{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T15:10:01Z","timestamp":1755961801418,"version":"3.44.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>Presently, cloud computing stands as a dependable choice for enterprises seeking contemporary, adaptable IT solutions capable of managing vast volumes of business data. Its adoption holds the promise of enhancing operational efficiency and productivity. However, cloud computing remains a dynamic and evolving technology landscape, fraught with inherent security challenges. Malevolent actors perpetually scour for novel methodologies to compromise the integrity of data hosted within cloud environments. For instance, data theft, achieved through downloading or encrypting sensitive information, and Distributed Denial of Service (DDoS) assaults targeting cloud infrastructures, pose persistent threats. To address these pressing concerns, the solution outlined in this article advocates for intrusion detection within cloud environments employing a plethora of classification algorithms. To ensure the precision of outcomes, the proposed approach incorporates the meticulous selection of pertinent attributes from the dataset, leveraging the Boruta algorithm. Our research has demonstrated that combining Boruta with classifiers yields impressive results, achieving a recall of 100% with KNN on the CICIDS 2017 dataset and a precision of 100% with Naive Bayes on the CICDDOS 2019 dataset. These results underscore the significant role of feature selection in enhancing detection performance, affirming its importance for achieving optimal results in intrusion detection systems.<\/jats:p>","DOI":"10.1145\/3736761","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T07:14:49Z","timestamp":1747898089000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance Enhancement of Intrusion Detection System in Cloud by Using Boruta Algorithm"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6418-305X","authenticated-orcid":false,"given":"Oumaima","family":"Lifandali","sequence":"first","affiliation":[{"name":"Faculty of Sciences Ain Chock, Hassan II University of Casablanca","place":["Casablanca, Morocco"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6200-0087","authenticated-orcid":false,"given":"Zouhair","family":"Chiba","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ain Chock, Hassan II University of Casablanca","place":["Casablanca, Morocco"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8429-6712","authenticated-orcid":false,"given":"Noreddine","family":"Abghour","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ain Chock, Hassan II University of Casablanca","place":["Casablanca, Morocco"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7542-9640","authenticated-orcid":false,"given":"Khalid","family":"Moussaid","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ain Chock, Hassan II University of Casablanca","place":["Casablanca, Morocco"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7908-1701","authenticated-orcid":false,"given":"Mounia","family":"Miyara","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ain Chock, Hassan II University of Casablanca","place":["Casablanca, Morocco"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0914-8090","authenticated-orcid":false,"given":"Abdellah","family":"Ouaguid","sequence":"additional","affiliation":[{"name":"2IACS Laboratory, ENSET of Mohammedia, Hassan II University of Casablanca","place":["Mohammedia, Morocco"]}]}],"member":"320","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Y. Kayode Saheed A. Idris Abiodun S. Misra M. Kristiansen Holone and R. Colomo-Palacios. 2022. A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal 61 12 (2022) 9395\u20139409. DOI:10.1016\/J.AEJ.2022.02.063","DOI":"10.1016\/J.AEJ.2022.02.063"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Kunal and M. Dua. 2019. Machine learning approach to IDS: A comprehensive review. In Proceedings of the 2019 3rd International Conference on Electronics Communication and Aerospace Technology (ICECA). 117\u2013121 DOI:10.1109\/ICECA.2019.8822120","DOI":"10.1109\/ICECA.2019.8822120"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"M. B. Kursa A. Jankowski and W. R. Rudnicki. 2010. Boruta\u2013a system for feature selection. FuFundamenta Informaticae 101 4 (2010) 271\u2013285.","DOI":"10.3233\/FI-2010-288"},{"key":"e_1_3_1_5_2","unstructured":"Calibrating Expansion: 2023 Annual Cybersecurity Report (trendmicro.com)"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","unstructured":"K. Srinivasan A. Mubarakali A. S. Alqahtani and A. Dinesh Kumar. 2020. A survey on the impact of DDoS attacks in cloud computing: Prevention detection and mitigation techniques. In Proceedings of the Intelligent Communication Technologies and Virtual Mobile Networks: ICICV 2019. 252\u2013270. DOI:10.1007\/978-3-030-28364-3_24","DOI":"10.1007\/978-3-030-28364-3_24"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","unstructured":"G. K. Saini and G. Somani. 2024. Resource targeted cybersecurity attacks in cloud computing environments. In Proceedings of the Resource Management in Distributed Systems. 169\u2013188. DOI:10.1007\/978-981-97-2644-8_9","DOI":"10.1007\/978-981-97-2644-8_9"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","unstructured":"Kurniabudi D. Stiawan Darmawijoyo M. Y. bin Idris A. M. Bamhdi and R. Budiarto. 2020. CICIDS-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access 8 (2020) 132911\u2013132921. DOI:10.1109\/ACCESS.2020.3009843","DOI":"10.1109\/ACCESS.2020.3009843"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","unstructured":"M. B. Kursa and W. R. Rudnicki. 2010. Feature selection with the boruta package. Journal of Statistical Software 36 11 (2010) 1\u201313. DOI:10.18637\/JSS.V036.I11","DOI":"10.18637\/JSS.V036.I11"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Q. Yang J. Singh and J. Lee. 2019. Isolation-based feature selection for unsupervised outlier detection. In Proceedings of the Annual Conference of the Prognostics and Health Management Society.DOI:10.36001\/PHMCONF.2019.V11I1.824","DOI":"10.36001\/PHMCONF.2019.V11I1.824"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","unstructured":"P. Mishra I. Verma and S. Gupta. 2020. KVMInspector: KVM Based introspection approach to detect malware in cloud environment. Journal of Information Security and Applications 51 (2020) 102460. DOI:10.1016\/J.JISA.2020.102460","DOI":"10.1016\/J.JISA.2020.102460"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","unstructured":"D. Liu Y. Y. Zhang N. Zhang and K. Hu. 2014. A research on KVM-based virtualization security. In Proceedings of the Applied Mechanics and Materials. Trans Tech Publications. DOI:10.4028\/www.scientific.net\/AMM.543-547.3126","DOI":"10.4028\/www.scientific.net\/AMM.543-547.3126"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","unstructured":"M. Prasad S. Tripathi and K. Dahal. 2020. An efficient feature selection based Bayesian and Rough set approach for intrusion detection. Applied Soft Computing 87 (2020) 105980. DOI:10.1016\/J.ASOC.2019.105980","DOI":"10.1016\/J.ASOC.2019.105980"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","unstructured":"J. Gu and S. Lu. 2021. An effective intrusion detection approach using SVM with na\u00efve Bayes feature embedding. Computers and Security 103 (2021) 102158. DOI:10.1016\/J.COSE.2020.102158","DOI":"10.1016\/J.COSE.2020.102158"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","unstructured":"T. Wu H. Fan H. Zhu C. You H. Zhou and X. Huang. 2022. Intrusion detection system combined enhanced random forest with SMOTE algorithm. Eurasip Journal on Advances in Signal Processing 2022 1 (2022) 1\u201320. DOI:10.1186\/S13634-022-00871-6\/TABLES\/6","DOI":"10.1186\/S13634-022-00871-6\/TABLES\/6"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"T. R. Prajwala. 2015. A comparative study on decision tree and random forest using R tool. InternationalJournal of Advanced Research in Computer and Communication Engineering 4 1 (2015) 196\u2013199.","DOI":"10.17148\/IJARCCE.2015.4142"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","unstructured":"P. R. Kannari N. S. Chowdary and R. Laxmikanth Biradar. 2022. An anomaly-based intrusion detection system using recursive feature elimination technique for improved attack detection. Theoretical Computer Science 931 56\u201364. DOI:10.1016\/J.TCS.2022.07.030","DOI":"10.1016\/J.TCS.2022.07.030"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","unstructured":"M. Chater A. Borgi M. T. Slama K. Sfar-Gandoura and M. I. Landoulsi. 2022. Fuzzy isolation forest for anomaly detection. Procedia Computer Science 207 916\u2013925. DOI:10.1016\/J.PROCS.2022.09.147","DOI":"10.1016\/J.PROCS.2022.09.147"},{"key":"e_1_3_1_19_2","unstructured":"Mukesh Kumar Yadav and Mahaiyo Ningshen. 2023. Enhancement of intrusion detection system using machine learning. International Journal of Engineering Research and Technology 12 01 (2023)."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","unstructured":"Y. K. Saheed T. O. Kehinde M. Ayobami Raji and U. A. Baba. 2023. Feature selection in intrusion detection systems: A new hybrid fusion of Bat algorithm and Residue Number System. Journal of Information and Telecommunication 8 2 (2023) 189\u2013207. DOI:10.1080\/24751839.2023.2272484","DOI":"10.1080\/24751839.2023.2272484"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","unstructured":"R. M. Balajee and M. K. Jayanthi Kannan. 2023. Intrusion detection on AWS cloud through hybrid deep learning algorithm. Electronics 12 6 (2023) 1423. DOI:10.3390\/electronics12061423","DOI":"10.3390\/electronics12061423"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","unstructured":"M. A. Umar Z. Chen K. Shuaib and Y. Liu. 2024. Effects of feature selection and normalization on network intrusion detection. Authorea Preprints. DOI:10.36227\/TECHRXIV.12480425.V3","DOI":"10.36227\/TECHRXIV.12480425.V3"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","unstructured":"M. K. Baklizi I. Atoum M. Alkhazaleh H. Kanaker N. Abdullah O. A. Al-Wesabi and A. A. Otoom. 2024. Web attack intrusion detection system using machine learning techniques. International Journal of Online and Biomedical Engineering 20 03 (2024) 24\u201338. DOI:10.3991\/IJOE.V20I03.45249","DOI":"10.3991\/IJOE.V20I03.45249"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","unstructured":"M. Fatima O. Rehman S. Ali and M. F. Niazi. 2024. ELIDS: Ensemble feature selection for lightweight IDS against DDoS attacks in resource-constrained IoT environment. Future Generation Computer Systems 159 (2024) 172\u2013187. DOI:10.1016\/J.FUTURE.2024.05.013","DOI":"10.1016\/J.FUTURE.2024.05.013"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","unstructured":"X. Chen M. Liu Z. Wang and Y. Wang. 2024. Explainable deep learning-based feature selection and intrusion detection method on the Internet of Things. Sensors 24 16 (2024) 5223. DOI:10.3390\/S24165223","DOI":"10.3390\/S24165223"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","unstructured":"H. Gholami A. Mohammadifar S. Golzari D. G. Kaskaoutis and A. L. Collins. 2021. Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran. Aeolian Research 50. DOI:10.1016\/J.AEOLIA.2021.100682","DOI":"10.1016\/J.AEOLIA.2021.100682"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","unstructured":"A. A. Masrur Ahmed R. C. Deo Q. Feng A. Ghahramani N. Raj Z. Yin and L. Yang. 2021. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices rainfall and periodicity. Journal of Hydrology 599 126350. DOI:10.1016\/J.JHYDROL.2021.126350","DOI":"10.1016\/J.JHYDROL.2021.126350"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","unstructured":"N. Farhana A. Firdaus M. F. Darmawan and M. F. Ab Razak. 2023. Evaluation of Boruta algorithm in DDoS detection. Egyptian Informatics Journal 24 1 (2023) 27\u201342. DOI:10.1016\/J.EIJ.2022.10.005","DOI":"10.1016\/J.EIJ.2022.10.005"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","unstructured":"S. Chowdhury and M. P. Schoen. 2020. Research paper classification using supervised machine learning techniques. In Proceedings of the 2020 Intermountain Engineering Technology and Computing (IETC). 1\u20136. DOI:10.1109\/IETC47856.2020.9249211","DOI":"10.1109\/IETC47856.2020.9249211"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","unstructured":"G. Manikandan B. Pragadeesh V. Manojkumar A. L. Karthikeyan R. Manikandan and A. H. Gandomi. 2024. Classification models combined with Boruta feature selection for heart disease prediction. Informatics in Medicine Unlocked 44 101442. DOI:10.1016\/J.IMU.2023.101442","DOI":"10.1016\/J.IMU.2023.101442"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","unstructured":"A. A. Masrur Ahmed R. C. Deo Q. Feng A. Ghahramani N. Raj Z. Yin and L. Yang. 2021. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices rainfall and periodicity. Journal of Hydrology 599. DOI:10.1016\/J.JHYDROL.2021.126350","DOI":"10.1016\/J.JHYDROL.2021.126350"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","unstructured":"H. Gholami A. Mohammadifar S. Golzari D. G. Kaskaoutis and A. L. Collins. 2021. Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran. Aeolian Research 50. DOI:10.1016\/J.AEOLIA.2021.100682","DOI":"10.1016\/J.AEOLIA.2021.100682"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","unstructured":"F. T. Liu K. M. Ting and Z. H. Zhou. 2008. Isolation forest. In Proceedings of the IEEE International Conference on Data Mining ICDM. 413\u2013422. DOI:10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"key":"e_1_3_1_34_2","unstructured":"Irina Rish. 2001. An empirical study of the Na\u00efve bayes classifier. In Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence."},{"key":"e_1_3_1_35_2","unstructured":"Gongde Guo Hui Wang David Bell and Yaxin Bi. 2004. KNN model-based approach in classification."},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","unstructured":"I. Sharafaldin A. H. Lashkari and A. A. Ghorbani. 2018. Toward generating a new intrusion detection dataset and intrusion traffic characterization. - In Proceedings of the 4th International Conference on Information Systems Security and Privacy ICISSP 2018. 108\u2013116. DOI:10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","unstructured":"A. Oyelakin A. O. Ameen T. S. Ogundele T. Salau-Ibrahim U. T. Abdulrauf H. I. Olufadi I. K. Ajiboye S. Muhammad-Thani and I. A. Adeniji. 2023. Overview and exploratory analyses of CICIDS 2017 intrusion detection dataset. Journal of Systems Engineering and Information Technology 2 2 (2023) 45\u201352. DOI:10.29207\/JOSEIT.V2I2.5411","DOI":"10.29207\/JOSEIT.V2I2.5411"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","unstructured":"T. Elmasri N. Samir M. Mashaly and Y. Atef. 2020. Evaluation of CICIDS2017 with qualitative comparison of machine learning algorithm. In Proceedings of the 2020 IEEE Cloud Summit Cloud Summit 2020. 46\u201351. DOI:10.1109\/IEEECLOUDSUMMIT48914.2020.00013","DOI":"10.1109\/IEEECLOUDSUMMIT48914.2020.00013"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","unstructured":"B. Kerim. 2021. Securing IoT network against DDoS attacks using multi-agent IDS. Journal of Physics: Conference Series 1898 1 (2021). DOI:10.1088\/1742-6596\/1898\/1\/012033","DOI":"10.1088\/1742-6596\/1898\/1\/012033"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","unstructured":"I. Sharafaldin A. H. Lashkari S. Hakak and A. A. Ghorbani. 2019. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In Proceedings of the International Carnahan Conference on Security Technology 2019-October. DOI:10.1109\/CCST.2019.8888419","DOI":"10.1109\/CCST.2019.8888419"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","unstructured":"J. F. C. Garcia and G. E. T. Blandon. 2022. A deep learning-based intrusion detection and preventation system for detecting and preventing denial-of-service attacks. IEEE Access 10 (2022) 83043\u201383060. DOI:10.1109\/ACCESS.2022.3196642","DOI":"10.1109\/ACCESS.2022.3196642"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","unstructured":"Z. Chiba N. Abghour K. Moussaid A. EL Omri and M. Rida. 2019. Intelligent and improved self-adaptive anomaly based intrusion detection system for networks. International Journal of Communication Networks and Information Security 11 2 (2019) 312\u2013330. DOI:10.17762\/IJCNIS.V11I2.4144","DOI":"10.17762\/IJCNIS.V11I2.4144"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","unstructured":"D. Aksu S. \u00dcstebay M. A. Aydin and T. Atmaca. 2018. Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. Communications in Computer and Information Science 935 (2018) 141\u2013149. DOI:10.1007\/978-3-030-00840-6_16","DOI":"10.1007\/978-3-030-00840-6_16"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","unstructured":"Y. G. Damtew H. Chen and Z. Yuan. 2023. Heterogeneous ensemble feature selection for network intrusion detection system. International Journal of Computational Intelligence Systems 16 1 (2023) 1\u201325. DOI:10.1007\/S44196-022-00174-6\/TABLES\/9","DOI":"10.1007\/S44196-022-00174-6\/TABLES\/9"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","unstructured":"V. Agarwal. 2015. Research on data preprocessing and categorization technique for smartphone review analysis. International Journal of Computer Applications 131 4 (2015) 30\u201336. DOI:10.5120\/IJCA2015907309","DOI":"10.5120\/IJCA2015907309"}],"container-title":["ACM Transactions on Privacy and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3736761","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T14:47:50Z","timestamp":1755960470000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3736761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,23]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,8,31]]}},"alternative-id":["10.1145\/3736761"],"URL":"https:\/\/doi.org\/10.1145\/3736761","relation":{},"ISSN":["2471-2566","2471-2574"],"issn-type":[{"type":"print","value":"2471-2566"},{"type":"electronic","value":"2471-2574"}],"subject":[],"published":{"date-parts":[[2025,8,23]]},"assertion":[{"value":"2024-07-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}