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It outlines dataset source and core pipeline: feature extraction from APKs via Androguard, data loading, visualization, class balancing, model training (Random Forest, Extra-Tree, XGBoost, stacking, CNN), testing, and output labeling as Malware Alert or Benign.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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dataset is used for Android malware detection in this project?","Question",{"text":63,"@type":64},"The project uses the invesandmal2019 dataset from the University of New Brunswick, containing Android application samples (APK files) labeled as benign or malicious.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which software tools and programming environment are specified?",{"text":68,"@type":64},"It specifies VS Code Studio, Google Colab, Python (version > 3.7), and Google Chrome, with Windows 10 or above as the operating system plus an NVIDIA GPU driver version requirement.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the project implement feature extraction and model training?",{"text":72,"@type":64},"It extracts permissions/features from APK files using Androguard, loads data from a CSV location, visualizes class distribution, balances data using SMOTE, then trains multiple models including Random Forest, Extra-Tree, XGBoost, stacking, and a CNN 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Project Cyber Security  \nSai Hari Parthiban Student ID: 22169148  \nSchool of Computing National College of Ireland  \nSupervisor: Jawad Salahuddin  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name: Sai Hari Parthiban  \nStudent ID: 22169148  \nProgramme: MSc in Cyber Security Year: 2023  \nModule: MSc Research Project  \nLecturer: Jawad Salahuddin  \nSubmission Due  \nDate: 05/04/2024  \nProject Title: Android Malware Detection using Machine Learning and  \nConvolutional Neural Network  \nWord Count: 415 Page Count: 6  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: Sai Hari Parthiban  \nDate: 04/04/2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nConfiguration Manual  \nSai Hari Parthiban  \nStudent ID: 22169148  \n1 Introduction  \nThis document contains all the information and details about the software technology, tools that are used during this research project that is used in the idea of improving the Android Malware Detection using Machine Learning Algorithms and Convolutional Neural Network in the data preparation , feature extraction, implementation and evaluation phases.  \n2 Section 2  \nCode Editor  \nVS code studio 1.85.0  \nGoogle Colab  \nProgram Language  \npython – version >3.7  \nWeb Brower  \nGoogle chrome  \nHardware Specification  \nRam – 4GB  \nDisk Space – Minimum 2GB  \nOS – Windows 10 and above  \nNVIDIA GPU driver version: Windows 461.33 or higher  \n3 Package Details  \nPython libraries used are numpy, pandas, seaborn, scikit learn and etc  \nNumpy – Version 1.21  \nUsed for numerical operations in python. Supports handling large number dimensional arrays and matrices.  \nPandas – Version 1.3.5  \nUsed for data manipulation and offer structured dataframes.  \nSeaborn – Version 0.12.0  \nUsed for statistical data visualization in python like graphs.  \nScikit learn – Version 0.22  \nThis library contribute to various machine learning workflows.  \nTkinter – Version 8.6  \nused to construct basic graphical user interface (GUI) applications.  \n4 Dataset  \nThe dataset [https://www.unb.ca/cic/datasets/invesandmal2019.html](https://www.unb.ca/cic/datasets/invesandmal2019.html), more precisely the\"invesandmal2019\" dataset from the University of New Brunswick, comprises of gathered samples of Android applications (APK files) classified as benign or malicious (or \"malign\") .  \n5 Implementation  \nStep 1: Importing the packages  \nRequired packages and libraries are imported  \nStep 2: Collecting the data  \nStep 3: Extracting features from Androguard  \nThe above code aims to collect all unique permissions requested by the APK files in the provided benign and malignant directories.  \nStep 4: Loading the data  \nDataset is loaded from the desired location file with .csv  \nStep 5: Data Visualization ","cbCaiv2e1bWXRhNU","https://ap.wps.com/l/cbCaiv2e1bWXRhNU","pdf",665222,"English","# 1 Introduction\n# 2 Section 2: Code Editor\n## Program Language\n## Web Brower\n## Hardware Specification\n# 3 Package Details\n# 4 Dataset\n# 5 Implementation\n## Step 1: Importing the packages\n## Step 2: Collecting the data\n## Step 3: Extracting features from Androguard\n## Step 4: Loading the data\n## Step 5: Data Visualization\n## Step 6: Data balancing\n## Step 7: Training the Models\n## Step 8: Testing\n## Step 9: Output\n# References","[{\"question\":\"What dataset is used for Android malware detection in this project?\",\"answer\":\"The project uses the invesandmal2019 dataset from the University of New Brunswick, containing Android application samples (APK files) labeled as benign or malicious.\"},{\"question\":\"Which software tools and programming environment are specified?\",\"answer\":\"It specifies VS Code Studio, Google Colab, Python (version \\u003e 3.7), and Google Chrome, with Windows 10 or above as the operating system plus an NVIDIA GPU driver version requirement.\"},{\"question\":\"How does the project implement feature extraction and model training?\",\"answer\":\"It extracts permissions/features from APK files using Androguard, loads data from a CSV location, visualizes class distribution, balances data using SMOTE, then trains multiple models including Random Forest, Extra-Tree, XGBoost, stacking, and a CNN model.\"}]","Android Malware Detection using Machine Learning and Convolutional Neural Network - Configuration Manual | PDF"]