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:
Based on our application attributes and actions, a machine learning model is created to detect possible malicious android applications.
Usually, android applications are wrapped as an APK (Android Package Kit) file .We must bring out the features from the APKs:
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.
We consider algorithms for binary classification (benign vs. malicious) like,
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.
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.
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.
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.
This involves the document findings, capable future enhancements and challenges.
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.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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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