Machine Learning for Cloud Security: A Systematic Review

The use of several machine learning methods for cloud-based intrusion detection, notably with the NSL KDD dataset. The methods used are J48 (88% accuracy), PART (88%), Decision Table (93%), Decision Stump (90%), and AODE (99% accuracy). The module also includes various critical capabilities, such as numeric to nominal conversion, feature selection approaches such as information gain, intrinsic information, and gain ratio, and feature selection methods. The machine learning component includes the Average One Dependency Estimator (AODE) classifier, which is notable for its ability to handle complex datasets. Through extensive examination, these methodologies provide powerful intrusion detection capabilities in cloud environments, which are critical for guaranteeing the security and integrity of sensitive data and systems. Cloud computing has developed as a game-changing concept in information technology, providing scalable and on-demand access to a shared pool of resources over the Internet. As organizations shift their key data and apps to the cloud, the security of these assets becomes increasingly important. The dynamic nature of cloud computing creates a new set of security concerns, ranging from data breaches to unauthorized access, necessitating a thorough awareness of threats and effective mitigation techniques. This comprehensive literature review digs into the huge corpus of research on cloud computing security, with the goal of providing a synthesis of the most common risks and the changing landscape of mitigation techniques.