Abstract
Today, cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major issues facing cloud service providers and their customers. Since cloud computing is a virtual pool of resources provided in an open environment (Internet), cloud-based services entail security risks. Detection of intrusions and attacks through unauthorized users is one of the biggest challenges for both cloud service providers and cloud users. In the present study, artificial intelligence techniques, e.g. MLP Neural Network sand particle swarm optimization algorithm, were used to detect intrusion and attacks. The methods were tested for NSL-KDD, KDD-CUP datasets. The results showed improved accuracy in detecting attacks and intrusions by unauthorized users.
Highlights
Uploading sensitive data to public cloud storage services poses security risks such as accessibility, confidentiality and integration to organizations
These results prove that the proposed method is more accurate within testing and training data compared to neural network and our criterion for evaluation of precision rate of the anomaly or intrusion detection system is the preciseness of the test data
A scenario related to cloud computations and its risks was indicated and the position of the intrusion detection system was specified for internal and external attacks in order to have the most efficiency and security
Summary
Uploading sensitive data to public cloud storage services poses security risks such as accessibility, confidentiality and integration to organizations. Using firewall and intrusion detection system is the only permanent solution to protect users’ data and cloud resources. Intelligent and meta-heuristic algorithms are the most commonly used attack detection techniques. Meta-heuristic algorithms can be used either to analyse attack database or to optimize and increase the accuracy of the classifiers. These methods are reliable and suitable to detect attacks and anomalies. MLP was used to classify attacks and Particle Swarm algorithm was employed to optimize and increase the accuracy of this classifier.
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