[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116845-en":3,"doc-seo-116845-105":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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},116845,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","MACHINE LEARNING ALGORITHMS FOR DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION - Article 6","Cyber attacks are expanding globally, pushing businesses to deploy intelligent systems that analyze security and infrastructure logs to detect attacks quickly and automatically. Security analytics using machine learning targets mining security data, but achieving reliable log analytics depends on selecting suitable ML techniques. Large-scale Security Operations Centre environments introduce substantial false positives, making minimization of false alerts a key requirement. Logistic regression is highlighted as a commonly used supervised classification algorithm within ML approaches for cyber threat exposure systems, particularly for threat detection.","International Journal of Smart Sensor and Adhoc Network  \nVolume 3  \nIssue 4 Role of Emerging & Intelligent Article 6  \nTechnologies for the Society.  \nJanuary 2023  \nMACHINE LEARNING ALGORITHMS FOR DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION  \nHari Gonaygunta  \nDEPARTMENT OF INFORMATION TECHNOLOGY, UNIVERSITY OF THE CUMBERLANDS, [hmriet@gmail.com](hmriet@gmail.com)  \nFollow this and additional works at: [https://www.interscience.in/ijssan](https://www.interscience.in/ijssan)  \nRecommended Citation  \nGonaygunta, Hari (2023) \"MACHINE LEARNING ALGORITHMS FOR DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION,\" International Journal of Smart Sensor and Adhoc Network: Vol. 3: Iss. 4, Article 6.  \nDOI: 10.47893/IJSSAN.2023.1229  \nAvailable at: [https://www.interscience.in/ijssan/vol3/iss4/6](https://www.interscience.in/ijssan/vol3/iss4/6)  \nThis Article is brought to you for free and open access by the Interscience Journals at Interscience Research Network. It has been accepted for inclusion in International Journal of Smart Sensor and Adhoc Network by an authorized editor of Interscience Research Network. For more information, please contact [sritampatnaik@gmail.com](sritampatnaik@gmail.com).  \nMACHINE LEARNING ALGORITHMS FOR THE DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION  \n1HariGonaygunta  \n1Dept. ofComputer and Information Sciences, University of the Cumberlands, KY, USA  \n[1](1hmriet@gmail.com)[hmriet@gmail.com](1hmriet@gmail.com)  \nAbstract-The threat of cyber attacks is expanding globally; thus, businesses are developing intelligent artificial intelligence systems that can analyze security and other infrastructure logs from their systems department and quickly and automatically identify cyber attacks. Security analytics based on machine learning the next big thing in cyber security is machine data, which aims to mine security data to show the high maintenance costs of static relationship rules and methods. But, choosing the appropriate machine learning technique for log analytics using ML continues to be a significant barrier to AI success in cyber security due to the possibility of a substantial number of false-positive detections in large-scale or global Security Operations Centre (SOC) settings, selecting the proper machine learning technique for security log analytics remains a substantial obstacle to AI success in cyber security. A machine learning technique for a cyber threat exposure system that can minimize false positives is required. Today's machine learning methods for identifying threats frequently use logistic regression. Logistic regression is the first of three machine learning subcategories—supervised, unsupervised, and reinforcement learning. Any machine learning enthusiast will encounter this supervised machine learning algorithm at the beginning of their machine learning career. It's an essential and often applied classification algorithm.  \nKeywords: SOC, Machine learning, Cyber threats, MLAW, Regression analysis  \nI. Introduction  \nCyber security refers to the policies, defence mechanisms, technologies, and structures put in place to protect programs, data, networks, and computers against illegal access, harm, and cyber threats [1] . The computer network and its applications are one of the fastestgrowing components of Information Communication Technology (ICT); due to this promise, cyber threats are also increasing and gaining ground in the cyber world [2] . Individuals, research institutes, companies, and governments have all suffered significant losses and harm due to cyber threats. Many efforts have been put in place by industries, research institutes, and governments to curb the activities of intruders. Still, all actions  \nmust be revised to handle the intruders [1] .  \nMany enterprises are nearing physical limits when gathering, parsing, normalizing, searching, analyzing, visualizing, and investigating the vast amount of cyber-defence results collected by Security Information an","cbCaicF8sj6m66EN","https://ap.wps.com/l/cbCaicF8sj6m66EN","pdf",304766,1,"English","en",105,"# Introduction\n# ML Analytic workflow (MLAW) for threat detection","[{\"question\":\"Why is machine learning needed for cyber threat detection using security logs?\",\"answer\":\"Cyber attacks are increasing and security logs are noisy, requiring ML models to detect anomalies and identify novel patterns for more timely and accurate threat detection.\"},{\"question\":\"What challenges arise in Security Operations Centre (SOC) settings?\",\"answer\":\"In large-scale SOC environments, selecting ML techniques for security log analytics can produce many false-positive detections, which reduces detection reliability.\"},{\"question\":\"Why is logistic regression emphasized in the document?\",\"answer\":\"The document notes that modern threat identification methods frequently use logistic regression, describing it as an essential and widely applied supervised classification algorithm within ML workflows.\"}]","MACHINE LEARNING ALGORITHMS FOR DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION - 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