Image Classifiers for Network Intrusions

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>Image Classifiers for Network Intrusions</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Image Classifiers for Network Intrusions"> <meta name="description" content="This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2鈥檚 convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle."/> <meta name="keywords" content="Neural Networks, Computer Vision, Image Classification, Intrusion Detection, MNIST Benchmark."/>   <meta name="dc.title" content="Image Classifiers for Network Intrusions"> <meta name="citation_author" content="David A. Noever"> <meta name="citation_author" content="Samantha E. Miller Noever"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT)"> <meta name="dc.date" content="2021/04/24"> <meta name="dc.identifier" content="10.5121/csit.2021.110504"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="title" content="Image Classifiers for Network Intrusions"> <meta name="description" content="This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2鈥檚 convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle."/> <meta name="dc.subject" content="Neural Networks"> <meta name="dc.subject" content="Computer Vision"> <meta name="dc.subject" content="Image Classification"> <meta name="dc.subject" content="Intrusion Detection"> <meta name="dc.subject" content="MNIST Benchmark">   <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="2021/04/24"> <meta name="prism.volume" content="11"> <meta name="prism.number" content="5"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="53">   <meta name="citation_journal_title" content="Computer Science & Information Technology (CS & IT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_author" content="David A. Noever and Samantha E. 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Miller Noever"> <meta name="citation_doi" content="10.5121/csit.2021.110504"> <meta name="citation_abstract_html_url" content="https://aircconline.com/csit/abstract/v11n5/csit110504.html"> <meta name="citation_pdf_url" content="https://aircconline.com/csit/papers/vol11/csit110504.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/abstract/v11n5/csit110504.html"/> <meta property="og:title" content="Image Classifiers for Network Intrusions"/> <meta property="og:description" content="This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2鈥檚 convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. 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Noever and Samantha E. Miller Noever, PeopleTec, Inc., USA</p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2鈥檚 convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Neural Networks, Computer Vision, Image Classification, Intrusion Detection, MNIST Benchmark.</p><br> <button type="button" id="button"><a target="blank" href="/csit/papers/vol11/csit110504.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/csit/V11N05.html">Volume 11, Number 05</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li id="id"><a href="http://airccse.org/editorial.html">Editorial Board</a></li> <li><a href="http://airccse.org/arch.html">Archives</a></li> <li><a href="http://airccse.org/indexing.html">Indexing</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div> <div class="clear_left"></div> <br> </div> <div class="clear"></div> <div id="footer"> <table width="100%" > <tr> <td width="46%" class="F_menu"><a href="http://airccse.org/subscription.html">Subscription</a> <a href="http://airccse.org/membership.html">Membership</a> <a href="http://airccse.org/cscp.html">AIRCC CSCP</a> <a href="http://airccse.org/acontact.html">Contact Us</a> </td> <td width="54%" align="right"><a href="http://airccse.org/index.php"><img src="/csit/abstract/img/logo.gif" alt="" width="21" height="24" /></a><a href="http://www.facebook.com/AIRCCSE"><img src="/csit/abstract/img/facebook.jpeg" alt="" width="21" height="24" /></a><a href="https://twitter.com/AIRCCFP"><img src="/csit/abstract/img/twitter.jpeg" alt="" width="21" height="24" /></a><a href="http://cfptech.wordpress.com/"><img src="/csit/abstract/img/index1.jpeg" alt="" width="21" height="24" /></a></td> </tr> <tr><td height="25" colspan="2"> <p align="center">All Rights Reserved ® AIRCC</p> </td></tr> </table> </div> </div> </div> </div> </div> </div> </body> </html>

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Image Classifiers for Network Intrusions