Abstract
Cloud services are a popular concept used to describe how internet-based services are delivered and maintained. The computer technology environment is being restructured with respect to information preservation. Data protection is of critical importance when storing huge volumes of information. In today's cyber world, an intrusion is a significant security problem. Services, information, and services are all vulnerable to attack in the cloud due to its distributed structure of the cloud. Inappropriate behavior in the connection and in the host is detected using intrusion detection systems (IDS) in the cloud. DDoS attacks are difficult to protect against since they produce massive volumes of harmful information on the network. This assault forces the cloud services to become unavailable to target consumers, which depletes computer resources and leaves the provider exposed to massive financial and reputational losses. Cyber-analyst data mining techniques may assist in intrusion detection. Machine learning techniques are used to create many strategies. Attribute selection techniques are also vital in keeping the dataset's dimensionality low. In this study, one method is provided, and the dataset is taken from the NSL-KDD dataset. In the first strategy, a filtering method called learning vector quantization (LVQ) is used, and in the second strategy, a dimensionality-simplifying method called PCA. The selected attributes from each technique are used for categorization before being tested against a DoS attack. This recent study shows that an LVQ-based SVM performs better than the competition in detecting threats.