Open Access

ARTICLE

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

by R. Sheeba^1,*, R. Sharmila², Ahmed Alkhayyat³, Rami Q. Malik⁴

1 Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
2 Department of Computer Applications, Dhanalakshmi Srinivasan Engineering College, Perambalur, 621212, India
3 College of Technical Engineering, The Islamic University, Najaf, Iraq
4 Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

* Corresponding Author: R. Sheeba. Email:

Computer Systems Science and Engineering 2023, 46(2), 1415-1429. https://doi.org/10.32604/csse.2023.034321

Received 13 July 2022; Accepted 13 November 2022; Issue published 09 February 2023

Abstract

Lately, the Internet of Things (IoT) application requires millions of structured and unstructured data since it has numerous problems, such as data organization, production, and capturing. To address these shortcomings, big data analytics is the most superior technology that has to be adapted. Even though big data and IoT could make human life more convenient, those benefits come at the expense of security. To manage these kinds of threats, the intrusion detection system has been extensively applied to identify malicious network traffic, particularly once the preventive technique fails at the level of endpoint IoT devices. As cyberattacks targeting IoT have gradually become stealthy and more sophisticated, intrusion detection systems (IDS) must continually emerge to manage evolving security threats. This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning (IDMBOA-DL) algorithm. In the presented IDMBOA-DL model, the Hadoop MapReduce tool is exploited for managing big data. The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets. Finally, the sine cosine algorithm (SCA) with convolutional autoencoder (CAE) mechanism is utilized to recognize and classify the intrusions in the IoT network. A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm. The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

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Keywords

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