{"id":2723,"date":"2026-08-30T15:59:49","date_gmt":"2026-08-30T15:59:49","guid":{"rendered":"https:\/\/sites.wp.odu.edu\/locky\/?p=2723"},"modified":"2026-08-30T16:10:58","modified_gmt":"2026-08-30T16:10:58","slug":"linear-algebra-behind-4-cybersecurity-ml-models","status":"publish","type":"post","link":"https:\/\/sites.wp.odu.edu\/locky\/2026\/08\/30\/linear-algebra-behind-4-cybersecurity-ml-models\/","title":{"rendered":"Linear Algebra Behind for Cybersecurity ML Models"},"content":{"rendered":"\n<p>Naive Bayes assumes every feature is independent. For spam detection, that&#8217;s provably false \u2014 words in a sentence are obviously correlated. The model works well anyway.<\/p>\n\n\n\n<p>Figuring out why a wrong assumption still produces a right answer taught me more than a correct model would have. It&#8217;s also the thing that shifted how I read model behavior generally: the math isn&#8217;t decoration on top of scikit-learn, it&#8217;s the explanation for when a model will hold up and when it won&#8217;t.<\/p>\n\n\n\n<p>My first CYSE 420 assignment traced that math through four models used in security work \u2014 Naive Bayes, logistic and softmax regression, neural networks, and Gaussian anomaly detection.<\/p>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"wp-block-group__inner-container\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-layout-1 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"wp-block-group__inner-container\">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"wp-block-group__inner-container\"><a href=\"https:\/\/sites.wp.odu.edu\/locky\/wp-content\/uploads\/sites\/38189\/2026\/08\/Mod_1_Assignment_Fall2026_CYSE-420_fd.pdf\" class=\"pdfemb-viewer\" style=\"\" data-width=\"max\" data-height=\"max\"  data-toolbar=\"both\" data-toolbar-fixed=\"on\">Mod_1_Assignment_Fall2026_CYSE-420_fd<br\/><\/a>\n<p class=\"wp-block-pdfemb-pdf-embedder-viewer\"><\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<ul class=\"wp-block-social-links has-normal-icon-size has-visible-labels has-icon-color has-icon-background-color is-style-default is-horizontal is-content-justification-center is-layout-flex wp-container-core-social-links-layout-1 wp-block-social-links-is-layout-flex\"><li style=\"color: #ffffff; background-color: #cf2e2e; \" class=\"wp-social-link wp-social-link-chain has-white-color wp-block-social-link\"><a rel=\"https:\/\/www.credly.com\/users\/carl-lochstampfor-jr noopener nofollow\" target=\"_blank\" href=\"http:\/\/sites.wp.odu.edu\/locky\/wp-content\/uploads\/sites\/38189\/2026\/08\/Mod_1_Assignment_Fall2026_CYSE-420_fd.pdf\" class=\"wp-block-social-link-anchor\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M15.6,7.2H14v1.5h1.6c2,0,3.7,1.7,3.7,3.7s-1.7,3.7-3.7,3.7H14v1.5h1.6c2.8,0,5.2-2.3,5.2-5.2,0-2.9-2.3-5.2-5.2-5.2zM4.7,12.4c0-2,1.7-3.7,3.7-3.7H10V7.2H8.4c-2.9,0-5.2,2.3-5.2,5.2,0,2.9,2.3,5.2,5.2,5.2H10v-1.5H8.4c-2,0-3.7-1.7-3.7-3.7zm4.6.9h5.3v-1.5H9.3v1.5z\"><\/path><\/svg><span class=\"wp-block-social-link-label\">Full Screen or Download<\/span><\/a><\/li><\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Naive Bayes assumes every feature is independent. For spam detection, that&#8217;s provably false \u2014 words in a sentence are obviously correlated. The model works well anyway. Figuring out why a wrong assumption still produces a right answer taught me more than a correct model would have. It&#8217;s also the thing that shifted how I read&#8230; <\/p>\n<div class=\"link-more\"><a href=\"https:\/\/sites.wp.odu.edu\/locky\/2026\/08\/30\/linear-algebra-behind-4-cybersecurity-ml-models\/\">Read More<\/a><\/div>\n","protected":false},"author":30379,"featured_media":2725,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"image","meta":{"footnotes":"","wds_primary_category":1},"categories":[165,1],"tags":[267,271,275,269,273,274,270,276,272],"_links":{"self":[{"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/posts\/2723"}],"collection":[{"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/users\/30379"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/comments?post=2723"}],"version-history":[{"count":2,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/posts\/2723\/revisions"}],"predecessor-version":[{"id":2727,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/posts\/2723\/revisions\/2727"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/media\/2725"}],"wp:attachment":[{"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/media?parent=2723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/categories?post=2723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/locky\/wp-json\/wp\/v2\/tags?post=2723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}