A Multi-Layer Feed Forward Network Intrusion Detection System using Individual Component Optimization Methodology for Cloud Computing

摘要

Cloud Computing has provided opportunities for organizations to get rid of their infrastructure and instead utilize the services from the cloud vendors. However, the openness, the multi-tenancy nature of the cloud and the volume of the critical data that it stores lures the intruders to launch attacks on the cloud. To counter such attacks and protect the critical data in the cloud, Network Intrusion Detection System (NIDS) is used in the cloud environment. NIDS can detect these attacks in a timely manner and help to minimize the damages to the cloud resources. Various researchers have proposed NIDS models for cloud using machine learning techniques. However, the major characteristics of the cloud that can impact the performance of NIDS are the high volume of incoming network traffic and the high dimensionality of this traffic. NIDS in the cloud must have ability to process this high volume of traffic quickly and accurately. One way to do this is by reducing the number of features in the traffic data so that the NIDS have fewer features to process. However, NIDS must be able to predict the attacks using fewer features but with higher accuracy. This research work proposes a Multi-layer NIDS based on Individual Component Optimization Technique where each component is optimized individually and independently before integrating them to create a multi-layer NIDS. This model uses only 7 features from the dataset to predict the attacks with higher accuracy. Proposed model was evaluated repeatedly using NSL-KDD dataset and it outperformed the exiting Network Intrusion Detection systems in terms of number of features as well as accuracy.