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An effective optimization enabled deep learning based Malicious behaviour detection in cloud computing

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Abstract

The quick deployment of cloud with computing platforms has driven novel tendencies which shifted operations of networks. However, the cloud is facing several security issues and is susceptible because of suspicious tasks and attacks. This paper devises a new method to detect malicious activities in cloud. Here, first step is the simulation of cloud patterns, wherein the data outsourced by the users are utilized for detecting malicious behaviors. The data pre-processing is done to eradicate unnecessary data and noise contained in the data and is performed using a min–max normalization process. The selection of imperative features is done using distance measure, namely Hellinger distance for mining the essential features. The augmentation of data is performed to make the data appropriate for improved processing. The malicious behavior detection is performed by exploiting the Deep Q network wherein training is performed with Autoregressive chimp optimization algorithm (AChOA), which is developed by integrating chimp optimization algorithm (ChOA) and Conditional Autoregressive Value at risk (CAViaR). The proposed AChOA-based Deep Q network outperformed with the highest testing accuracy of 94%, sensitivity of 94.1%, and specificity of 91.4%.

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Abbreviations

CU:

Cloud user

IDS:

Intrusion Detection Systems

EMRs:

Electronic Medical Records

HMM:

Hidden Markov Model

SVM:

Support Vector Machines

NN:

Neural Networks

TPA:

Third-Party Auditor

CNN:

Convolutional Neural Network

VHD:

Virtual Honeypot Device

AFO:

App feature ontology

VMs:

Virtual machines

En-ABC:

Ensemble Artificial Bee Colony-based Anomaly Detection Scheme

TA-Edge:

Trusted Authority for Edge Computing

SDN-ADS:

Software-Defined Network-based Anomaly Detection System

PSO-PNN:

Particle Swarm Optimization-based probabilistic neural network

NB:

Naive Bayes

CSP:

Cloud Service Provider

AMD:

Agent-based malware detection

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The authors declare they have no funding in this manuscript.

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Authors and Affiliations

  1. MIT World Peace University, Pune, India

    Sukhada Bhingarkar

  2. SRM Institute of Science and Technology, Chennai, India

    S. Thanga Revathi

  3. Department of Information Technology, Aditya College of Engineering and Technology, Andhra Pradesh, Surampalem, India

    Chandra Sekhar Kolli

  4. Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia

    Hiren K. Mewada

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  1. Sukhada Bhingarkar
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  2. S. Thanga Revathi
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  3. Chandra Sekhar Kolli
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  4. Hiren K. Mewada
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Bhingarkar, S., Revathi, S.T., Kolli, C.S. et al. An effective optimization enabled deep learning based Malicious behaviour detection in cloud computing. Int J Intell Robot Appl 7, 575–588 (2023). https://doi.org/10.1007/s41315-022-00239-x

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