{"id":32077,"date":"2017-03-21T18:26:29","date_gmt":"2017-03-21T15:26:29","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=32077"},"modified":"2018-08-01T17:08:36","modified_gmt":"2018-08-01T14:08:36","slug":"deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/","title":{"rendered":"Deep Learning for Cybersecurity: Identifying Anomalies and Malicious Traffic"},"content":{"rendered":"<p>A recent <a href=\"https:\/\/www.altoros.com\/webinars\/deep-learning-for-cyber-security\">webinar<\/a> discussed how deep learning solutions can be applied to deliver better cybersecurity. However, things don&#8217;t always come as we expect, so there are certain difficulties to solve. <a href=\"https:\/\/www.linkedin.com\/in\/huttsteven\/\" target=\"_blank\">Steven Hutt<\/a>, a consultant in deep learning and financial risk, digs into the challenges on the way and the approaches at hand to save the day.<\/p>\n<p>&nbsp;<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">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: #999;color:#999\" 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: #999;color:#999\" 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 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/#Categorical_data_and_other_issues\" >Categorical data and other issues<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/#Generative_models_and_multivariate_distributions\" >Generative models and multivariate distributions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/#Tricks_at_hand\" >Tricks at hand<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/#Related_slides\" >Related slides<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/#Further_reading\" >Further reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/#About_the_expert\" >About the expert<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Categorical_data_and_other_issues\"><\/span>Categorical data and other issues<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>In a view of exemplary data breaches experienced by such giants as <a href=\"https:\/\/www.theguardian.com\/technology\/2016\/dec\/14\/yahoo-hack-security-of-one-billion-accounts-breached\" target=\"_blank\">Yahoo<\/a>, <a href=\"https:\/\/www.bloomberg.com\/tosv2.html?vid=&#038;uuid=1ed93f78-a3cb-11ec-93ef-50744243764a&#038;url=L25ld3MvYXJ0aWNsZXMvMjAxNS0xMC0yMC9zb255LXRvLXBheS1hcy1tdWNoLWFzLTgtbWlsbGlvbi10by1zZXR0bGUtZGF0YS1icmVhY2gtY2xhaW1z\" target=\"_blank\">Sony<\/a>, and even <a href=\"https:\/\/www.crn.com\/slide-shows\/security\/300083246\/the-10-biggest-data-breaches-of-2016.htm\/10\" target=\"_blank\">Democratic National Committee<\/a>, Steven emphasized the urgency of enhancing cybersecurity. While attack vectors are constantly changing and static detection approaches fail, deep learning can be employed to \u201cbetter identify malicious network traffic.\u201d <\/p>\n<p>Still, things don\u2019t come that easy. Though, there is plenty of data, and we observe steady progress in general areas of unsupervised feature and anomaly detection, hurdles are also to count:<\/p>\n<ul>\n<li>Practical usage of deep learning requires a very low false positive rate.<\/li>\n<li>Essentially no labelled data.<\/li>\n<li>Data is a hybrid of categorical (mostly) and numeric (some).<\/li>\n<\/ul>\n<p>Steven introduced a notion of a network flow\u2014\u201ca record of the information exchanged via packets between a source and a destination machine during the course of the network protocol session.\u201d<\/p>\n<p>Network flow values are a mixture of <em>categorical<\/em>, <em>numerical<\/em>, and <em>text<\/em> data. With deep learning solutions for treating numerical and text data extensively developed, the main focus shifts to categorical data.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Generative_models_and_multivariate_distributions\"><\/span>Generative models and multivariate distributions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>In attempting to fit a probability distribution to the data, Steven suggest using <a href=\"https:\/\/www.altoros.com\/blog\/the-diversity-of-tensorflow-wrappers-gpus-generative-adversarial-networks-etc\/\" target=\"_blank\">generative models<\/a>.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/03\/deep-learning-for-cybersecurity-unsurpervised-anomaly-detection-v12.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/03\/deep-learning-for-cybersecurity-unsurpervised-anomaly-detection-v12.png\" alt=\"\" width=\"640\" class=\"aligncenter size-full wp-image-32082\" \/><\/a><\/center><\/p>\n<p>In contrast to numerical or text data, deep learning solutions that treat categorical data has been studied less. In his example, Steven demonstrated how to model multivariate <a href=\"https:\/\/en.wikipedia.org\/wiki\/Categorical_distribution\" target=\"_blank\">categorical distributions<\/a>. (Note: <a href=\"https:\/\/juliastats.org\/Distributions.jl\/multivariate.html\" target=\"_blank\">Multivariate distributions<\/a> are probabilistic distributions, whose samples are represented as vectors.) According to him, every multivariate categorical distribution is a mixture of multivariate categorical distributions with independent marginals.<\/p>\n<p>Though the mixture representation result is quite encouraging, there come up some challenges to address:<\/p>\n<ul>\n<li>Direct likelihood maximization is computationally very expensive.<\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Stochastic_gradient_descent\" target=\"_blank\">Stochastic gradient descent<\/a> is not possible as variables are discrete.<\/li>\n<li>The indicated size of the mixing variable is geometric in the indicated dimension.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Tricks_at_hand\"><\/span>Tricks at hand<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>To eliminate the current issues, one may utilize:<\/p>\n<ol>\n<li style=\"margin-bottom: 6px;\">\n<p>A <a href=\"https:\/\/en.wikipedia.org\/wiki\/Variational_Bayesian_methods\" target=\"_blank\">variational inference<\/a> for approximate likelihood maximization<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/03\/deep-learning-for-cybersecurity-reparametrization-v12.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/03\/deep-learning-for-cybersecurity-reparametrization-v12.png\" alt=\"\" width=\"640\" class=\"aligncenter size-full wp-image-32081\" \/><\/a><small>Reparametrization may fail with categorical variables<\/small><\/center><\/li>\n<li style=\"margin-bottom: 6px;\">\n<p>The <a href=\"https:\/\/en.wikipedia.org\/wiki\/Categorical_distribution#Sampling_via_the_Gumbel_distribution\" target=\"_blank\">Gumbel softmax<\/a> to relax categorical variables to continuous variables<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/03\/deep-learning-for-cybersecurity-gumbel-softmax-distribution-v12.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/03\/deep-learning-for-cybersecurity-gumbel-softmax-distribution-v12.png\" alt=\"\" width=\"746\" height=\"558\" class=\"aligncenter size-full wp-image-32080\" \/><\/a><small>Sampling random variables with Gumbel&#8217;s distribution<\/small><\/center><\/li>\n<li>The <a href=\"https:\/\/en.wikipedia.org\/wiki\/Dirichlet_process\" target=\"_blank\">Dirichlet processes<\/a> to incorporate the indicated size of the mixing variable as part of the inference<\/li>\n<\/ol>\n<p>At the webinar, Steven detailed how each of the approaches work. For more detail, watch the <a href=\"https:\/\/www.altoros.com\/webinars\/deep-learning-for-cyber-security\">video recording<\/a> of the webinar.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Related_slides\"><\/span>Related slides<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><center><iframe loading=\"lazy\" src=\"\/\/www.slideshare.net\/slideshow\/embed_code\/key\/emXdSS0D2njAmT\" width=\"595\" height=\"485\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" style=\"border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;\" allowfullscreen> <\/iframe><\/center><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/the-diversity-of-tensorflow-wrappers-gpus-generative-adversarial-networks-etc\/\">The Diversity of TensorFlow: Wrappers, GPUs, Generative Adversarial Networks, etc.<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/cybersecurity-for-the-iot-and-ge-predix\/\">Cybersecurity for the IoT: Issues, Challenges, and Solutions<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/deep-learning-in-healthcare-finance-and-iiot-tensorflow-use-cases\/\">Deep Learning in Healthcare, Finance, and IIoT: TensorFlow Use Cases (Videos)<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"About_the_expert\"><\/span>About the expert<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><small><a href=\"https:\/\/www.linkedin.com\/in\/huttsteven\/\" target=\"_blank\">Steven Hutt<\/a> is a consultant in deep learning and financial risk, currently working in cybersecurity and algorithmic trading. He has previously been head quant for credit at UBS and Morgan Stanley, and before that a mathematician dealing with an obscure branch of topology.<\/small><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A recent webinar discussed how deep learning solutions can be applied to deliver better cybersecurity. However, things don&#8217;t always come as we expect, so there are certain difficulties to solve. Steven Hutt, a consultant in deep learning and financial risk, digs into the challenges on the way and the approaches [&#8230;]<\/p>\n","protected":false},"author":3,"featured_media":32086,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[214],"tags":[748,749],"class_list":["post-32077","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorials","tag-machine-learning","tag-tensorflow"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Deep Learning for Cybersecurity: Identifying Anomalies and Malicious Traffic | Altoros<\/title>\n<meta name=\"description\" content=\"Learn how to treat categorical data, as well as use generative models and other means to enable better cybersecurity.\" \/>\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.altoros.com\/blog\/deep-learning-for-cybersecurity-identifying-anomalies-and-malicious-traffic\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning for Cybersecurity: Identifying Anomalies and Malicious Traffic | Altoros\" \/>\n<meta property=\"og:description\" content=\"A recent webinar discussed how deep learning solutions can be applied to deliver better cybersecurity. However, things don&#8217;t always come as we expect, so there are certain difficulties to solve. 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