{"id":714,"date":"2024-02-01T13:12:46","date_gmt":"2024-02-01T13:12:46","guid":{"rendered":"https:\/\/www.newevol.io\/resources\/?p=714"},"modified":"2024-07-14T18:01:16","modified_gmt":"2024-07-14T18:01:16","slug":"ai-and-machine-learning-for-log-analysis","status":"publish","type":"post","link":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/","title":{"rendered":"Artificial Intelligence &amp; Machine Learning for Log Analysis"},"content":{"rendered":"\n<p>Manual threat detection methods can&#8217;t keep up with the evolving tactics of cybercriminals. Machine learning (ML) can analyze large volumes of data quickly and accurately to detect potential threats. In this blog, we explore how ML can transform cybersecurity and Data analytics to keep our systems safe.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_66_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title ez-toc-toggle\" style=\"cursor: pointer\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #0a0a0a;color:#0a0a0a\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #0a0a0a;color:#0a0a0a\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Introduction\" title=\"Introduction\">Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#What_is_log_analytics_in_machine_learning\" title=\"What is log analytics in machine learning?\">What is log analytics in machine learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#What_is_AI_log_analysis\" title=\"What is AI log analysis?\">What is AI log analysis?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#What_is_the_purpose_of_log_analysis\" title=\"What is the purpose of log analysis?\">What is the purpose of log analysis?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Benefits_of_MLAI_for_log_Analysis\" title=\"Benefits of ML\/AI for log Analysis\">Benefits of ML\/AI for log Analysis<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#1_Categorize_info_rapidlyfaster\" title=\"1. Categorize info rapidly\/faster\">1. Categorize info rapidly\/faster<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#2_Identification_of_issues_automatically\" title=\"2. Identification of issues automatically\">2. Identification of issues automatically<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#3_Alert_critical_info\" title=\"3. Alert critical info\">3. Alert critical info<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#4_Allocation_of_resource_in_an_efficient_manner\" title=\"4. Allocation of resource in an efficient manner\">4. Allocation of resource in an efficient manner<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#5_Scalable_options\" title=\"5. Scalable options\">5. Scalable options<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#6_Environments_agnostic\" title=\"6. Environments agnostic\">6. Environments agnostic<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#What_is_an_example_of_a_log_analysis\" title=\"What is an example of a log analysis?\">What is an example of a log analysis?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#1_Data_collection_Preprocessing\" title=\"1. Data collection &amp; Preprocessing\">1. Data collection &amp; Preprocessing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#2_Feature_Extraction\" title=\"2. Feature Extraction\">2. Feature Extraction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#3_Visualizations_and_Interpreting\" title=\"3. Visualizations and Interpreting\">3. Visualizations and Interpreting<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#4_Deployment_and_Monitoring\" title=\"4. Deployment and Monitoring\">4. Deployment and Monitoring<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Common_Types_of_MLAI_models\" title=\"Common Types of ML\/AI models\">Common Types of ML\/AI models<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#1_Supervised_machine_learning_models\" title=\"1. Supervised machine learning models\">1. Supervised machine learning models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#2_Unsupervised_machine_learning\" title=\"2. Unsupervised machine learning\">2. Unsupervised machine learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#3_Semisupervised_model\" title=\"3. Semisupervised model\">3. Semisupervised model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#4_Reinforcement_model\" title=\"4. Reinforcement model\">4. Reinforcement model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#5_Deep_Learning_model\" title=\"5. Deep Learning model\">5. Deep Learning model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#6_Ensemble_Model\" title=\"6. Ensemble Model\">6. Ensemble Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#7_Instance-based_Model\" title=\"7. Instance-based Model\">7. Instance-based Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#8_Probabilistic_Model\" title=\"8. Probabilistic Model\">8. Probabilistic Model<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#AIMachine_learning_approach_to_log_analytics_challenges\" title=\"AI\/Machine learning approach to log analytics &amp; challenges\">AI\/Machine learning approach to log analytics &amp; challenges<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Machine_Learning_Comes_to_the_Rescue\" title=\"Machine Learning Comes to the Rescue\">Machine Learning Comes to the Rescue<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Evolution_of_Log_analysis_using_AI_ML\" title=\"Evolution of Log analysis using AI &amp; ML\">Evolution of Log analysis using AI &amp; ML<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Your_Dynamic_Threat_Defense_Platform\" title=\"Your Dynamic Threat Defense Platform\">Your Dynamic Threat Defense Platform<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#Conclusion\" title=\"Conclusion\">Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><strong>Introduction<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The world of cybersecurity can be complex and overwhelming. It&#8217;s like looking for a needle in a haystack of log data, where potential threats are hidden amongst the overwhelming volume and complexity of data. Every day, organizations face the challenge of sorting through terabytes of logs, hoping to find clues to identify malicious threats or anomalies. But this manual process is time-consuming, prone to human error, and struggles to keep up with the constantly evolving attack methods used by cybercriminals. Traditional threat detection methods often fall short of catching the sophisticated tactics used by malicious actors. Machine learning (ML) is one of the dynamic and capable automated log analysis tools in the digital world that can help efficiently and accurately analyze vast amounts of log data, uncovering threats that might otherwise remain undetected. This technology can potentially transform how we approach cybersecurity and keep our systems safe.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_log_analytics_in_machine_learning\"><\/span><strong>What is log analytics in machine learning?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Application of different techniques and algorithms to examine the data generated from sources such as networks, applications &amp; systems also known as &#8220;Machine Data&#8221; &amp; then proactively extract insights, find patterns, recognize an anomaly &amp; serve accurate predictions based on the findings from the logs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_AI_log_analysis\"><\/span><strong>What is AI log analysis?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>It involves using artificial intelligence methodologies such as <a href=\"https:\/\/en.wikipedia.org\/wiki\/Natural_language_processing\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">natural language processing<\/a>(nlp) to examine logs from applications, networks &amp; systems to get insights, find abnormalities &amp; improve system performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_the_purpose_of_log_analysis\"><\/span><strong>What is the purpose of log analysis?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Infrastructure monitoring and troubleshooting to find out errors, and issues in real time or historically<\/li>\n\n\n\n<li>Performance optimization by finding out the bottlenecks and understanding the metrics<\/li>\n\n\n\n<li>Security Monitoring, which involves finding unauthorized access or any suspicious activities<\/li>\n\n\n\n<li>Compliance &amp; audit to follow regulations and internal policies<\/li>\n\n\n\n<li>Predictive maintenance for the forecast potential failures or issues before they occur<\/li>\n\n\n\n<li>Business Intelligence to figure out a user action or a user pattern to make business decisions<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Benefits_of_MLAI_for_log_Analysis\"><\/span><strong>Benefits of ML\/AI for log Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Categorize_info_rapidlyfaster\"><\/span>1. Categorize info rapidly\/faster<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"275\" height=\"183\" src=\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/categorize-info-rapidly.webp\" alt=\"A keyboard with keys displaying symbols representing different types of information\" class=\"wp-image-1474\" style=\"width:206px;height:auto\"\/><\/figure>\n\n\n\n<p>It helps quickly categorize log data from log streams, resulting in proper organization and prioritization of logs for analysis or action.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Identification_of_issues_automatically\"><\/span>2. Identification of issues automatically<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It helps to detect issues automatically and enables proactive identification by finding the root cause of an issue, sorting out the hard problems before they escalate.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Alert_critical_info\"><\/span>3. Alert critical info<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Generate alerts for critical event types &amp; abnormalities in real time, which helps create proper mitigation strategies and timely responses to prevent security breaches and service disruptions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Allocation_of_resource_in_an_efficient_manner\"><\/span>4. Allocation of resource in an efficient manner<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It helps to optimize allocation, thereby increasing efficiency and reducing cost by making sure that actual demand is taken into consideration to allocate resources.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Scalable_options\"><\/span>5. Scalable options<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"970\" height=\"970\" src=\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options.webp\" alt=\"A visual representation of a graph chart with arrows and a bar graph\" class=\"wp-image-1477\" style=\"width:222px;height:auto\" srcset=\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options.webp 970w, https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options-300x300.webp 300w, https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options-150x150.webp 150w, https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options-768x768.webp 768w, https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options-24x24.webp 24w, https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options-48x48.webp 48w, https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/scalable-options-96x96.webp 96w\" sizes=\"auto, (max-width: 970px) 100vw, 970px\" \/><\/figure>\n\n\n\n<p>Can easily accommodate growth in data size &amp; complexity without affecting the performance significantly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_Environments_agnostic\"><\/span>6. Environments agnostic<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It can seamlessly adjust to different environments like development, testing, and production, increasing its ability to generalize patterns and abnormalities across diverse setups and configurations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_an_example_of_a_log_analysis\"><\/span><strong>What is an example of a log analysis?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Below are some of the points on How to automate log analysis!<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Data_collection_Preprocessing\"><\/span>1. Data collection &amp; Preprocessing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The former refers to gathering logs from the web server that might include IP addresses, time stamps, response codes, request types, etc. The latter refers to Cleaning and preprocessing, such as converting timestamps into a standard format and parsing out relevant fields like IP addresses or request types.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Feature_Extraction\"><\/span>2. Feature Extraction<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Extracting meaningful features from the figure collected, such as the frequency of requests per IP address, errors encountered &amp; last but not least average response times within a specific given time window.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Visualizations_and_Interpreting\"><\/span>3. Visualizations and Interpreting<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Imaginations of results to understand patterns and outliers in the log data by executing time series data, generating &amp; observing heatmaps, or using dashboards.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Deployment_and_Monitoring\"><\/span>4. Deployment and Monitoring<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Deploy log anomaly detection model &amp; set up alerts for continuous processes of incoming log info and send an alert to administrators when significant changes are noticed respectively.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Common_Types_of_MLAI_models\"><\/span><strong>Common<\/strong> <strong>Types of ML\/AI models<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Below are a few AI \/ ML log analysis approaches!<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Supervised_machine_learning_models\"><\/span>1. Supervised machine learning models<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>supervised learning models trained on historical malware samples can quickly classify the threat, allowing security teams to take instant action to contain and eliminate the infection.<\/p>\n\n\n\n<p><strong>(E.g)<\/strong> &#8211; Logistic Regression, Linear Support Vector Machines (SVM)(identifies patterns in string based data), Decision Trees, Random Forest, Neural Networks (for classification tasks), Linear Regression, Polynomial Regression, Support Vector Regression, Neural Networks (for regression tasks).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Unsupervised_machine_learning\"><\/span>2. Unsupervised machine learning<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Unsupervised learning algorithms can uncover a series of unauthorized access attempts across multiple user accounts, signaling a potential data breach in progress.<\/p>\n\n\n\n<p><strong>(E.g)<\/strong> &#8211; K-means &amp; Hierarchical Clustering, DBSCAN, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding, Autoencoders.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Semisupervised_model\"><\/span>3. Semisupervised model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It uses labeled and unlabelled data (two major categories falling under data labeling) for training.<\/p>\n\n\n\n<p><strong>(E.g)<\/strong> &#8211; Self-training &amp; Co-training, Transfer learning approaches.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Reinforcement_model\"><\/span>4. Reinforcement model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Makes use of trial and error methods to achieve its goal.<\/p>\n\n\n\n<p><strong>(E.g)<\/strong> &#8211; Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Deep_Learning_model\"><\/span>5. Deep Learning model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Deep Learning to detect anomalies in logs is an extremely expensive way for Neural networking with multiple layers (deep architecture) to learn representations of info.<\/p>\n\n\n\n<p><strong>(E.g)<\/strong> &#8211; Convolutional Neural for image info, Recurrent Neural for sequential info, Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) for handling sequential with long-range dependencies, Transformer like BERT and GPT for natural language processing tasks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_Ensemble_Model\"><\/span>6. Ensemble Model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Contains the combination of multiple Popular models resulting in performance improvement.<\/p>\n\n\n\n<p><strong>(E.g)<\/strong> &#8211; Bagging: Random Forest, Boosting: Gradient Boosting Machines (GBM), AdaBoost, Stacking: Combining multiple models via a meta-learner.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_Instance-based_Model\"><\/span>7. Instance-based Model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Uses specific examples rather than generic rules to learn.<\/p>\n\n\n\n<p><strong>(E.g) <\/strong>&#8211; k-Nearest Neighbors (k-NN).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"8_Probabilistic_Model\"><\/span>8. Probabilistic Model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Makes use of probability distributions and represents uncertainty.<\/p>\n\n\n\n<p><strong>(E.g) <\/strong>&#8211; Bayesian Networks, Gaussian Mixture &amp; Hidden Markov.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"AIMachine_learning_approach_to_log_analytics_challenges\"><\/span><strong>AI<\/strong>\/<strong>Machine learning approach to log analytics &amp; challenges<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"275\" height=\"183\" src=\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/07\/machine-learning-approach-and-challenges.webp\" alt=\"The word &quot;challenge&quot; spelled out with wooden blocks\" class=\"wp-image-1480\" style=\"width:267px;height:auto\"\/><\/figure>\n\n\n\n<p>The sheer volume, velocity, and variety of log data generated by modern systems is expanding rapidly and is nothing short of staggering. Terabytes of logs are produced every second, capturing all the actions, log events, and transactions happening across the digital ecosystem. This escalating growth poses a major challenge for traditional security tools, which can struggle to keep up with the scale and complexity of the Months of data set.<\/p>\n\n\n\n<p>In the age of information, it&#8217;s easy to overlook valuable security insights amidst the abundance of available data. More often than not, crucial indicators of malicious activity are hidden among the haystack of logs, making real-time threat detection feel like searching for a needle in a stack of needles. The sheer volume of benign data can easily lead to overlooking critical alerts.<\/p>\n\n\n\n<p>Compounding this challenge, security teams also deal with an overwhelming number of false alarms triggered by traditional methods. These false positives create confusion and waste valuable time and resources. Moreover, they divert attention from real threats, which remain hidden in the shadows.<\/p>\n\n\n\n<p>Analyzing huge amounts of data manually is a challenging task, which would require various approaches from a large team of analysts and an indefinite amount of time. Even with a dedicated workforce, the sheer size of the data makes manual inspection impractical, if not impossible. This leaves organizations vulnerable to undetected security threats that may be hidden within the data.<\/p>\n\n\n\n<p>Furthermore, traditional rule-based security systems are no longer effective in keeping up with the constantly evolving tactics of threat actors. These systems rely on static rule sets that can&#8217;t adapt quickly enough to counter emerging attack vectors. As the threat landscape continues to change, the limitations of rule-based systems become more apparent, highlighting the need for a more agile and proactive &amp; different approach to threat detection.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Machine_Learning_Comes_to_the_Rescue\"><\/span><strong>Machine Learning Comes to the Rescue<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The challenge of detecting hidden threats from a vast amount of logs can be overwhelming, but machine learning offers a promising solution. Unlike humans, machine learning algorithms can analyze large volumes of data with lightning speed and unwavering accuracy. Using advanced statistical models, ML models can sift through logs, detecting subtle anomalies and patterns that often elude the human eye. This makes machine learning a valuable tool that helps find that needle in the haystack.<\/p>\n\n\n\n<p>In the field of cybersecurity, machine learning (ML) algorithms are utilized in advanced platforms equipped with a range of techniques designed for threat detection. Anomaly detection algorithms identify metric anomalies from normal behavior, warning security teams of possible malicious activity. Supervised training models requires labeled datasets to accurately identify known threats, while unsupervised learning algorithms reveal new threats that are hidden in the background without the requirement of predefined labels.<\/p>\n\n\n\n<p>Imagine a situation where a cybersecurity platform powered by machine learning detects a malware infection in an organization&#8217;s network. By analyzing log data from different endpoints and network devices, the platform&#8217;s anomaly detection algorithms can identify unusual patterns of file access and execution that may indicate a potential malware outbreak.<\/p>\n\n\n\n<p>With these insights, security teams can act quickly to mitigate the breach and protect sensitive information from being compromised. In both cases, machine learning proves to be an asset in the ongoing fight against cyber threats. By transforming the haystack of logs into actionable intelligence, ML helps security teams stay a step ahead of cybercriminals.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Evolution_of_Log_analysis_using_AI_ML\"><\/span><strong>Evolution of Log analysis using AI &amp; ML<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong><u>1969:<\/u><\/strong> <a href=\"https:\/\/en.wikipedia.org\/wiki\/Unix\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Bell Labs Unix<\/a> was the one who laid down the foundation. During this period, operating systems did not have the necessary tools to aggregate log files, due to which admins had to rely on text manipulation tools to understand the log files on an as-needed basis.&nbsp;<\/p>\n\n\n\n<p><strong><u>1990s:<\/u> <\/strong>During this period, the traditional log analysis evolved and became even more complicated. Each boot-up, system event type, and application had separate logs.&nbsp; A proprietary log analysis tool like BootHawk was introduced, which catered to specific tasks and helped enhance visibility into log data.<\/p>\n\n\n\n<p><strong><u>1998:<\/u><\/strong> <a href=\"https:\/\/en.wikipedia.org\/wiki\/Syslog-ng\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Syslog-ng <\/a>was introduced during this phase to deal with the growing demand for log collection. They played a pivotal role in enhancing data transmission providing a wide range of support to applications and operating systems in the form of a unified interface, which helped IT teams study data from multiple locations.<\/p>\n\n\n\n<p><strong><u>2004:<\/u> <\/strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Rsyslog\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Rsyslog<\/a>, which came from the Sysklogd standard package, emerged in the market. This open source tool aimed to provide rich features &amp; reliable syslog daemon without affecting its drop-in replacement capabilities to stock syslogd.<\/p>\n\n\n\n<p><strong><u>Current scenario:<\/u> <\/strong>People have adapted to agile\/DevOps instead of using Waterfall for reinforcing automation, Machine learning and AI, Which was very important looking at the volumes of data generated each day &amp; it wasn&#8217;t feasible to entirely rely on manual operations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Your_Dynamic_Threat_Defense_Platform\"><\/span><strong>Your Dynamic Threat Defense Platform<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Welcome to the cutting-edge world of modern cybersecurity defense with NewEvol, our Dynamic Threat Defense Platform. Our all-in-one cybersecurity platform uses machine learning for advanced threat detection and response. Harnessing the unparalleled capabilities of ML, it navigates through the haystack of logs with accuracy and agility, making it an innovative solution for any organization concerned about cybersecurity.<\/p>\n\n\n\n<p>With the advanced log aggregation capability of the <a href=\"https:\/\/www.newevol.io\/product\/data-lake-solutions.php\"><strong><u>Data Lake solution<\/u><\/strong><\/a>, NewEvol consolidates logs from different sources into a central repository for comprehensive analysis &amp; log management. It uses sophisticated anomaly detection algorithms to sift through vast Log volumes, quickly identifying any variations from normal behavior that could indicate potential threats. Each anomaly is thoroughly evaluated and assigned a threat score, giving security teams actionable insights into the severity and urgency of the threat.<\/p>\n\n\n\n<p>Our DTD platform is equipped with an automated incident response capability that enables organizations to respond to security threats in real time with remarkable speed and efficiency. This feature automates response actions based on predefined rules and policies, which helps reduce response times and mitigate the impact of security incidents before they escalate into severe breaches.<\/p>\n\n\n\n<p>Our Dynamic Threat Defense Platform has been tested and proven in the real world, helping organizations across various industries detect anomalous activities and respond to threats more efficiently. It can detect sophisticated malware infections, stop unauthorized access attempts, and mitigate data breaches, ultimately safeguarding sensitive assets and preserving the integrity of organizational networks.<\/p>\n\n\n\n<p>A client sought a solution to monitor various smart devices in a smart city, with a wide range of devices placed in public places. Compliance requirements set by the government also had to be met. We deployed a <a href=\"https:\/\/www.newevol.io\/resources\/blog\/siem\/what-is-siem\/\"><strong>Security Information and Event Management<\/strong><\/a> (SIEM) system with <a href=\"https:\/\/www.newevol.io\/resources\/blog\/analytics\/what-is-cybersecurity-analytics\/\"><strong>cybersecurity analytics<\/strong><\/a> platform and threat intelligence capabilities to monitor the smart city&#8217;s end-to-end environment, including custom alerts, dashboards, and reports created combining log lines. Machine learning algorithms detected anomalies in large volumes of network traffic on public devices, and threat intelligence capabilities kept the analyst updated with the latest threat feeds.<\/p>\n\n\n\n<p>With NewEvol, enterprises can stay one step ahead of cyber threats, transforming the haystack of logs into a powerful management tool for proactive defense in today&#8217;s ever-evolving threat landscape.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Amidst the overwhelming volume and complexity of logs, machine learning offers unprecedented speed, accuracy, and agility in threat detection. With our Dynamic Threat Defense Platform, organizations can consolidate &amp; Analyze logs, detect anomalies in logs, and automate response actions to stay ahead of threat vectors and protect sensitive assets. Join us in redefining the future of cybersecurity and <a href=\"https:\/\/www.newevol.io\/contact-us.php\">Book a Demo<\/a> today.<\/p>\n\n\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/machine-learning-and-log-analysis-uncovering-hidden-needles-in-your-haystack\/\"\n  },\n  \"headline\": \"Artificial Intelligence & Machine Learning for Log Analysis\",\n  \"description\": \"Discover the power of machine learning & AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors & cyber threats.\",\n  \"image\": \"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp\",  \n  \"author\": {\n    \"@type\": \"Person\",\n    \"name\": \"Krunal Medapara\"\n  },  \n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"NewEvol\",\n    \"logo\": {\n      \"@type\": \"ImageObject\",\n      \"url\": \"\"\n    }\n  },\n  \"datePublished\": \"\"\n}\n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>Manual threat detection methods can&#8217;t keep up with the evolving tactics of cybercriminals. Machine learning (ML) can analyze large volumes of data quickly and accurately to detect potential threats. In this blog, we explore how ML can transform cybersecurity and Data analytics to keep our systems safe. Introduction The world of cybersecurity can be complex&hellip; <a class=\"more-link\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/\">Continue reading <span class=\"screen-reader-text\">Artificial Intelligence &amp; Machine Learning for Log Analysis<\/span><\/a><\/p>\n","protected":false},"author":6,"featured_media":1472,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[83],"class_list":["post-714","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-lake","tag-log-management","entry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Artificial Intelligence &amp; Machine Learning for Log Analysis<\/title>\n<meta name=\"description\" content=\"Discover the power of machine learning &amp; AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors &amp; cyber threats.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial Intelligence &amp; Machine Learning for Log Analysis\" \/>\n<meta property=\"og:description\" content=\"Discover the power of machine learning &amp; AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors &amp; cyber threats.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"NewEvol\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/NewEvolPlatform\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-02-01T13:12:46+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-14T18:01:16+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"900\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Krunal Medapara\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@krunalpatel17\" \/>\n<meta name=\"twitter:site\" content=\"@NewEvolPlatform\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Krunal Medapara\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/\",\"url\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/\",\"name\":\"Artificial Intelligence & Machine Learning for Log Analysis\",\"isPartOf\":{\"@id\":\"https:\/\/www.newevol.io\/resources\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp\",\"datePublished\":\"2024-02-01T13:12:46+00:00\",\"dateModified\":\"2024-07-14T18:01:16+00:00\",\"author\":{\"@id\":\"https:\/\/www.newevol.io\/resources\/#\/schema\/person\/7929a2b0ea108d69f18541bb94a98680\"},\"description\":\"Discover the power of machine learning & AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors & cyber threats.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#primaryimage\",\"url\":\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp\",\"contentUrl\":\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp\",\"width\":1920,\"height\":900,\"caption\":\"ai and machine learning for log analysis\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.newevol.io\/resources\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Artificial Intelligence &amp; Machine Learning for Log Analysis\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.newevol.io\/resources\/#website\",\"url\":\"https:\/\/www.newevol.io\/resources\/\",\"name\":\"NewEvol\",\"description\":\"Innovation in Motion\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.newevol.io\/resources\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.newevol.io\/resources\/#\/schema\/person\/7929a2b0ea108d69f18541bb94a98680\",\"name\":\"Krunal Medapara\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.newevol.io\/resources\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2022\/03\/krunal-mendapara-1-scaled.jpg\",\"contentUrl\":\"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2022\/03\/krunal-mendapara-1-scaled.jpg\",\"caption\":\"Krunal Medapara\"},\"description\":\"Krunal Mendapara is the Chief Technology Officer, responsible for creating product roadmaps from conception to launch, driving the product vision, defining go-to-market strategy, and leading design discussions.\",\"sameAs\":[\"https:\/\/www.newevol.io\/\",\"https:\/\/x.com\/krunalpatel17\"],\"url\":\"https:\/\/www.newevol.io\/resources\/author\/krunal-medapara\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Artificial Intelligence & Machine Learning for Log Analysis","description":"Discover the power of machine learning & AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors & cyber threats.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/","og_locale":"en_US","og_type":"article","og_title":"Artificial Intelligence & Machine Learning for Log Analysis","og_description":"Discover the power of machine learning & AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors & cyber threats.","og_url":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/","og_site_name":"NewEvol","article_publisher":"https:\/\/www.facebook.com\/NewEvolPlatform\/","article_published_time":"2024-02-01T13:12:46+00:00","article_modified_time":"2024-07-14T18:01:16+00:00","og_image":[{"width":1920,"height":900,"url":"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp","type":"image\/webp"}],"author":"Krunal Medapara","twitter_card":"summary_large_image","twitter_creator":"@krunalpatel17","twitter_site":"@NewEvolPlatform","twitter_misc":{"Written by":"Krunal Medapara","Est. reading time":"11 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/","url":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/","name":"Artificial Intelligence & Machine Learning for Log Analysis","isPartOf":{"@id":"https:\/\/www.newevol.io\/resources\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#primaryimage"},"image":{"@id":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#primaryimage"},"thumbnailUrl":"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp","datePublished":"2024-02-01T13:12:46+00:00","dateModified":"2024-07-14T18:01:16+00:00","author":{"@id":"https:\/\/www.newevol.io\/resources\/#\/schema\/person\/7929a2b0ea108d69f18541bb94a98680"},"description":"Discover the power of machine learning & AI in detecting subtle anomalies and patterns. Keep your systems safe from malicious actors & cyber threats.","breadcrumb":{"@id":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#primaryimage","url":"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp","contentUrl":"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2024\/02\/ai-and-machine-learning-for-log-anaysis.webp","width":1920,"height":900,"caption":"ai and machine learning for log analysis"},{"@type":"BreadcrumbList","@id":"https:\/\/www.newevol.io\/resources\/blog\/data-lake\/ai-and-machine-learning-for-log-analysis\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.newevol.io\/resources\/"},{"@type":"ListItem","position":2,"name":"Artificial Intelligence &amp; Machine Learning for Log Analysis"}]},{"@type":"WebSite","@id":"https:\/\/www.newevol.io\/resources\/#website","url":"https:\/\/www.newevol.io\/resources\/","name":"NewEvol","description":"Innovation in Motion","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.newevol.io\/resources\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.newevol.io\/resources\/#\/schema\/person\/7929a2b0ea108d69f18541bb94a98680","name":"Krunal Medapara","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.newevol.io\/resources\/#\/schema\/person\/image\/","url":"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2022\/03\/krunal-mendapara-1-scaled.jpg","contentUrl":"https:\/\/www.newevol.io\/resources\/wp-content\/uploads\/2022\/03\/krunal-mendapara-1-scaled.jpg","caption":"Krunal Medapara"},"description":"Krunal Mendapara is the Chief Technology Officer, responsible for creating product roadmaps from conception to launch, driving the product vision, defining go-to-market strategy, and leading design discussions.","sameAs":["https:\/\/www.newevol.io\/","https:\/\/x.com\/krunalpatel17"],"url":"https:\/\/www.newevol.io\/resources\/author\/krunal-medapara\/"}]}},"_links":{"self":[{"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/posts\/714","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/comments?post=714"}],"version-history":[{"count":77,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/posts\/714\/revisions"}],"predecessor-version":[{"id":1525,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/posts\/714\/revisions\/1525"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/media\/1472"}],"wp:attachment":[{"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/media?parent=714"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/categories?post=714"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newevol.io\/resources\/wp-json\/wp\/v2\/tags?post=714"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}