Abstract
At present times, Cloud Computing (CC) becomes more familiar in several domains such as education, media, industries, government, and so on. On the other hand, uploading sensitive data to public cloud storage services involves diverse security issues, specifically integrity, availability and confidentiality to organizations/companies. Besides, the open and distributed (decentralized) structure of the cloud is highly prone to cyber attackers and intruders. Therefore, it is needed to design an intrusion detection system (IDS) for cloud environment to achieve high detection rate with low false alarm rate. The proposed model involves a binary grasshopper optimization algorithm with mutation (BGOA-M) as a feature selector to choose the optimal features. For classification, improved particle swarm optimization (IPSO) based NN model, called IPSO-NN has been derived. The significance of the IPSO-NN model is assessed using a set of two benchmark IDS dataset. The experimental results stated that the IPSO-NN model has achieved maximum accuracy values of 99.36% and 97.80% on the applied NSL-KDD 2015 and CICIDS 2017 dataset. The obtained experimental outcome clearly pointed out the extraordinary detection performance of the IPSO-NN model over the compared methods.
Highlights
Nowadays, cloud computing (CC) has become a major revolution in the field of information technology (IT) with a rapid development of computing networks
Under the applied CICIDS 2017 dataset, it is noticed that the improved particle swarm optimization (IPSO)-neural network (NN) model has achieved optimal detection performance by offering a minimum false acceptance rate (FAR) and false negative rate (FNR) values of 0.091 and 0.012
This paper has introduced a new feature selection (FS) with optimal NN based intrusion detection system (IDS) model for cloud environment to achieve high detection rate with low false alarm rate
Summary
Cloud computing (CC) has become a major revolution in the field of information technology (IT) with a rapid development of computing networks. [12] proposed a network IDS (NIDS) for Cloud atmosphere under the application of Multilayer Perceptron (MLP) as well as Particle Swarm Optimization Algorithm (PSO) to forecast intrusions and attacks. [13] projected a NIDS in CC platform based on Fuzzy C Means (FCM) technique for detecting intrusion actions from common nature, and, oppose network usage in Cloud resources as well as services from diverse types of threats and attacks. This paper presents a new feature selection (FS) with optimal neural network (NN) based IDS model for cloud environment. In wrapper-based models, identifying optimized features from FS is considered to be a most significant task This happens as the selected subset needs a validation process by applying learning methodologies at each optimization step. Positive components assume the measures of same components of targets; otherwise, grasshoppers are identified from the search space as well as from random assumption of component relied on likelihood of maximum value in the following
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