Dynamic Malware Detection Using Effective Machine Learning Models with Feature Selection Techniques
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 3
Abstract
Dynamic Malware is a type of virus that is self-modifying, which makes it difficult to analyze in the course of its operation. It occasionally changes its behavior based on the existing environment and the context of execution. The goal of this study was to identify and detect dynamic malware in Android devices using effective machine-learning models with feature selection techniques. With new malicious software emerging daily, relying solely on manual heuristic analysis has become ineffective. To address this limitation, the study used dynamic detection methods to detect the events of interest using machine learning models. Some of these measures entailed duplication of an environment in which the behavior of malware could be replicated and then come up with reports. The reports were then transformed into sparse vector models so that other machine-learning techniques could then be applied to them. In this research study seven different models, namely, KNN, DT, RF, AdaBoost, SGD, Extra Trees, and Gaussian NB, were used to train an effective malware detection model to predict the dynamic malware in its early stages. The study showed that Random Forest, Stochastic Gradient Descent, Extra Tree, and Gaussian Naive Bayes classifiers achieved the highest accuracy compared to other models. This study endorses the application of machine learning-based automated behavior analysis for malware detection, about the complexities involved in the dynamic behavioral analysis of malicious software.
Authors and Affiliations
Inam Ullah Khan, Fida Muhammad Khan, Zeeshan Ali Haider, Saba Khattak, Gulshan Naheed, Sana Shaoor Kiani
Keywords
Related Articles
- EP ID EP760439
- DOI -
- Views 31
- Downloads 0
Trust Management for the LPWAN Devicesin a Smart City
The Internet of Things (IoT) is used in several domains like health care, transportation, military, banking, and many more. These applications can lead to the realization of a smart city application. Recently, Low Powe...
Computational Analysisof ModelHousesof Da Kali KORin Matta Swat
Natural disasters such as floods and earthquakes, exacerbated by global warming and environmental degradation, pose significant challenges for modern architecture. This study critically evaluates a rural residential ho...
Predictive Analysis and Email CategorizationUsing Large Language Models
With the global rise in internet users, email communication has become an integral part of daily life. Categorizing emails based on their intent can significantly save time and boost productivity. While previous resear...
An Enhanced Novel Iot-Based Car Accident Detection and AlertSystem
The excessive use of vehicles for our day-to-day tasks in this revolutionized era has become a necessity, making our lives convenient and technology-dependent. This rise in the use of vehicles has led to a greater numb...
Enhancing Face Mask Detection in Public Places with Improved Yolov4 Model for Covid-19 Transmission Reduction
Over the past decade, computer vision has emerged as a pivotal field, focusing on automating systems through the interpretation of images and video frames. In response to the global impact of the COVID-19 pandemic, the...