Welcome to AdFlush

Document for paper AdFlush: A Real-World Deployable Machine Learning Solution for Effective Advertisement and Web Tracker Prevention, accepted to the Web Conference 2024, Singapore.

AdFlush is the first advertisement and web tracker blocking Chrome Extension based on Machine Learning prediction. With thorough feature engineering process considering state-of-the-art methodology, AdFlush takes advantage of its lightweight and powerful classification ability to detect and block advertisement and web trackers preserving user privacy in the same time. Please refer to our paper for details of the framework and evaluation results of AdFlush.

Abstract

Conventional ad blocking and tracking prevention tools often fall short in addressing web content manipulation. Machine learning approaches have been proposed to enhance detection accuracy, yet aspects of practical deployment have frequently been overlooked. This paper introduces AdFlush, a novel machine learning model for real-world browsers. To develop AdFlush, we evaluated the effectiveness of 883 features, ultimately selecting 27 key features for optimal performance. We tested AdFlush on a dataset of 10,000 real-world websites, achieving an F1 score of 0.98, thereby outperforming AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84). Additionally, AdFlush significantly reduces computational overhead, requiring 56% less CPU and 80% less memory than AdGraph. We also assessed AdFlush’s robustness against adversarial manipulations, demonstrating superior resilience with F1 scores ranging from 0.89 to 0.98, surpassing the performance of AdGraph and WebGraph, which recorded F1 scores between 0.81 and 0.87. A six-month longitudinal study confirmed that AdFlush maintains a high F1 score above 0.97 without the need for retraining, underscoring its effectiveness.

The demo of exploring real-life web sites and detecting, blocking advertisements and trackers using AdFlush is available here.

Preparing AdFlush

We recommend you to take advantage of our fully build Docker image to explore several processes and results of AdFlush. If not available, you can make yourself an environment to match AdFlush's settings.

• A. (Recommended) Pull our Docker image and simply run the fully built container
• B. (Alternative of A.) Make a python environment from scratch
• C. (Optional) Simple steps to setup a Docker image of AdFlush

A. Within Docker Image

1. Pull the Docker image

Run the following codes to pull the Docker image of AdFlush and start a container.

$ apt-get install Docker.io
$ docker pull chaejinlim98330/adflush:artifact.v2

• If you encounter an error saying Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?., see here and run the command corresponding to your OS to start your Docker daemon.

2. Start the Docker container

On first run:

$ docker run -it --name adflush_con chaejinlim98330/adflush:artifact.v2

On post runs:

$ docker start -i adflush_con

3. Start python venv

container $ source adflushenv/bin/activate
(adflushenv) container $ cd source

(When closing AdFlush)

To exit the docker container:

(adflushenv) container $ exit

B. Within Python Environment

0. Prerequisites

1. This study has been run and tested in Python==3.7.17, in both following environments:

• javac=17.0.7, Windows 10 Pro 22H2
• openjdk==11.0.19, Ubuntu 18.04.6 LTS (Bionic Beaver)

A package used by AdFlush, H2O requires 64-bit JDK of versions over 7. You can download Java as the same version as tested from here.

2. The git repository requires Git LFS (Large File Storage). If you do not have LFS, download it from here.

1. Clone Repository

Setup the directory structure as it is the same as this GitHub repository.

1. Move to the directory you want to import AdFlush.

$ cd path/to/working/directory

2. Within the directory, run the following source codes.

$ git clone https://github.com/SKKU-SecLab/AdFlush.git
$ git lfs pull
$ cd AdFlush

2. Python Venv

Build a python environment

$ python3 -m venv adflushenv
$ source adflushenv/bin/activate
(adflushenv) $ pip3 install -r requirements.txt
(adflushenv) $ cd source

3. Prepare Dataset

Download the all_df_883_train.csv .csv file from here to the directory /path/to/working/directory/adflush/dataset. You can see the proper location marked with all_df_883_train.placeholder.

C. (Optional) Setup a Docker image of AdFlush

Run the following code to build a Docker image as the same steps of AdFlush. The saame codes are writtin in the comments of /AdFlush/Dockerfile. You can see the steps of building Adflush's Docker image.

$ docker build --tag adflush:custom.v1
$ docker run -it adflush:custom.v1
container $ apt-get install libbz2-dev liblzma-dev lzma zlib1g-dev
container $ npm init -y
container $ npm install fs acorn-loose
container $ tar -xvzf Python-3.7.17.tgz
container $ cd Python-3.7.17
container $ ./configure
container $ make altinstall
container $ cd /adflush
container $ python3.7 -m venv adflushenv
container $ source adflushenv/bin/activate
container $ python -m pip install --upgrade pip setuptools
container $ pip install requests tabulate future
container $ pip install -r requirements.txt

1. Exploring the processes of AdFlush

Run the code python main.py -p [your options] to follow processes and explore results of AdFlush. Available options are as below. Note that the process performance-eval requires the arguments -d {dataset} and -m {model}, and train-gan requires the argument -s {subset}.

usage: main.py [-h] -p
               {feature-eng,model-sel,performance-eval,train-gan,extract-new-feat}
               [-d {testset,gan,gnirts,javascript-obfuscator,wobfuscator}]
               [-m {mojo,onnx,custom_mojo,custom_onnx}]
               [-s {adflush,webgraph,adgraph}]

Run AdFlush Experiments

optional arguments:
  -h, --help            show this help message and exit
  -p {feature-eng,model-sel,performance-eval,train-gan,extract-new-feat}
                        Experiment process you want to run
  -d {testset,gan,gnirts,javascript-obfuscator,wobfuscator,custom-gan}
                        Specify dataset during 'performance-eval'
  -m {mojo,onnx,custom_mojo,custom_onnx}
                        Specify model during 'performance-eval' *For custom,
                        follow model-sel process first.
  -s {adflush,webgraph,adgraph}
                        Specify model during 'train-gan'

1. Feature Enginnering Framework of AdFlush

Run python main.py -p feature-eng to follow the steps and reproduce the results of AdFlush's feature engineering framework. The contents are:

1. Point-Biserial Correlation
2. *RFECV(Recursive Feature Elimination with Cross-Validation) Existing Features / New Robust Features
3. Pearson & Spearman
4. RFI-PI mean Importance

You can also see the results of RFECV in figures in /adflush/output. To fetch the figures from the docker container, run the following codes in another terminal.

$ docker cp adflush_con:/adflush/output /path/to/fetch/figures/

• RFECV process requires many hours, and the entire 'feature-eng' process might run over a day.

2. Modeling AdFlush

Run python main.py -p model-sel and follow the steps to reproduce the model of AdFlush upon various datasets. The newly trained AdFlush is saved in /adflush/model/, where the mojo model is AdFlush_custom/(name_of_model) and the ONNX model is AdFlush_custom.onnx.

After running this process, you can evaluate your custom AdFlush model by setting the -m argument as custom_mojo or custom_onnx in 3. Performance Evaluation.

• We must note that the results of the source code may differ from the results in our paper. The ONNX convertion involves compatibility within multiple environments, nessecary for browser extension implementation. However in this way the limit of Opsets in conversion might act as an upperbound in model performance by degrading precise floating point computation.

---

3. Performance Evaluation

Run the following code to reproduce the performance evaluation of AdFlush.

(adflushenv) container $ python main.py -p performance-eval -m {model} -d {dataset}

Samples of running performance evaluation are below:

(adflushenv) container $ python main.py -p performance-eval -m mojo -d testset
[[Performance evaluation process of AdFlush using dataset:  testset , model:  mojo ]]
Loading model...
Inference time elapsed:  1.7641267776489258 seconds for  166032  samples.
Accuracy : 0.9865809000674569
Precision : 0.9890499244032438
Recall : 0.9772003681740679
F1 : 0.9830894407675026
False Negative Rate: 0.02279963182593213
False Positive Rate: 0.007187321444681683
AUROC:  0.9850065233646931
TPR  0.9772003681740679 at FPR  0.007187321444681683

(adflushenv) container $ python main.py -p performance-eval -m onnx -d javascript-obfuscator
[[Performance evaluation process of AdFlush using dataset:  javascript-obfuscator , model:  onnx ]]
Loading Model
Checking model integrity...
Running Inference Session
Inference time elapsed:  0.1128230094909668 seconds for  13695  samples.
Accuracy : 0.9516611902154071
Precision : 0.962189838519102
Recall : 0.9122479462285288
F1 : 0.9365535748514472
False Negative Rate: 0.08775205377147124
False Positive Rate: 0.02302434344645641
AUROC:  0.9446118013910361
TPR  0.9122479462285288 at FPR  0.02302434344645641
Attack Success Rate:  0.048338809784592915
...

4. Generate GAN mutated datasets

Run python main.py -p train-gan -s {subset} to train a new GAN as the robustness evaluation of AdFlush and create a custom mutated dataset.

The output of the code will generate a mutated dataset from the newly trained GAN as GAN_custom_mutated_{subset}.csv. You can also use the mutated dataset to evaluate performance of AdFlush with the -d argument as custom-gan here.

5. Encodings used in AdFlush

The details of encodings used in AdFlush are implemented in /adflush/source/adflush_encodings.py.

• trainCharEmbeddings: Train a custom character embedding dictionary using Word2Vec.
• char2vec_pretrained: Embed a url with AdFlush's pretrained character embedding dictionaries.
• extract_JS_Features: Extract robust features as implemented in AdFlush.
  • input: file name of HTML or JavaScript source code. Place the file within /adflush/source/processing/ directory for proper use.

By running the following code, you can see samples of results of extracting robust features from actual JavaScript and HTML source codes.

(adflushenv) container $ python main.py -p extract-new-feat

New features for processing/sample.js
         ast_depth:  78 ast_breadth:  34445 avg_ident:  0.2485594869073883  avg_charperline:  88165.66666666667 brackettodot:  2.64977523498161 num_requests_sent:  0 num_set_storage:  19 num_get_storage:  3 num_get_cookie:  1 ngram:  {'ng_15_15_0': 0.08983040866800142, 'ng_15_0_12': 0.006168295842656931, 'ng_0_12_4': 0.009259804498881169, 
         ...

New features for processing/sample.html
         ast_depth:  45 ast_breadth:  4809 avg_ident:  0.4763645860537999  avg_charperline:  8068.4 brackettodot:  8.0 num_requests_sent:  0 num_set_storage:  0 num_get_storage:  0 num_get_cookie:  0 ngram:  {'ng_15_15_3': 0.008132865336871842, 'ng_15_3_9': 0.0032103415803441484, 'ng_3_9_15': 0.002910709699512028, 'ng_9_15_3': 0.003039123362725794,
         ...

2. Within Chrome Extension

The following browser extension is developed in npm==9.5.1. For those who run AdFlush within a Docker image, the extension source code is not included in the image thus have to clone this github directory in order to run the Chrome extension.

Setting Up AdFlush

1. Open a Chrome web browser.

2. Click on the options and navigate to Extensions > Manage Extensions.
   

3. Click on Manage Extensions Within the page, click on Load unpacked. If you don't see this button, make sure you have enabled Developer mode on the right side of the page.
   

4. Navigate to path/to/working/directory/AdFlush/extension/dist and select the folder.
   

5. Open your extensions and pin AdFlush to utilize full functionalities.
   

Applying Modification to AdFlush

If you decide to apply some modifications within our chrome extension or want to customize behavior, you must use webpack to repack the extension reflecting your modifications.

1. Open a command line prompt and navigate to path/to/working/directory/AdFlush/extension. Run the source code below to install the packages required for AdFlush chrome extension.

npm install

2. When you are done applying modifications to AdFlush, un the source code below to pack the source codes with appropriate npm packages.

npx webpack --config=webpack.config.js

3. Press the refresh button to reload AdFlush within your chrome extension and you are ready to run the modified AdFlush.
   

3. Dataset

We opensource our AdFlush dataset used within our study. Our dataset consists of top 10K web pages from Tranco list, crawled at the date April 4, 2023. We divided our dataset for training processes and evaluation by 8:2 ratio. We also provide the datasets obtained with our trained GAN or several JavaScript obfuscation and used in robustness evaluation. These are available in ./dataset directory. The identical dataset is also provided here via Zenodo.

• AdFlush_train.csv: Train set of AdFlush
• AdFlush_test.csv: Test set of AdFlush
• all_df_883_: Dataset collected by extracting all possible 883 features
• GAN_mutated_ : GAN mutated test sets for each method
• JS_obfuscated_: JavaScript features obtained by each obfuscation method for AdFlush

(Optional) We collected our dataset from OpenWPM. To run all tasks (Graph building, Feature extraction or Classification) on WebGraph and AdGraph, the crawl data used is collected using a custom version of OpenWPM. Follow the instructions here to setup OpenWPM in your environment.