ExpSSOA-Deep maxout: Exponential Shuffled shepherd optimization based Deep maxout network for intrusion detection using big data in cloud computing framework

Bishwajeet Kumar Pandey; Veeramanickam M．R．M．; Shabeer Ahmad; Ciro Rodriguez; Doris Esenarro

doi:10.1016/j.cose.2022.102975

ExpSSOA-Deep maxout: Exponential Shuffled shepherd optimization based Deep maxout network for intrusion detection using big data in cloud computing framework

Bishwajeet Kumar Pandey, Veeramanickam M．R．M．, Shabeer Ahmad, Ciro Rodriguez, Doris Esenarro

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

The evolution of the Internet produced a large quantity of information. This makes the internet world more complex and affected by powerful attacks. In modern networks, the Intrusion Detection System (IDS) acts as a significant function for network security. The IDS can be either anomaly or signature-based behavior detection. Recently, several detection approaches have been proposed by researchers to find network intrusions. In this paper, a deep learning approach to intrusion detection using the Exponential Shuffled Shepherded Optimization Algorithm (ExpSSOA) is proposed. The proposed ExpSSOA combines the exponential weighted moving average (EWMA) and the shuffled shepherded optimization algorithm (SSOA). The proposed ExpSSOA-based Deep Maxout network for intrusion detection is examined using the MQTT-IOT-IDS2020 dataset and the Apache Web Server dataset. According to the experimental results using the Apache webserver dataset, the suggested ExpSSOA-Deep maxout network offers a better result with an accuracy of 0.883, an F-measure of 0.8768, a precision of 0.8746, and a recall of 0.8564.

Original language	English
Article number	102975
Journal	Computers and Security
Volume	124
DOIs	https://doi.org/10.1016/j.cose.2022.102975
State	Published - Jan 2023

Bibliographical note

Funding Information:
Dr. M.R.M. Veeramanickam is currently working as an Associate Professor in Dept. of Information Technology, Shri Vishnu Engineering College for Women(A), West Godavari, Adhara Pradesh. He completed his Ph.D in Dept. of Computer Science and Engineering at Karpagam Academy of Higher Education, Karpagam University, andCoimbatore, Tamil Nadu, October 2019. He received his B.Tech. degree in Information Technology from Lord Venkateshwara Engg. College, Anna University, Chennai, India, in 2006, and M.Tech.degree in Information Technology from Sathyabama University, Chennai, India, in2011.Completed Funding Research Project entitled as “Classroom Note's sharing using Smart E-learning& Internet of Things” under SPPU BCUD Research Grant Scheme AY: 2016-18. His main research work focuses on E-learning, Social Network, Internet of Things and Artificial Neural Network.With 14 years of teaching experience from various reputed engineering college, 14 international and national journal publication, 3 Book chapters are in E-learning and IoT platform, Gamification platform. His Ph.D research work was main focused on Dragonfly Swarm optimization for students marks prediction model in e-learning design aspects.

Publisher Copyright:
© 2022

Keywords

Deep Maxout network
Exponential weighted moving average
Information gain
Intrusion detection
Shuffled shepherded optimization

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abstract = "The evolution of the Internet produced a large quantity of information. This makes the internet world more complex and affected by powerful attacks. In modern networks, the Intrusion Detection System (IDS) acts as a significant function for network security. The IDS can be either anomaly or signature-based behavior detection. Recently, several detection approaches have been proposed by researchers to find network intrusions. In this paper, a deep learning approach to intrusion detection using the Exponential Shuffled Shepherded Optimization Algorithm (ExpSSOA) is proposed. The proposed ExpSSOA combines the exponential weighted moving average (EWMA) and the shuffled shepherded optimization algorithm (SSOA). The proposed ExpSSOA-based Deep Maxout network for intrusion detection is examined using the MQTT-IOT-IDS2020 dataset and the Apache Web Server dataset. According to the experimental results using the Apache webserver dataset, the suggested ExpSSOA-Deep maxout network offers a better result with an accuracy of 0.883, an F-measure of 0.8768, a precision of 0.8746, and a recall of 0.8564.",

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ExpSSOA-Deep maxout: Exponential Shuffled shepherd optimization based Deep maxout network for intrusion detection using big data in cloud computing framework

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