Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security

Abstract

Secure data transmission in wireless mesh networks is a necessary attribute for machine learning-based intrusion detection systems (IDS). Numerous attacks may have an adverse effect on the system's computing efficiency. In order to accurately detect an attack and enable data protection, Whale with Cuckoo search optimization (WCSO) based quantum neural network (QNN) and elliptical curve cryptography (ECC) is presented. Whale optimization algorithm (WOA) is used to choose the features in the network data that aid in precisely detecting intrusions. To identify attacks, the optimized quantum network which combines the WOA approach with the feed-forward and backpropagation algorithms, is used. Sensitive data retrieving requires an encryption procedure that is enabled by the ECC algorithm, which could safely save the data files in the server, in order to, secure the documentation with security measures. In the event that the data owner maintains sensitive data on a server, the document is encrypted using the encryption method. To determine the best key optimized ECC is used. The QNN with WOA-based IDS framework is a solid option for real-time intrusion detection analysis with high accuracy of 98.5%. Thus, the study has demonstrated that the suggested effort will also provide better secure data storage, resolving security concerns.

Keywords

• Intrusion Detection System (IDS)
• Data Security
• Quantum Neural Network (QNN)
• Whale Optimization Algorithm (WOA)
• Elliptical Curve Cryptography (ECC)
• Cuckoo Search Optimization (CSO)