{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:49:10Z","timestamp":1787064550371,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,21]],"date-time":"2018-06-21T00:00:00Z","timestamp":1529539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains \u201csecurity\u201d, as more and more data get attached to the internet. This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others. XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results. The whole motive is to learn about the integrity of data and have a higher accuracy in the prediction of data. By doing so, the amount of mischievous data floating in a network can be minimized, making the network a secure place to share information. The more secure a network is, the fewer situations where data is hacked or modified. By changing various parameters of the model, future research can be done to get the most out of the data entering and leaving a network. The most important player in the network is data, and getting to know it more closely and precisely is half the work done. Studying data in a network and analyzing the pattern and volume of data leads to the emergence of a solid Intrusion Detection System (IDS), that keeps the network healthy and a safe place to share confidential information.<\/jats:p>","DOI":"10.3390\/info9070149","type":"journal-article","created":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T02:46:21Z","timestamp":1529635581000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":441,"title":["Effective Intrusion Detection System Using XGBoost"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4517-3816","authenticated-orcid":false,"given":"Sukhpreet Singh","family":"Dhaliwal","sequence":"first","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2391-5767","authenticated-orcid":false,"given":"Abdullah-Al","family":"Nahid","sequence":"additional","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7177-2871","authenticated-orcid":false,"given":"Robert","family":"Abbas","sequence":"additional","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,21]]},"reference":[{"key":"ref_1","unstructured":"Leswing, K. (2017, September 09). The Massive Cyberattack that Crippled the Internet. Available online: https:\/\/www.businessinsider.com.au\/amazon-spotify-twitter-githuband-etsy-down-in-apparent-dns-attack-2016-10?r=US&IR=T."},{"key":"ref_2","unstructured":"(2017, September 09). Virtual Machine Escape. Available online: https:\/\/en.wikipedia.org\/wiki\/Virtual_machine_escape."},{"key":"ref_3","unstructured":"Kuranda, S. (2018, April 03). The 20 Coolest Cloud Security Vendors of the 2017 Cloud 100. Available online: https:\/\/www.crn.com\/slide-shows\/security\/300083542\/the-20-coolest-cloud-security-vendors-of-the-2017-cloud-100.htm."},{"key":"ref_4","unstructured":"(2018, March 21). Malware Analysis via Hardware Virtualization Extensions. Available online: http:\/\/ether.gtisc.gatech.edu\/."},{"key":"ref_5","unstructured":"Cisco (2010). Zone-Based Policy Firewall Design and Application Guide, Cisco. Available online: https:\/\/www.cisco.com\/c\/en\/us\/support\/docs\/security\/ios-firewall\/98628-zone-design-guide.html."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ektefa, M., Memar, S., Sidi, F., and Affendey, L.S. (2010, January 17\u201318). Intrusion Detection Using Data Mining Techniques. Proceedings of the 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), Shah Alam, Malaysia.","DOI":"10.1109\/INFRKM.2010.5466919"},{"key":"ref_7","first-page":"316145","article-title":"A Hybrid PSO\/ACO Algorithm for Discovering Classification Rules in Data Mining","volume":"2008","author":"Holden","year":"2008","journal-title":"J. Artif. Evol. Appl."},{"key":"ref_8","unstructured":"Ardjani, F., and Sadouni, K. (2018, March 26). International Journal of Modern Education and Computer Science (IJMECS). Available online: http:\/\/www.mecs-press.org\/ijmecs\/ijmecs-v2-n2\/v2n2-5.html."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.proeng.2012.01.827","article-title":"A Hybrid Intelligent Approach for Network Intrusion Detection","volume":"30","author":"Panda","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_10","unstructured":"Petrussenko, D. (2018, March 26). Incrementally Learning Rules for Anomaly Detection. Available online: http:\/\/cs.fit.edu\/~pkc\/theses\/petrusenko09.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mahoney, M.V., and Chan, P.K. (2003, January 19\u201322). Learning Rules for Anomaly Detection of Hostile Network Traffic. Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, FL, USA.","DOI":"10.1109\/ICDM.2003.1250987"},{"key":"ref_12","unstructured":"Mahoney, M.V., and Chan, P.K. (2018, March 26). Packet Header Anomaly Detection for Identifying Hostile Network Traffic. Available online: https:\/\/cs.fit.edu\/~mmahoney\/paper3.pdf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiang, G., and Min, W. (2010, January 15\u201317). Applying Semi-Supervised Cluster Algorithm for Anomaly Detection. Proceedings of the 3rd International Symposium on Information Processing, Qingdao, China.","DOI":"10.1109\/ISIP.2010.68"},{"key":"ref_14","unstructured":"Wang, Q., and Megalooikonomou, V. (2018, March 26). A Clustering Algorithm for Intrusion Detection. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.59.4965&rep=rep1&type=pdf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mao, C., Lee, H., Parikh, D., Chen, T., and Huang, S. (2009, January 8\u201312). Semi-Supervised Co-Training and Active Learning-Based Approach for Multi-View Intrusion Detection. Proceedings of the 2009 ACM symposium on Applied Computing, Honolulu, HI, USA.","DOI":"10.1145\/1529282.1529735"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chiu, C., Lee, Y., Chang, C., Luo, W., and Huang, H. (2010, January 12\u201314). Semi-Supervised Learning for False Alarm Reduction. Proceedings of the Industrial Conference on Data Mining, Berlin, Germany.","DOI":"10.1007\/978-3-642-14400-4_46"},{"key":"ref_17","unstructured":"Lakshmi, M.N.S., and Radhika, Y.D. (2018, March 26). Effective Approach for Intrusion Detection Using KSVM and R. Available online: http:\/\/www.jatit.org\/volumes\/Vol95No17\/20Vol95No17.pdf."},{"key":"ref_18","unstructured":"Monowar, H.B., Bhattacharyya, D.K., and Kalita, J.K. (2012, January 3\u20135). An Effective Unsupervised Network Anomaly Detection Method. Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Chennai, India."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lane, T. (2006). A Decision Theoretic, Semi-Supervised Model for Intrusion Detection. Machine Learning and Data Mining for Computer Security, Springer.","DOI":"10.1007\/1-84628-253-5_10"},{"key":"ref_20","unstructured":"Zhang, F. (2018, March 26). Multifaceted Defense against Distributed Denial of Service Attacks: Prevention, Detection and Mitigation. Available online: https:\/\/pdfs.semanticscholar.org\/eb7d\/4c742f6cd110d9c96a08e398cc415c3a8518.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, Z., Papatriantafilou, M., and Tsigas, P. (2011, January 21\u201324). CluB: A Cluster-Based Framework for Mitigating Distributed Denial of Service Attacks. Proceedings of the 2011 ACM Symposium on Applied Computing, Taichung, Taiwan.","DOI":"10.1145\/1982185.1982297"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TDSC.2012.18","article-title":"Mitigating Distributed Denial of Service Attacks in Multiparty Applications in the Presence of Clock Drifts","volume":"9","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Depend. Secure Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.compeleceng.2012.05.013","article-title":"Automatic Network Intrusion Detection: Current Techniques and Open Issues","volume":"38","author":"Catania","year":"2012","journal-title":"Comput. Electr. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_25","unstructured":"(2017, November 05). Boosting (Machine Learning). Available online: https:\/\/en.wikipedia.org\/wiki\/Boosting_(machine_learning)."},{"key":"ref_26","unstructured":"Brodley, C., and Utgoff, P. (2018, April 03). Multivariate Decision Trees\u2014Semantic Scholar. Available online: https:\/\/www.semanticscholar.org\/paper\/Multivariate-Decision-Trees-Brodley-Utgoff\/21a84fe894493e9cd805bf64f1dc342d4b5ce17a."},{"key":"ref_27","unstructured":"Brownlee, J. (2018, March 02). A Gentle Introduction to XGBoost for Applied Machine Learning. Available online: http:\/\/machinelearningmastery.com\/gentle-introduction-xgboost-applied-machine-learning\/."},{"key":"ref_28","unstructured":"Reinstein, I. (2018, March 02). XGBoost a Top Machine Learning Method on Kaggle, Explained. Available online: http:\/\/www.kdnuggets.com\/2017\/10\/xgboost-top-machine-learning-method-kaggle-explained.html."},{"key":"ref_29","unstructured":"(2018, March 03). Introduction to Boosted Trees\u2014Xgboost 0.7 Documentation. Available online: http:\/\/xgboost.readthedocs.io\/en\/latest\/model.html."},{"key":"ref_30","unstructured":"(2018, March 16). NSL-KDD | Datasets | Research | Canadian Institute for Cybersecurity | UNB. Available online: http:\/\/www.unb.ca\/cic\/datasets\/nsl.html."},{"key":"ref_31","unstructured":"(2018, March 12). XGBoost Parameters\u2014Xgboost 0.7 Documentation. Available online: http:\/\/xgboost.readthedocs.io\/en\/latest\/parameter.html."},{"key":"ref_32","unstructured":"Brownlee, J. (2018, March 16). Classification Accuracy is not enough: More Performance Measures You Can Use. Available online: https:\/\/machinelearningmastery.com\/classification-accuracy-is-not-enough-more-performance-measures-you-can-use\/."},{"key":"ref_33","unstructured":"Joshi, R. (2018, April 03). Accuracy, Precision, Recall & F1 Score: Interpretation of Performance Measures. Available online: http:\/\/blog.exsilio.com\/all\/accuracy-precision-recall-f1-score-interpretation-of-performance-measures\/."},{"key":"ref_34","unstructured":"(2018, March 16). Plotting and Intrepretating an ROC Curve. Available online: http:\/\/gim.unmc.edu\/dxtests\/roc2.htm."},{"key":"ref_35","unstructured":"(2018, March 26). A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms. Available online: https:\/\/pdfs.semanticscholar.org\/1b34\/80021c4ab0f632efa99e01a9b073903c5554.pdf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Garg, T., and Khurana, S.S. (2014, January 9\u201311). Comparison of Classification Techniques for Intrusion Detection Dataset Using WEKA. Proceedings of the International Conference on Recent Advances and Innovations in Engineering, Jaipur, India.","DOI":"10.1109\/ICRAIE.2014.6909184"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.procs.2016.07.345","article-title":"Feature Analysis, Evaluation and Comparisons of Classification Algorithms Based on Noisy Intrusion Dataset","volume":"92","author":"Hussain","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chauhan, H., Kumar, V., and Pundir, S. (2013, January 24\u201326). A Comparative Study of Classification Techniques for Intrusion Detection. Proceedings of the 2013 International Symposium on Computational and Business Intelligence, New Delhi, India.","DOI":"10.1109\/ISCBI.2013.16"},{"key":"ref_39","unstructured":"(2018, May 10). A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download;jsessionid=7C05CDF892E875A01EDF75C2970CBDB9?doi=10.1.1.680.6760&rep=rep1&type=pdf."},{"key":"ref_40","unstructured":"Yadav, A., and Ingre, B. (2015, January 2\u20133). Performance Analysis of NSL-KDD Dataset Using ANN. Proceedings of the 2015 International Conference on Signal Processing and Communication Engineering Systems, Guntur, India."},{"key":"ref_41","unstructured":"Zygmunt, Z. (2018, May 10). What is Better: Gradient-Boosted Trees, or a Random Forest?\u2014FastML. Available online: http:\/\/fastml.com\/what-is-better-gradient-boosted-trees-or-random-forest\/."},{"key":"ref_42","unstructured":"Validated, C. (2018, May 10). Gradient Boosting Tree vs. Random Forest. Available online: https:\/\/stats.stackexchange.com\/questions\/173390\/gradient-boosting-tree-vs-random-forest."},{"key":"ref_43","unstructured":"(2018, May 10). Why XGBoost? And Why Is It So Powerful in Machine Learning. Available online: http:\/\/www.abzooba.com\/blog\/why-xgboost-and-why-is-it-so-powerful-in-machine-learning\/."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/7\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:09:33Z","timestamp":1760195373000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/7\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,21]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["info9070149"],"URL":"https:\/\/doi.org\/10.3390\/info9070149","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,21]]}}}