Malware Analysis and Detection Using Machine Learning Algorithm
Malware Analysis and Detection Using Machine Learning Algorithm
IEEE BASE PAPER TITLE:
Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection
IEEE BASE PAPER ABSTRACT:
Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets. As the Android ecosystem endures to produce, therefore is the risk of malware attacks on these devices. Identifying Android malware is vital for keeping user data, privacy, and device integrity. Android malware detection utilizing deep learning (DL) signifies a cutting-edge system for the maintenance of mobile devices.
DL approaches namely recurrent neural network (RNN) and convolutional neural network (CNN) are best in automatically removing intricate designs and behaviors in Android app data. By leveraging features such as application programming interface (API) call sequences, code patterns, and permissions, these approaches are efficiently differentiated between benign and malicious apps, even in the face of previous unseen attacks. This study presents an Intelligent Pattern Recognition using an Equilibrium Optimizer with Deep Learning (IPR-EODL) Approach for Android Malware Recognition.
The purpose of the IPR-EODL approach is to properly identify and categorize the Android malware in such a way that security can be achieved. In the IPR-EODL technique, the data pre-processing step was applied to convert input data into a compatible setup. In addition, the IPR-EODL technique applies channel attention long short-term memory (CA-LSTM) methodology for the recognition of Android malware.
To enhance the solution of the CA-LSTM algorithm, the IPR-EODL system employs the Equilibrium optimization (EO) algorithm for the hyper-parameter tuning method. The experimentation evaluation of the IPR-EODL model can be verified on a benchmark Android malware database. The extensive results highlight the significant result of the IPR-EODL approach to the Android malware detection process.
PROJECT (Malware Analysis and Detection Using Machine Learning Algorithm) OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Extra Tree Classifier and Logistic Regression.
OUR PROPOSED PROJECT ABSTRACT:
The project “Malware Analysis and Detection Using Machine Learning Algorithm” aims to enhance cyber-security measures by accurately identifying malicious software through advanced machine learning techniques. Developed using Python, the project employs the Flask web framework for backend operations and utilizes HTML, CSS, and JavaScript for a responsive and interactive frontend interface.
Two machine learning models are central to this project: the Extra Tree Classifier and Logistic Regression. The Extra Tree Classifier model demonstrates superior performance, achieving a training accuracy of 97.42% and a testing accuracy of 97.23%. In comparison, the Logistic Regression model achieves a training accuracy of 94.84% and a testing accuracy of 93.67%. Both models are trained and validated using the TUNADROMD dataset, which comprises 4465 instances and 242 attributes, with the target classification attribute distinguishing between malware and goodware.
For the analysis, a subset of 23 attributes was selected based on their relevance and impact on the classification task. This strategic selection aims to optimize model performance while reducing computational complexity. The project’s results indicate that the Extra Tree Classifier is highly effective in distinguishing between malicious and benign software, offering a reliable tool for malware detection in real-world applications.
Overall, this project demonstrates the efficacy of machine learning algorithms in cyber-security, providing a robust solution for malware detection that can be integrated into various digital security infrastructures.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
SOFTWARE REQUIREMENTS:
- Operating System : Windows 10 / 11.
- Coding Language : Python 3.10.9.
- Web Framework : Flask.
- Frontend : HTML, CSS, JavaScript.
REFERENCE:
MOHAMMED MARAY, MASHAEL MAASHI, HAYA MESFER ALSHAHRANI, SUMAYH S. ALJAMEEL, SITELBANAT ABDELBAGI, AND AHMED S. SALAMA, “Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection”, IEEE ACCESS, 2024.
