[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127734-en":3,"doc-seo-127734-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127734,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features","The research addresses the growing complexity and volume of targeted cyber attacks by improving early prediction and detection for stronger network resilience. It leverages unstructured big data and time-series methods to forecast cyber events rather than relying on limited attention to event forecasting. Using the CSE-CIC-IDS2018 dataset, the study builds tuned time-series models with SMOreg, linear regression, and LSTM, and evaluates them with Naive Bayes and random forest. Best results reach 90.4% using SVM and random forest, assessed with MAE and RMSE, supporting more effective cyber threat detection for critical infrastructure.","information   \nArticle  \nRapid Forecasting of Cyber Events Using Machine Learning-Enabled Features  \nYussuf Ahmed 1, *, Muhammad Ajmal Azad 1 and Taufiq Asyhari 2  \nCitation: Ahmed, Y.; Azad, M.A.; Asyhari, T. Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features. Information 2024, 15, 36. [https://](https://)[ ](https://)[doi.org/10.3390/info15010036](doi.org/10.3390/info15010036)  \nAcademic Editors: Jiaping Gui and Futai Zou  \nReceived: 5 December 2023  \nRevised: 30 December 2023  \nAccepted: 5 January 2024  \nPublished: 11 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School Computing, Birmingham City University, SteamHouse, Belmont Row, Birmingham B4 7RQ, UK; [muhammadajmal.azad@bcu.ac.uk](muhammadajmal.azad@bcu.ac.uk)  \n2 Data Science Indonesia, Monash University, Green Office 9 Building, Jl. BSD Green Office Park, Sampora, Cisauk, Tangerang Regency, Banten 15345, Indonesia; [taufiq.asyhari@monash.edu](taufiq.asyhari@monash.edu)  \n* [Correspondence: yussuf.ahmed@bcu.ac.uk](Correspondence: yussuf.ahmed@bcu.ac.uk)  \nAbstract: In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating potential risks from cyber attacks and network resilience. With the rapid increase of digital data and the increasing complexity of cyber attacks, big data has become a crucial tool for intrusion detection and forecasting. By leveraging the capabilities of unstructured big data, intrusion detection and forecasting systems can become more effective in detecting and preventing cyber attacks and anomalies. While some progress has been made on attack prediction, little attention has been given to forecasting cyber events based on time series and unstructured big data. In this research, we used the CSE-CIC-IDS2018 dataset, a comprehensive dataset containing several attacks on a realistic network. Then we used timeseries forecasting techniques to construct time-series models with tuned parameters to assess the effectiveness of these techniques, which include Sequential Minimal Optimisation for regression (SMOreg), linear regression and Long Short-Term Memory (LSTM) to forecast the cyber events. We used machine learning algorithms such as Naive Bayes and random forest to evaluate the performance of the models. The best performance results of 90.4% were achieved with Support Vector Machine (SVM) and random forest. Additionally, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics were used to evaluate forecasted event performance. SMOreg’s forecasted events yielded the lowest MAE, while those from linear regression exhibited the lowest RMSE. This work is anticipated to contribute to effective cyber threat detection, aiming to reduce security breaches within critical infrastructure.  \nKeywords: forecasting; big data; time series; cyber attack prediction; cyber events; intrusion detection  \n1. Introduction  \nThe threat landscape is dynamic and continuously evolving. It challenges even the best security defences deployed by organisations that invested a significant amount of their budget on security investments. Cybercriminals are finding ways to circumvent these security controls. The vast number of applications used in typical organisations also increases the attack surface due to potential vulnerabilities and the discovery of new software bugs. Security solution providers are also a target of these cyber attacks, as demonstrated by the attack that compromised a global cybersecurity firm, which was comprom","cbCailwiVaH1gZJl","https://ap.wps.com/l/cbCailwiVaH1gZJl","pdf",5610370,2,1,16,"English","en",105,"# Introduction\n## Motivation and evolving threat landscape\n## Big data, machine learning, and predictive security approaches\n## Dataset and proposed forecasting framework","[{\"question\":\"Why is forecasting cyber events important for cybersecurity?\",\"answer\":\"Early prediction and detection help mitigate risks and improve network resilience against ongoing cyber threats and anomalies.\"},{\"question\":\"Which dataset and forecasting models were used?\",\"answer\":\"The study uses the CSE-CIC-IDS2018 dataset and constructs time-series models with tuned parameters including SMOreg, linear regression, and LSTM.\"},{\"question\":\"How were model performance and forecast quality evaluated?\",\"answer\":\"Performance was assessed using machine learning classifiers such as Naive Bayes and random forest, while forecast accuracy used MAE and RMSE metrics.\"}]","Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features | 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is forecasting cyber events important for cybersecurity?","Question",{"text":76,"@type":77},"Early prediction and detection help mitigate risks and improve network resilience against ongoing cyber threats and anomalies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which dataset and forecasting models were used?",{"text":81,"@type":77},"The study uses the CSE-CIC-IDS2018 dataset and constructs time-series models with tuned parameters including SMOreg, linear regression, and LSTM.",{"name":83,"@type":74,"acceptedAnswer":84},"How were model performance and forecast quality evaluated?",{"text":85,"@type":77},"Performance was assessed using machine learning classifiers such as Naive Bayes and random forest, while forecast accuracy used MAE and RMSE 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