Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Natarajan, Arul Kumar; Galety, Mohammad Gouse; Mishra, Nilamadhab; Prabu, M.; Manimaran, A.; Ramasamy, Gobi Title A name given to the resource Enhancing Log File Analysis in Digital Forensics and Incident Response through Machine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Cyber Security, Forensics and National Security;pp.378-396 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003497868-17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003497868-17</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021606008?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021606008?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Natarajan A.K., Samarkand International University of Technology, Samarkand, Uzbekistan; Galety M.G., Samarkand International University of Technology, Samarkand, Uzbekistan; Mishra N., VIT Bhopal University, Bhopal, India; Prabu M., Amrita Vishwa Vidyapeetham Erachakulam, Andhra Pradesh, India; Manimaran A., VIT-AP University, Amaravati, India; Ramasamy G., Christ University, Bengaluru, India Description An account of the resource Log file analysis is crucial for identifying and exploring digital security incidents by recording system and network traffic. The growing volume and complexity of log data do not allow traditional analytical methods to be used, which led to the need for the development of more advanced analytical tools. This chapter shows a new method to infer practical information from the log file analysis using machine learning algorithms combined with Python programming. The technique has the following structure: Data preprocessing, Feature extraction, and then using multiple machine learning models such as RandomForestClassifier, Gradient Boosting Classifier, SVM, XGBoostClassifier, and MLPClassifier. Adding Python greatly improves these advanced models' accuracy and efficiency in analyzing log files. The XGBoostClassifier achieved the highest accuracy, which was 0.9198 as precision, and it indicates good applicability to complicated log data compared to another model in our test. This section compares the machine learning models using the UNSWNb15 dataset, which provides a broad range of network traffic data. The chapter contains some visualizations of flagship results and a detailed discussion about the results, discussing the challenges and limitations of the proposed approach. It also suggests future research directions. The results also typify the specifics of how Python and machine learning can be disrupted to develop digital forensics incident response practicability, bringing forth such innovations that cater to solving the cyber world's rapidly transitioning threat landscapes and tooling up valued scientific knowledge in the domain. 2026 selection and editorial matter, Vinay Aseri, Sumit Kumar Choudhary, and Adarsh Kumar; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104041196-4; 978-104041198-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online