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# System prepended metadata

title: 6/27 進度報告

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# 分工
### javascript detection -> 凱威
### phishing detection -> 品睿
### google extension -> 長崎

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# Google extension dev

Reading https://developer.chrome.com/docs/extensions/mv3/

# phishing detection 

### use dataset and test on google colab
input good / bad
cut the data to 0.1 -> 50000
![](https://hackmd.io/_uploads/SkaEMpw_2.png)

The numbers of good and bad
almost 7:3

![](https://hackmd.io/_uploads/r1hLzpD_2.png)

preprocess the words as input

![](https://hackmd.io/_uploads/SkB_M6P_n.png)

delete stopwords and train test split

![](https://hackmd.io/_uploads/B1nYf6Dd3.png)

accuracy of random forest and knn

![](https://hackmd.io/_uploads/S1t2MTwOh.png)

accuracy of svm and xgboost

![](https://hackmd.io/_uploads/S1ApGTvun.png)

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# Javascript detection
* Learning prerequiste knowledge
    * Neural Network
        * Python code
![](https://hackmd.io/_uploads/H1oXwJ_dh.png)
![](https://hackmd.io/_uploads/Hy9Vvkd_h.png)
        * Model traning evaluation and prediction result
![](https://hackmd.io/_uploads/BJsrD1ddh.png)
        * Testing data
![](https://hackmd.io/_uploads/H1VLvk_u2.png)

    * Word2Vec
        * Python code
![](https://hackmd.io/_uploads/SyPwPJu_h.png)
        * Prediction result
![](https://hackmd.io/_uploads/BkGuvJOu3.png)

* Later work
    * Tried to perform word2vec by Fasttext
    * Training LSTM model

* References 
    * https://youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO
    * https://radimrehurek.com/gensim/models/word2vec.html
    * https://www.kaggle.com/datasets/reihanenamdari/youtube-toxicity-data?resource=download