Affiliation:
1. Department of Computer Science and Engineering Jawaharlal Nehru Technological University Anantapur Ananthapuramu Andhra Pradesh India
2. Department of Computer Science & Engineering K.S.R.M College of Engineering, Kadapa, Affiliated to JNTUA Ananthapuramu Kadapa Andhra Pradesh India
Abstract
SummarySecurity becomes the key concern in a cloud environment, as the servers are distributed throughout the globe and involve the circulation of highly sensitive data. Intrusions in the cloud are common because of the huge network traffic that paves the way for intruders to breach traditional security systems with sophisticated software. To avoid such problems, intrusion detection systems (IDSs) have been introduced by various researchers. Each IDS was developed to achieve a particular objective, that is, providing security by detecting intrusions. Most of the available IDS are inefficient and are unable to provide accurate classification. Also, some of them are computationally expensive to be implemented in practical scenarios. This article proposes a new and efficient IDS framework that can accurately classify the intrusion type through effective training to address the existing drawbacks. The proposed framework, named flow directed deep belief network (FD‐DBN), involves three main phases: pre‐processing, clustering, and classification. In pre‐processing, certain data mining operations are carried out to clean the data. The clustering phase is carried out using the game‐based k‐means (GBKM) clustering algorithm. The clustered data is then provided as input to the FD‐DBN classification framework, where the training process is carried out. The deep belief network (DBN) training is performed with dataset features, and the flow direction algorithm is adopted for tuning the weight parameters of DBN. Through tuning, the model yielded accurate classification outcomes. The simulations are done in Python 3.6, and the results proved that the proposed framework is much more effective than the existing IDS frameworks.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Cited by
1 articles.
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