Intelligent Malware Detection Using Machine Learning: A Comprehensive Approach to CyberSecurity

Malware has become an ever-evolving threat to modern digital infrastructures, necessitating advanced detection methods beyond traditional signature-based approaches are often insufficient to combat the rapid evolution of new malware variants, which can evade detection by exploiting previously unseen attack vectors. Malware detection is crucial for ensuring the security of information systems, as malware can cause data breaches, financial losses, and system disruptions. To address this challenge, this project focuses on developing an advanced malware detection system using machine learning techniques, aiming to accurately detect both known and novel malware strains. The system leverages Obfuscated-MalMem2022 dataset contains a mixture of benign and malware samples, malware behaviors such as Ransomware, Spyware, Trojan-Emotet, Trojan-Refroso, Trojan-Zeus, and features. By analyzing a comprehensive dataset of malware characteristics, the system applies algorithms like Voting Ensemble with KNearest Neighbor, Naive Bayes, Decision Tree, Cascading Ensemble with autoencoder and Support Vector Machines and Random Forest with Recurrent neural network ensemble to classify malicious software. The project emphasizes a multi-step pipeline that includes data preprocessing to handle noise and inconsistencies, followed by feature extraction to identify the most relevant attributes of malware behavior. The system incorporates adversarial robustness mechanisms to mitigate attempts by malware to evade detection. Model evaluation metrics, such as precision, recall, and F1 score, will guide the optimization process, ensuring a scalable and efficient detection system.