---
title: "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
source_url: https://systemweakness.com/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-b765edeb0f71
type: article
source: "System Weakness"
published: 2024-02-02T16:35:53.122Z
updated: 2024-05-09T09:21:59.549Z
tags: ["cyber", "deep-learning", "android", "malware", "bert"]
reading_time: 3
upvotes: 2
comments: 0
language: en
---

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# 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: <https://systemweakness.com/using-sequence-modeling-to-detect-android-malware-in-highly-imbalanced-datasets-b765edeb0f71>

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---

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)
