Android Malware Detection Using Machine Learning Projects

The malware detection using machine learning in android applications helps us in self-operating the security assessment process and act as a shield to users from harmful applications. Our ultimate goal is to develop a fantastic research work our research team will explain the concepts clearly. We have worked on many topics in this area a few are explained below. Get the best dissertation services for your Android Malware Detection Using Machine Learning Projects.

The following guideless are essential to approach such a project:

  1. Objective Definition

Based on our application attributes and actions, a machine learning model is created to detect possible malicious android applications.

  1. Data Collection :
  • Malware Datasets: This datasets provides us good-natured and malicious android applications. Sources such as Contagio, Drebin and Androzoo .
  • Application Attributes: The permissions are extracted, Application Programming Interface (APIs), Intents and other visible details of file.
  1. Feature Extraction :

Usually, android applications are wrapped as an APK (Android Package Kit) file .We must bring out the features from the APKs:

  • Static Analysis: The application should examine before the execution process. It extracts features for us like activity components, service components and requested permissions.
  • Dynamic Analysis: This is optional, but it has more enhanced features. It implements the application in a sandbox environment and observes its behaviour, system calls and activity of network.
  1. Data Preprocessing
  • Encoding: The conversion of categorical features (permission names) into numerical format is done by us using the encoding process and the techniques involves like one-hot encoding.
  • Feature Selection: The every gained feature is not relevant to each other. We use tools such as, tree-based methods to select relevant features, mutual information and correlation.
  • Normalization/Standardization: Make confirm that the feature must be on the similar scale.
  1. Exploratory Data Analysis (EDA)

The distribution of good-natured vs. malicious models is being learned by us with the help of EDA. It represents the process of various features are assigned among the benign and malicious samples.

  1. Model Selection

We consider algorithms for binary classification (benign vs. malicious) like,

  • Decision Trees and Random Forest
  • Gradient Boosting Machines
  • Support Vector Machines (SVM)
  • Neural Networks
  • Deep Learning: This is optional .If we work in studying the bytecode as an image; Convolutional Neural Networks (CNNs) is used. For sequential data, Recurrent Neural Networks (RNNs) are used.
  1. Training and Validation

The datasets are divided by us into training, validation, and test sets. We train model on the training data and the performance of model is explored by using validation set.

  1. Model Evaluation
  • Classification Metrics: Some classification metrics are Recall, F1-score, ROC-AUC, Accuracy and Precision. By confusion matrix, it detail about the types of error that we made.
  1. Optimization and Hyper parameter Tuning

We utilize random search or grid search for hyper parameter tuning. It prevents over fitting through regularization. If there are lot of variations in-between benign and malicious samples and to tackle class imbalance, make use of methods like SMOTE or ADASYN.

  1. Deployment

To monitor and rate applications, an API is developed by us or hybrid into android app stores or security solutions. Real-times scanning solutions get beneficial by this integration process.

  1. Feedback and Continuous Learning

The model is frequently updated with new data depends on the exploring nature of malware. We collect reviews from real-world deployment to learn and enhance false excuses about positives and negatives.

  1. Conclusion and Future Work

This involves the document findings, capable future enhancements and challenges.

  • Integration with Heuristics: By integrating models with heuristic checks results in more robust solution.
  • Behavioural Analysis: For advanced detection, we observe the behaviour of model in over time.


  • Updating Model: Malware patterns are evolving frequently and rapidly, so the models are re-trained by us periodically with advanced data.
  • Feature Interpretability: By contributing the security context, we able to explain the difficulties faced by the model to categorize as an app like malware. The methods like tree-based models and SHAP which helps in clarification.
  • Privacy Concerns: This must confirmed by us, that the analysis respects user privacy and it did not permit the sensitive information without the clear-cut permissions.

Using Machine learning, a well-implemented or executed Android Malware detection system is created which is more powerful in addition of cyber security tools, it offers us an extra layer for protection to end-users.

Hurry up and make use of our experienced service and add value to your research work, multiple revising and editing takes place to avoid plagiarism.

Android Malware Detection Using Machine Learning Projects Thesis Ideas

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Android Malware Detection Using Machine Learning Projects Ideas
  1. An Analysis of Android Malware and IoT Attack Detection with Machine Learning


Internet of Things, Security, Malware Attacks, Android Malware dataset, Machine Learning

            An identification process of IoT malware attacks is carried out in our study by employing various ML methods such as Naive Bayes (NB), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). We constructed a ML framework by utilizing a set of Android malware data samples and various good applications. Results show that, Decision Tree algorithm achieved greater outcomes than others.

  1. Machine Learning Approaches for Analysing Static features in Android Malware Detection


Android, Malware, CICInvesAndMal2019, Random Forest (RF), K-NN, Naive Bayes, Trojan, Ransomware, Adware

            In the process of Android malware detection, we utilized Principal Component Analysis (PCA) to select the relevant features. We trained and examined the dataset by employing several ML techniques including Naive bias, Decision tree (DT), Random Forest (RF), and k-NN. From the analysis, we conclude that, Random forest method is considered as an optimal classifier in both binary and category classification.

  1. A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features


Co-existence, FP-growth

            A machine learning framework for the purpose of android malware identification is recommended in our study that is related to co-existence of static features. We extracted the important co-existed features by utilizing association rule mining based method named FP-growth technique. We examined our suggested framework by employing traditional ML approaches. At last, RF method and co-existence of features provides efficient outcomes.

  1. Malware Detection in Android Based Devices by a Hybrid Approach Using Machine Learning Techniques


Ensemble Learning

            ML based android malware detection models are reviewed in our research. Here we discussed about framework of android apps, safety approaches, android model patterns and classification of malware. We described various procedures that exist in many research studies, they are data preprocessing, feature selection, ML frameworks and techniques and identification accuracy. At last, various future hazards in android malware identification are evaluated.

  1. A Study on Android Malware Detection Using Machine Learning Algorithms


Android malware, Malware detection, Permissions, Activities, Smartphone protection

            We constructed an identification model based on ML approaches by integrating various application characteristics denoted as permission and activities that are acquired at the time of app installation. We utilized Androguard tool to extract permissions and activities of every apps. After that, we categorize the apps as benign or malicious by using these extracted features. As a result, RF provides better performance and KNN provides least performance among others.

  1. Android Malware Detection Using Machine Learning Classifiers


Android malware detection, Feature selection

            Our article proposed a ML based model for android malware identification. We performed random sampling to retrieve a balanced dataset from CICAndMal2017 dataset. Then we obtained the most relevant features by employing feature engineering process. By utilizing the chosen features, we trained the ML techniques. As a consequence, Random Forest technique achieved greater end results.

  1. Detection of Android Malwares on IOT Platform Using PCA and Machine Learning


Dynamic features, Static features, PCA, Deep learning

            For malware detection analysis, we utilized real devices such as android smart phones, watches and tablets in our study rather than using genymotion. We extracted the features from normal and harmful applications. To minimize the size of dataset, we employed PCA technique to extract only the relevant features. Then we input the extracted features into DL framework with various hidden layers. However, our suggested framework offers best end results.

  1. A Comparative Analysis of Machine Learning Algorithms for Android Malware Detection


Information Security

            A comparative analysis for various ML approaches is carried out in our work and their performances related to android malware identification are evaluated.  We normalize the features by using Synthetic Minority Oversampling Technique (SMOTE). We also utilized PCA approach in our work. We constructed a LGBM framework to detect and categorize the android malware into different categories.

  1. An Ensemble Approach Based on Fuzzy Logic Using Machine Learning Classifiers for Android Malware Detection


Mobile security, fuzzy logic

            We suggested a fuzzy logic-based dynamic ensemble (FL-BDE) framework to identify the android malware. Our framework comprises of ML processing power and Mamdani-type fuzzy inference system decision making power. We employed various ML methods like LR, BP), BDT, NN, DF and SVM. We performed the experimental analysis for our suggested model and ML techniques and it is considered as an efficient malware detection approach.

  1. DynaMalDroid: Dynamic Analysis-Based Detection Framework for Android Malware Using Machine Learning Techniques


Vulnerabilities, Dynamic analysis, System calls

            A dynamic analysis related model named DynaMalDroid is recommended in our research for the detection of harmful applications exists in android framework. By using dynamic analysis, various apps are extracted. We selected the relevant features by using feature engineering method. Several ML methods like RF, DT, LR, SVM, NB, KNN, and AdaBoost are employed to detect the malicious applications. In that, AdaBoost and SVM outperform others.


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