Securing the Data in Cloud Environment Using Parallel and Multistage Security Mechanism

Goyal, Ranjan; Manoov, R.; Prabu, S.; Swarnalatha, P.

doi:10.1007/978-981-15-0184-5_80

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feasibility of the anomaly-based design was evaluated using simulation tests that demonstrated the effectiveness of the proposed design, reflected by low false negative detections (<2%) with zero false positive detections. [14] had proposed a parallel and multistage security mechanism (PMSSM) that consists of intrusion identification and authentication along with encryption strategies. This multi-staging security measures significantly increases the probability of intrusion detection to enhance the data security of the cloud environment.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Approach-Based Machine Learning to Mitigate DDoS Attack in Cloud Computing

Alzahrani¹

2020

International Journal of Engineering Research and Technology

View full text Add to dashboard Cite

With the emerging growth of cloud computing technology and on-demand services, users can access cloud services and software freely and applications based on the "pay-as-you go" concept. This innovation reduced service costs and made them cheaper with high reliability. One of the most significant characteristics of the cloud concept is on-demand services. One can access the applications of cloud computing at any time at a much lower cost. In addition to providing cloud users with much-needed services, the cloud also gets rid of security concerns which are not tolerated by the cloud. One of the most security problems in the cloud environment is Distributed Denial of Service (DDoS) attack that are responsible for overloading the cloud servers. This paper highlights a prevention technique (CS-ANN) which detect the DDoS attack and makes the server side more sensitive by integrating a Cuckoo Search (CS) approach with the Artificial Neural Network (ANN) approach. The cloud user features, along with the attacker features, are optimized using CS as a natureinspired approach. Later on, these optimized features are passed to the ANN structure. The trained features are stored in the database and used during testing process to match the test features with the trained features and hence provide results in terms of attacker and normal cloud users. The test results of CS-ANN show a True Positive Rate (TPR), False Positive Rate (FPR) and detection accuracy of 0.99, 0.0105 and 0.9865% respectively. The proposed approach outperforms in contrast to the other two state-of-the-art techniques.

show abstract

Section: Literature Reviewmentioning

confidence: 99%