LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security

Cloud Computing (CC) is a relatively new technology that allows for widespread access and storage on the internet. Despite its low cost numerous benefits, cloud still confronts several obstacles, including data loss, quality concerns, security like recurring hacking. The of stored in has become major worry both Service Providers (CSPs) users. As result, powerful Intrusion Detection System (IDS) must be set up to detect prevent possible threats at an early stage. Intending develop novel IDS system, this paper introduces optimization concept named Lion Mutated-Genetic Algorithm (LM-GA) with hybridization Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM). Initially, input text preprocessed balanced avoid redundancy vague data. then subjected hybrid Deep (DL) models namely CNN-LSTM model get output. Now, intruded are discarded non-intruded secured using Advanced Encryption Standard (AES) encryption model. Besides, optimal key selection done by proposed LM-GA cipher further via steganography approach. NSL-KDD UNSW-NB15 datasets used verify performance LM-GA-based terms average intrusion detection rate, accuracy, precision, recall, F-Score.