[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117312-en":3,"doc-seo-117312-105":29,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117312,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Intelligent Incident Response Systems Using Machine Learning","Machine learning (ML) strengthens cybersecurity by enabling more accurate prediction, detection, and response to evolving cyber threats. ML-driven systems analyse large datasets in near real time, learn patterns and anomalies, and automate response actions that mitigate damage faster than manual, signature-based approaches. The study examines major ML tasks for security operations, including classification, anomaly detection, and natural language processing. It also outlines future directions such as explainable AI, adversarial machine learning, federated learning, and privacy-preserving techniques to build adaptive, trustworthy defenses.","Intelligent Incident Response Systems Using Machine Learning  \nNeibo Augustine Olobo 1, Waliu Adebayo Ayuba 2, Abiamamela Obi-Obuoha 3, Izevbigie Hope Iyobosa 4, Aderemi Ibraheem Adebayo 5, Ishiwu Ifeanyichukwu Jude 6, Chioma Jessica Ifechukwu 7  \n1 Joseph Sarwuan Tarka University Makurdi  \nP. M. B. 2373, Makurdi, Benue State, Nigeria  \n2 Northeastern University  \n360 Huntington Ave, Boston, MA 02115, US  \n3 National Center for Artificial Intelligence and Robotics  \nPlot 790, Alimoh-Abu Street, Behind VIO Yard, Wuye District, Abuja, Nigeria  \n4 Ulster University  \nLouisa Ryland House, 44 Newhall Street, Birmingham, B3 3PL, UK  \n5 University ofIlorin  \nP. M. B. 1515, Ilorin, Kwara State, Nigeria  \n6 Nile University of Nigeria  \nPlot 681, Cadastral Zone C-OO, Research & Institution Area, Jabi Airport Bypass, Abuja FCT, 900001, Nigeria  \n7 Federal University of Lokoja  \nP. M. B. 1154, Main Campus, Felele, Lokoja, Kogi State, Nigeria  \nDOІ: 10.22178/pos.112-13  \nLСC Subject Category: L7-991  \nReceіved 25.11.2024 Accepted 28.12.2024 Publіshed onlіne 31.12.2024  \nCorresponding Author: Neibo Augustine Olobo  \n[neibo.augustine@gmail.com](neibo.augustine@gmail.com)  \n© 2024 The Authors. Thіs artіcle іs lіcensed under a Creatіve Commons Attrіbutіon 4.0 Lіcense   \nAbstract. Machine learning (ML) is revolutionising cybersecurity by enhancing the ability to predict, detect, and respond to cyber threats. By leveraging advanced algorithms, ML systems can analyse vast datasets in real-time, identify patterns, and automate responses, addressing the challenges of increasingly sophisticated cyberattacks. This paper explores the transformative impact of machine learning in cybersecurity, highlighting key tasks such as classification, anomaly detection, and natural language processing. It also discusses future research directions, including explainable AI, adversarial machine learning, federated learning, and privacy-preserving techniques. The cybersecurity community can develop more robust and adaptive defences by focusing on these innovative areas, ensuring a safer digital environment. Integrating machine learning into cybersecurity practices is crucial for navigating the evolving threat landscape and maintaining trust in digital systems.  \nKeywords: Intelligent Incident Response; Machine Learning; Threat Detection; Automated Response; Predictive Analytics.  \nINTRODUCTION  \nIn an increasingly interconnected world, the rise of digital technologies has brought about significant advancements in efficiency and communication. However, this progress has also paved the way for a growing number of cyber threats that pose serious risks to organisations across various sectors. Cyber-attacks are becoming more sophisticated, employing advanced techniques that can easily bypass traditional security measures.  \nConsequently, organisations are finding it increasingly difficult to maintain effective incident response capabilities, leading to potential data breaches, financial losses, and reputational damage. Traditional incident response strategies often rely on static protocols and manual processes, which can be slow and ineffective in rapidly evolving threat environments. Such approaches typically involve identifying incidents based on predefined signatures or rules, which may not account  \nfor novel or previously unseen threats. As a result, organisations face significant challenges in responding promptly and effectively to security incidents, resulting in prolonged downtime and increased vulnerability. Organisations must develop more intelligent and adaptive systems to address these challenges that can enhance incident response capabilities. Intelligent Incident Response Systems (IIRS) harness the power of machine learning (ML) to revolutionise the way organisations manage and mitigate security threats. These systems can learn to identify patterns and anomalies indicative of potential security breaches by leveraging vast historical incident data. This proac","cbCaiiznMRRTmsl9","https://ap.wps.com/l/cbCaiiznMRRTmsl9","pdf",790144,1,14,"English","en",105,"# Introduction\n## Intelligent incident response and the limits of traditional strategies\n## Core ML tasks in incident handling\n## Future research directions","[{\"question\":\"Why are intelligent incident response systems needed in modern cybersecurity?\",\"answer\":\"Cyber threats are increasingly sophisticated and can bypass static security measures, making incident response slower and less effective. Intelligent systems use learning-based approaches to improve speed and accuracy when handling security events.\"},{\"question\":\"Which machine learning tasks are highlighted for improving incident response?\",\"answer\":\"The paper focuses on classification, anomaly detection, and natural language processing as key ML tasks. These methods help analyse large datasets in real time and support more effective threat handling.\"},{\"question\":\"What future research directions are discussed for ML in cybersecurity?\",\"answer\":\"The document discusses explainable AI, adversarial machine learning, federated learning, and privacy-preserving techniques. 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Intelligent systems use learning-based approaches to improve speed and accuracy when handling security events.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning tasks are highlighted for improving incident response?",{"text":80,"@type":76},"The paper focuses on classification, anomaly detection, and natural language processing as key ML tasks. These methods help analyse large datasets in real time and support more effective threat handling.",{"name":82,"@type":73,"acceptedAnswer":83},"What future research directions are discussed for ML in cybersecurity?",{"text":84,"@type":76},"The document discusses explainable AI, adversarial machine learning, federated learning, and privacy-preserving techniques. 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