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Intrusion detection for IoT based on a hybrid shuffled shepherd optimization algorithm

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Abstract

The use of current data management technologies for the Internet of Things (IoT) is not straightforward due to interface heterogeneity, highly complex and potentially unprotected conditions, and the huge scale of the network. A vast number of devices and sensors around the world are connected to wireless networks, making the data transmitted to and from them vulnerable to attack. In addition, data sent through such devices can be unrelated, duplicated or inaccurate, making it impossible to execute the necessary tasks. Therefore, the transmitted data must be screened and chosen to fit the issue being handled in order to maintain the best possible degree of protection and thereby keep devices free from any data that could pose a threat. Reducing the size of the data often contributes to an increase in the level of communication between different networks, which in turn leads to a boost in the overall performance of the wireless network infrastructure. The process of obtaining only the relevant features from all of the data in a dataset for use in a particular task is known as feature selection (FS). In this paper, a new FS wrapper model is proposed that uses a shuffled shepherd optimization (SSO) algorithm as a search mechanism for the problem space and a K-nearest neighbor classifier to solve IoT problems with FS. Shuffled shepherd optimization was applied in a basic form as well as in two hybrid models with simulated annealing (SA) named SSO-SA1 and SSO-SA2. In order to test the performance of the three proposed models, they were applied to nine well-known IoT datasets. All the obtained results indicated that the two hybrids SSO-SA1 and SSO-SA2 enhanced the search capability of the original SSO algorithm. In comparison experiments, the SSO-SA2 outperformed three FS wrapper approaches, namely, EPC, MOPSO and MOPSO-Lévy. The SSO-SA2 surpassed the other methods in six out of the nine IoT benchmark datasets with an accuracy rate of 0.987. As for number of selected features, the SSO-SA2 surpassed the other methods in five out of the nine datasets with a selection size rate of 39.11 features. Furthermore, the wrapper-based SSO-SA2 surpassed four filter methods, namely, ReliefF, correlation, information gain and symmetrical.

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Abbreviations

ABC:

Artificial bee colony

ACO:

Ant colony optimization

BA:

Bat algorithm

B-ABC:

Binary artificial bee colony

B-DA:

Binary dragonfly algorithm

B-DE:

Binary-differential evolution

CSO:

Cat swarm optimization

CSO:

Crow search optimization

DEA:

Differential evolution algorithm

DA:

Dragonfly algorithm

GWO:

Elephant herding optimization

EPC:

Emperor penguin colony

FS:

Feature selection

FA:

Firefly algorithm

GA:

Genetic algorithm

GO:

Grasshopper optimization

GWO:

Gray wolf optimization

HS:

Harmony search

IoT:

Internet of things

IBDA:

Improved binary dragonfly algorithm

IGA:

Improved genetic algorithm

ID:

Intrusion detection

MOPSO:

Multi-objective particle swarm optimization

PSO:

Particle swarm optimization

PIO:

Pigeon-inspired optimization

RFA:

Random forest algorithm

SSO:

Shuffled shepherd optimization

SA:

Simulated annealing

SMO:

Spider monkey optimization

SVM:

Support vector machine

WOA:

Watershed optimization algorithm

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Acknowledgements

The research reported in this publication was supported by the Deanship of Scientific Research and Innovation et al.-Balqa Applied University in Jordan. Grant Number: DSR-2020#295

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Authors and Affiliations

  1. Department of Computer Science, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan

    Mohammed Alweshah, Saleh Alkhalaileh & Majdi Beseiso

  2. Gulf University for Science and Technology, Mishref, Kuwait

    Muder Almiani

  3. Data Mining and Optimization Research Group, Center for Artificial IntelligenceTechnology, Universiti Kebangsaan Malaysia, Bangi Selangor, Malaysia

    Salwani Abdullah

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  1. Mohammed Alweshah
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Correspondence to Mohammed Alweshah.

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Alweshah, M., Alkhalaileh, S., Beseiso, M. et al. Intrusion detection for IoT based on a hybrid shuffled shepherd optimization algorithm. J Supercomput 78, 12278–12309 (2022). https://doi.org/10.1007/s11227-022-04357-y

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