[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119866-en":3,"doc-seo-119866-105":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},119866,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Cyber security Reinforcement through Firewall Log Analysis and Machine Learning","Firewalls provide primary protection for network security by limiting risks from external vulnerabilities and internal breaches. This study introduces a framework that uses firewall log data to classify incoming packets as permitted or forbidden. Data from a CS&IT department is cleaned and prepared via missing-value handling, categorical encoding, numerical standardization, and coherence checks. Multiple machine learning models address class imbalance and are evaluated using accuracy, precision, recall, and F1-score. AdaBoost achieves 99.00% accuracy, enabling automated threat detection for stronger corporate network security.","Cyber security Reinforcement through Firewall Log Analysis and Machine Learning  \n1Afrah Fathima, 2G. Shree Devi, 3Dr. Zameer Gulzar  \n1Dept. Of Computer Applications, BSAR Crescent Institute of Science and Technology, Chennai, India  \nDept. Of CS&IT, MANUU Hyderabad, India  \n[af.fathima1@gmail.com](af.fathima1@gmail.com)  \n2Dept. Of Computer Applications, BSAR Crescent Institute of Science and Technology, Chennai, India  \n[Shreedevi@crescent.education](Shreedevi@crescent.education)  \n3Department of CS&AI, S R University, Warangal, Telagana  \n[Zamir045@gmail.com](Zamir045@gmail.com)  \nAbstract—Firewalls play a crucial role as a primary protective measure in safeguarding network security, effectively mitigating risks posed by external vulnerabilities and internal security breaches. This study presents a new framework that utilizes firewall log data to classify incoming data packets as either permitted or forbidden. The dataset utilized in this research is obtained from Department of CS&IT, MANU University and is subjected to a thorough data pre-processing procedure. This procedure includes several tasks such as managing missing values, encoding categorical variables, standardizing numerical attributes, and guaranteeing data coherence. In order to mitigate the issue of class imbalance within the target variable, we utilize a range of machine learning models and assess their efficacy through the examination of fundamental metrics such as accuracy, precision, recall, and F1-score. The results of our study demonstrate that the AdaBoost model has superior performance compared to other models, achieving a remarkable accuracy rate of 99.00% . This study demonstrates the application of machine learning methods to automatically identify the activities indicated in firewall logs, thereby improving the security of corporate networks. Through the implementation of automation, we facilitate amore dependable and efficient method of detecting and addressing possible risks, thereby strengthening network security measures and protecting valuable corporate information.  \nKeywords-Network Security, Firewall log Analysis, Machine Learning, Cyber security  \nI. INTRODUCTION  \nInternet cyber threats and cyberattacks pose ongoing and dynamic difficulties in our digitally integrated global environment. The aforementioned dangers comprise a diverse array of malevolent actions, including but not limited to data breaches, ransomware attacks, phishing attempts, denial-ofservice (DDoS) attacks, and various others. Cybercriminals engage in the exploitation of weaknesses present in computer systems, networks, and human actions with the intention of unlawfully acquiring confidential data, causing disturbances to services, and undermining the reliability of digital infrastructure. The continuous progression of technology necessitates the adaptation of tactics and techniques utilized by malicious actors, underscoring the imperative for comprehensive cybersecurity measures, preemptive identification of threats, and swift reaction to incidents in order to protect individuals, companies, and nations from the constant risk posed by cyberattacks.Consequently, safeguarding data integrity and usability has become imperative [1].Often, attackers possess insights into an organization's defense mechanisms, enabling them to evade detection.  \nFigure 1. describes the various of Cyber attacks.To counter such attacks, continuous analysis of network traffic records is essential to create profiles that inform firewall rules, allowing apt responses to incoming packets. However, these rules are inconstant flux, adapting to evolving attack methods, tool advancements, and attack intricacies [2] . This dynamic rule evolution poses a challenge, as rules are manually defined by  \nsystem administrators or organizational engineers. Firewalls are network security mechanisms, either in the form of hardware devices or software applications, which are specifically developed to oversee, filt","cbCaikNqcwLWznVv","https://ap.wps.com/l/cbCaikNqcwLWznVv","pdf",665318,1,"English","en",105,"# Introduction\n## Cyber threats and attack evolution\n## Role of firewalls in traffic filtering\n## Challenges in manual firewall rule management\n## AI/ML for firewall log–based threat detection","[{\"question\":\"How does the proposed framework use firewall logs?\",\"answer\":\"It processes firewall log data to classify each incoming packet as either permitted or forbidden. The workflow includes data pre-processing to ensure the dataset is consistent for modeling.\"},{\"question\":\"What pre-processing steps are applied to the dataset?\",\"answer\":\"The study manages missing values, encodes categorical variables, standardizes numerical attributes, and verifies data coherence before training models.\"},{\"question\":\"Which machine learning model performs best and how is it measured?\",\"answer\":\"AdaBoost delivers the strongest results, reaching 99.00% accuracy. Performance is assessed using metrics including accuracy, precision, recall, and F1-score.\"}]","Cyber security Reinforcement through Firewall Log Analysis and Machine Learning | PDF",1785726718,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"cyber-security-reinforcement-through-firewall-log-analysis-and-machine-learning","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/cyber-security-reinforcement-through-firewall-log-analysis-and-machine-learning/119866/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed framework use firewall logs?","Question",{"text":75,"@type":76},"It processes firewall log data to classify each incoming packet as either permitted or forbidden. The workflow includes data pre-processing to ensure the dataset is consistent for modeling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What pre-processing steps are applied to the dataset?",{"text":80,"@type":76},"The study manages missing values, encodes categorical variables, standardizes numerical attributes, and verifies data coherence before training models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best and how is it measured?",{"text":84,"@type":76},"AdaBoost delivers the strongest results, reaching 99.00% accuracy. 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