G. Anbu Selvi

Work place: Department of Computer Science and Engineering, Sathyabama institute of science and technology, Chennai, Tamilnadu, India

E-mail: anbuselvi.cse@sathyabama.ac.in


Research Interests: Deep Learning, Network Security, Machine Learning


G. Anbu Selvi obtained her Bachelor’s degree in CSE from RVS College of Engg., Madurai Kamaraj University. Then she obtained her Master’s degree in Computer Science Engg in SRM University, Ramapuram, Chennai and doing her PhD in Computer Science majoring in the field of Machine learning in SRMIST, Kattankulathur, Chennai, India. Currently, she is a assistant professor of Computer Science Engg, Sathyabama Institute of Science and Technology. Her specializations deep learning, Machine learning and Network security.

Author Articles
A Cryptographic based I2ADO-DNN Security Framework for Intrusion Detection in Cloud Systems

By M. Nafees Muneera G. Anbu Selvi V. Vaissnave Gopal Lal Rajora

DOI: https://doi.org/10.5815/ijcnis.2023.06.04, Pub. Date: 8 Dec. 2023

Cloud computing's popularity and success are directly related to improvements in the use of Information and Communication Technologies (ICT). The adoption of cloud implementation and services has become crucial due to security and privacy concerns raised by outsourcing data and business applications to the cloud or a third party. To protect the confidentiality and security of cloud networks, a variety of Intrusion Detection System (IDS) frameworks have been developed in the conventional works. However, the main issues with the current works are their lengthy nature, difficulty in intrusion detection, over-fitting, high error rate, and false alarm rates. As a result, the proposed study attempts to create a compact IDS architecture based on cryptography for cloud security. Here, the balanced and normalized dataset is produced using the z-score preprocessing procedure. The best attributes for enhancing intrusion detection accuracy are then selected using an Intelligent Adorn Dragonfly Optimization (IADO). In addition, the trained features are used to classify the normal and attacking data using an Intermittent Deep Neural Network (IDNN) classification model. Finally, the Searchable Encryption (SE) mechanism is applied to ensure the security of cloud data against intruders. In this study, a thorough analysis has been conducted utilizing various parameters to validate the intrusion detection performance of the proposed I2ADO-DNN model.

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