Malware Analysis and Detection Using Artifical Intelligence and Machine Learning (AIML).

Malware continues to evolve rapidly, with polymorphic variants that change their characteristics to evade detection, posing a serious challenge to digital security. Artificial Intelligence (AI) and Machine Learning (ML), especially Cyber Security, have emerged as powerful tools for detecting and classifying malware across multiple platforms such as Windows, macOS, Linux, Android, and iOS. This study explores a broad range of AI/ML-based techniques, from traditional shallow learning models to advanced logic-driven architectures, pre-trained networks, and multitask learning frameworks, with a strong focus on making these models interpretable. This research explores evaluation metrics included detection accuracy, false positive and false negative rates, and resilience against adversarial attacks, while one major limitation was lack of standardized benchmark datasets, which hinders fair comparisons and reproducibility. Preventive measures remain critical, including avoiding suspicious links, unknown URLs, unverified applications, APK files, and other untrusted sources to reduce risk of infection. Python is widely regarded as the preferred programming language for implementing hybrid AI/ML cybersecurity systems, supported by a rich ecosystem of tools such as scikit-learn, TensorFlow, PyTorch, pefile, yara-python, Cuckoo Sandbox, and VirusTotal API, Models are like Character-Bert ,SVM,CNN,RNN, Decision Tree and Random Forest with typical workflow involving as collection of malicious and benign samples, static and dynamic feature extraction, and training and deployment of effective detection models..