--- title: Using Sequence Modeling to Detect Android Malware in... description: This post explores using sequence modeling, specifically LSTM and BERT, to detect Android malware in highly imbalanced datasets. The application of BERT... canonical: https://daily.dev/posts/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-cgq6knhde twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Using Sequence Modeling to Detect Android Malware in Highly Imbalanced Datasets | daily.dev og:description: This post explores using sequence modeling, specifically LSTM and BERT, to detect Android malware in highly imbalanced datasets. The application of BERT... og:url: https://daily.dev/posts/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-cgq6knhde og:image: https://api.daily.dev/og/posts/CgQ6knHdE.png og:image:alt: Using Sequence Modeling to Detect Android Malware in Highly Imbalanced Datasets og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Using Sequence Modeling to Detect Android Malware in Highly Imbalanced Datasets **[System Weakness](https://daily.dev/sources/systemweakness)** · 3 min read · 2 upvotes · 0 comments ## Summary This post explores using sequence modeling, specifically LSTM and BERT, to detect Android malware in highly imbalanced datasets. The application of BERT achieves excellent results with an F1 score of 0.919 on imbalanced malware detection. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#cyber](https://daily.dev/tags/cyber), [#deep-learning](https://daily.dev/tags/deep-learning), [#android](https://daily.dev/tags/android), [#malware](https://daily.dev/tags/malware), [#bert](https://daily.dev/tags/bert) [View this post on daily.dev](https://daily.dev/posts/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-cgq6knhde) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Using Sequence Modeling to Detect Android Malware in Highly Imbalanced Datasets","url":"https://daily.dev/posts/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-cgq6knhde","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-cgq6knhde"},"datePublished":"2024-02-02T16:35:53.122Z","dateModified":"2024-05-09T09:21:59.549Z","description":"This post explores using sequence modeling, specifically LSTM and BERT, to detect Android malware in highly imbalanced datasets. The application of BERT...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ca8af5af128deb33abb1b04a970aff52?_a=AQAEufR","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ca8af5af128deb33abb1b04a970aff52?_a=AQAEufR","isAccessibleForFree":true,"articleSection":"System Weakness","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"System Weakness","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/4675577b3a8145dab8e12f25b0751c15","url":"https://daily.dev/sources/systemweakness"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-cgq6knhde","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":2},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"cyber,deep-learning,android,malware,bert","timeRequired":"PT3M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"System Weakness","item":"https://daily.dev/sources/systemweakness"},{"@type":"ListItem","position":3,"name":"Using Sequence Modeling to Detect Android Malware in Highly Imbalanced Datasets"}]} ```