Detection of Malware and Adware Using Machine Learning: A Practical Approach

The paper presents a comprehensive approach to detecting malware and adware using machine learning techniques. It outlines the methodology involved in dataset preparation, feature engineering, model selection, and evaluation. A diverse dataset comprising instances of both malware and benign software was curated, ensuring representativeness and generalizability. Various machine learning algorithms, including logistic regression, decision trees, and neural networks , were employed to build and evaluate the detection models. The results demonstrate the superiority of neural network-based approaches over traditional methods, with significant improvements in accuracy, precision, recall, and F1 score. Evaluation metrics extended beyond conventional measures to include ROC-AUC score, false positive rate (FPR), and true negative rate (TNR), providing a comprehensive assessment of model performance. The research highlights the robustness and scalability of the model through extensive testing on large-scale datasets, indicating its suitability for real-world deployment in diverse environments. Future directions include enhancing interpretability, optimizing hyperparameters, and integrating advanced techniques like ensemble learning and deep learning architectures. Overall, the research contributes to advancing the state-of-the-art in cybersecurity and holds potential for practical applications in threat detection and mitigation.