SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud

Abstract

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan, etc are the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity, etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

KEYWORDS:

• Deep learning
• Intrusion detection
• IoT
• Machine learning
• Optimal homomorphic encryption
• Secure storage

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

J. Thresa Jeniffer

J Thresa Jeniffer obtained the bachelor degree in information technology, in the year 2012 from St Joseph's College of Engineering, Anna University. She also received her master's degree in computer science and engineering from the same college in 2014. Currently, she is working in the Department of Information Technology, St Joseph's College of Engineering, Chennai, India. She has seven years of teaching and research experience. Her research interests include IoT, machine learning, wireless sensor networks, network security and grid computing.

A. Chandrasekar

A Chandrasekar is head of the Department of CSE at St Joseph's College of Engineering, Chennai, Tamil Nadu. He has teaching experience of over 21 years in engineering colleges. He guided more than 30 research scholars and more than 50 ME students. He published over 110 research articles in refereed international and national journals and he is guiding research scholars and ME students in the areas of network security, cloud security, data mining, artificial intelligence and big data analysis. Email: [email protected]

S. Jothi

S Jothi obtained her Bachelor of Engineering degree in computer science and engineering from Madurai Kamaraj University in 2003. Then she obtained her Master of Engineering in computer science and engineering from Annamalai University in 2005 and PhD in wireless sensor networks from Anna University Chennai in 2017. Currently, she is working in the Department of Computer Science and Engineering at St Joseph's College of Engineering, Chennai. Her specializations include wireless sensor networks, mobile ad-hoc networks, big data analysis and image processing. Her current research interests are machine learning and deep learning. She is a Member of IEEE, Life Member of NCSSSASSOC, ISTE, CSI, ICSES, CRSI, IAENG, CSES, IACSIT and Fellow Member in ISRD. Email: [email protected]