Supervised Learning Methods and Applications in Medical Research

Ming, Yung Ching; Yuan, Lily

doi:10.53759/181x/jcns202202005

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Additionally, cloud technology enables users to import and export data via web object storage, even if this data may contain sensitive information like employee profiles or corporate data [12]. The security of data shared via the internet is still susceptible to attacks like brute force attacks and occlusion attacks, despite the cloud's abundance of services and vast storage capacity [13] [14]. Additionally, hacking is another risk that can result in data loss or damage during data transport and storage [15].…”

Section: Introductionmentioning

confidence: 99%

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

Thilagam

Aruna

2023

JMC

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

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