Description
My problem statement is In the face of ever-evolving malware, cybersecurity demands robust solutions that can effectively detect and predict malicious activities. While traditional methods have limitations, deep learning offers promising advancements. However, there is a need for a comparative study on the efficacy of various deep learning architectures, including Multi-Layer Perceptron (MLP) classifiers, in conjunction with powerful frameworks like TensorFlow and Keras, for malware detection and prediction. The study aims to implement and evaluate the performance of these models against benchmarks, focusing on the ability to handle complex patterns and adapt to changing malware characteristics. Utilizing a combination of static and dynamic analysis, the project will explore feature extraction and representation learning to optimize model performance. The ultimate goal is to develop a versatile and scalable system that can dynamically update its detection capabilities to counteract emerging malware threats.I need complete project while involves providing dataset files,csv files, methodology, dataset preparation,preprocessing, features extraction,ppts, discussions, and conclusions. Provide programming code for the problem statementI will provide sample files and links please follow it and complete the project
