled data offloading with cyber attack detection model in mobile edge computing systems. IEEE Access 8. doi: 10.1109\/ACCESS.2020.3030726.","DOI":"10.1109\/ACCESS.2020.3030726"},{"key":"e_1_3_2_1_7_1","article-title":"Botnets attack detection using machine learning approach for iot environment","author":"Htwe Chaw Su","year":"2020","unstructured":"Chaw Su Htwe , Yee Mon Thant , and Mie Mie Su Thwin . 2020 . Botnets attack detection using machine learning approach for iot environment . Journal of Physics: Conference Series. https:\/\/iopscience.iop.org\/article\/10.1088\/1742--6596 \/1646\/1\/012101. Chaw Su Htwe, Yee Mon Thant, and Mie Mie Su Thwin. 2020. Botnets attack detection using machine learning approach for iot environment. Journal of Physics: Conference Series. https:\/\/iopscience.iop.org\/article\/10.1088\/1742--6596 \/1646\/1\/012101.","journal-title":"Journal of Physics: Conference Series. https:\/\/iopscience.iop.org\/article\/10.1088\/1742--6596 \/1646\/1\/012101."},{"key":"e_1_3_2_1_8_1","article-title":"Iot botnet detection based on anomalies of multiscale time series dynamics","volume":"10","author":"Hussain Fatima","year":"2022","unstructured":"Fatima Hussain , Rasheed Hussain , Syed Ali Hassan , and Ekram Hossain . 2022 . Iot botnet detection based on anomalies of multiscale time series dynamics . IEEE Transactions on Knowledge and Data Engineering. doi : 10 .1109\/TKDE.202 2.3157636. 10.1109\/TKDE.202 Fatima Hussain, Rasheed Hussain, Syed Ali Hassan, and Ekram Hossain. 2022. Iot botnet detection based on anomalies of multiscale time series dynamics. IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109\/TKDE.202 2.3157636.","journal-title":"IEEE Transactions on Knowledge and Data Engineering. doi"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986444"},{"key":"e_1_3_2_1_10_1","article-title":"Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks","author":"Ganesh Karthik M.","year":"2021","unstructured":"M. Ganesh Karthik and M. B. Mukesh Krishnan . 2021 . Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks . Journal of Ambient Intelligence and Humanized Computing. https:\/\/doi .org\/10.1007\/s12652-021-03082--3. M. Ganesh Karthik and M. B. Mukesh Krishnan. 2021. Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks. Journal of Ambient Intelligence and Humanized Computing. https:\/\/doi .org\/10.1007\/s12652-021-03082--3.","journal-title":"Journal of Ambient Intelligence and Humanized Computing. https:\/\/doi .org\/10.1007\/s12652-021-03082--3."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2017.201"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.05.041"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2972520"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2018.03367731"},{"key":"#cr-split#-e_1_3_2_1_15_1.1","doi-asserted-by":"crossref","unstructured":"Yisroel Mirsky Tomer Doitshman Yuval Elovici and Asaf Shabtai. 2018. Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv:1802.09089. https:\/\/doi.org\/10.48550\/arXiv.1802.09089. 10.48550\/arXiv.1802.09089","DOI":"10.14722\/ndss.2018.23204"},{"key":"#cr-split#-e_1_3_2_1_15_1.2","doi-asserted-by":"crossref","unstructured":"Yisroel Mirsky Tomer Doitshman Yuval Elovici and Asaf Shabtai. 2018. Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv:1802.09089. https:\/\/doi.org\/10.48550\/arXiv.1802.09089.","DOI":"10.14722\/ndss.2018.23204"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00171"},{"key":"#cr-split#-e_1_3_2_1_17_1.1","unstructured":"Keiron O'Shea and Ryan Nash. 2015. An introduction to convolutional neural networks. arXiv:1511.08458. https:\/\/doi.org\/10.48550\/arXiv.1511.08458. 10.48550\/arXiv.1511.08458"},{"key":"#cr-split#-e_1_3_2_1_17_1.2","unstructured":"Keiron O'Shea and Ryan Nash. 2015. An introduction to convolutional neural networks. arXiv:1511.08458. https:\/\/doi.org\/10.48550\/arXiv.1511.08458."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102662"},{"volume-title":"Optimizing deep learning model hyperparameters for botnet attack detection in iot networks","author":"Popoola Segun","key":"e_1_3_2_1_19_1","unstructured":"Segun Popoola , Bamidele Adebisi , Guan Gui , Mohammad Hammoudeh , Haris Gacanin , and Darren Dancey . 2022. Optimizing deep learning model hyperparameters for botnet attack detection in iot networks . IEEE Internet of Things Journal . Segun Popoola, Bamidele Adebisi, Guan Gui, Mohammad Hammoudeh, Haris Gacanin, and Darren Dancey. 2022. Optimizing deep learning model hyperparameters for botnet attack detection in iot networks. IEEE Internet of Things Journal."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2938778"},{"key":"e_1_3_2_1_21_1","volume-title":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE","author":"Tran Thanh Cong","year":"2022","unstructured":"Thanh Cong Tran and Tran Khanh Dang . 2022 . Machine learning for multiclassification of botnet attacks . In 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE , Seoul, Korea. doi: 10.1109\/IMCOM53663. 2022.9721811. 10.1109\/IMCOM53663.2022.9721811 Thanh Cong Tran and Tran Khanh Dang. 2022. Machine learning for multiclassification of botnet attacks. In 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, Seoul, Korea. doi: 10.1109\/IMCOM53663.2022.9721811."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2918437"},{"key":"e_1_3_2_1_23_1","first-page":"1686","article-title":"Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network","volume":"19","author":"Yang Yanqing","year":"2019","unstructured":"Yanqing Yang , Kangfeng Zheng , Chunhua Wu , and Yixian Yang . 2019 . Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network . Sensors (Switzerland) , 19 , 1686 -- 1721 . https:\/\/doi.org\/10.3390\/s19112528. 10.3390\/s19112528 Yanqing Yang, Kangfeng Zheng, Chunhua Wu, and Yixian Yang. 2019. Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors (Switzerland), 19, 1686--1721. https:\/\/doi.org\/10.3390\/s19112528.","journal-title":"Sensors (Switzerland)"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2778504"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICBAIE49996.2020.00027"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Hongpo Zhanga Lulu Huanga Chase Q.Wu and Zhanbo Lia. 2020. An effective convolutional neural network based on smote and gaussian mixture model for intrusion detection in imbalanced dataset. Computer Networks 177. https:\/\/doi .org\/10.1016\/j.comnet.2020.107315. Hongpo Zhanga Lulu Huanga Chase Q.Wu and Zhanbo Lia. 2020. An effective convolutional neural network based on smote and gaussian mixture model for intrusion detection in imbalanced dataset. Computer Networks 177. https:\/\/doi .org\/10.1016\/j.comnet.2020.107315.","DOI":"10.1016\/j.comnet.2020.107315"}],"event":{"name":"MSWiM '22: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","sponsor":["SIGSIM ACM Special Interest Group on Simulation and Modeling"],"location":"Montreal Quebec Canada","acronym":"MSWiM '22"},"container-title":["Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551661.3561374","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T13:29:08Z","timestamp":1666618148000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551661.3561374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":29,"alternative-id":["10.1145\/3551661.3561374","10.1145\/3551661"],"URL":"https:\/\/doi.org\/10.1145\/3551661.3561374","relation":{},"subject":[],"published":{"date-parts":[[2022,10,24]]}}}