Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment
Abstract
:1. Introduction
2. Literature Review
3. The Proposed Model
3.1. BHPO-Based Feature Selection
3.2. Phishing Attack Detection
3.3. VFFO-Based Parameter Tuning
4. Experimental Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference | Year | Method | Performance | Dataset |
---|---|---|---|---|
Mughaid et al. [16] | 2022 | ML models such as SVM, DT, LR, NN, and decision forest | Accuracy, Precision, Recall, F-Score | Phishing email collection dataset |
Abdulrahman et al. [17] | 2019 | Random Forest and CAER feature selection | TPR, FPR, Accuracy, Precision, Recall, F-Measure | UCI phishing website dataset |
Jain et al. [18] | 2018 | PHISH-SAFE, an ML-based classifier | Accuracy | PhishTank URL dataset |
Huang et al. [19] | 2019 | Capsule-based neural network | TPR, FPR, Accuracy, Precision, Recall, F-Measure | PhishTank and Openphish data |
Zabihimayvan and Doran [20] | 2019 | Fuzzy Rough Set | F-measure | UCI Phishing and Mendeley dataset |
Jain and Gupta [21] | 2018 | Link and visual similarity relation | TPR, FPR | - |
Azeez et al. [22] | 2021 | Whitelist approach | TPR, TNR, FPR, FNR, Accuracy | PhishTank and Alexa data |
Alotaibi et al. [23] | 2020 | CNN | Accuracy | PhishingCorpus and SpamAssassin |
Alrowais et al. [24] | 2023 | Mayfly optimization with RELM | Accuracy, Precision, Recall, F-score | N-BaIoT dataset |
Ruiz-Villafranca et al. [25] | 2023 | MECInOT | Accuracy, Precision, Recall, F-score | Mendeley dataset |
Rookard and Khojandi [26] | 2023 | Deep Q-network | Accuracy | - |
Mengash et al. [27] | 2023 | SRO-MLCOSN model | Accuracy, Precision, Recall, F-score | - |
Class | No. of Samples |
---|---|
Normal | 1000 |
Fuzzers | 1000 |
DoS | 1000 |
Analysis | 1000 |
Exploits | 1000 |
Generic | 1000 |
Total Number of Samples | 6000 |
Class | |||||
---|---|---|---|---|---|
Training Phase (70%) | |||||
Normal | 99.38 | 98.30 | 98.02 | 98.16 | 98.84 |
Fuzzers | 99.33 | 97.69 | 98.26 | 97.98 | 98.90 |
DoS | 99.02 | 97.00 | 97.40 | 97.20 | 98.38 |
Analysis | 99.19 | 98.80 | 96.20 | 97.48 | 97.99 |
Exploits | 98.81 | 95.20 | 97.68 | 96.42 | 98.36 |
Generic | 98.93 | 97.11 | 96.42 | 96.76 | 97.92 |
Average | 99.11 | 97.35 | 97.33 | 97.33 | 98.40 |
Testing Phase (30%) | |||||
Normal | 99.61 | 98.31 | 99.32 | 98.81 | 99.49 |
Fuzzers | 99.28 | 97.75 | 98.06 | 97.91 | 98.80 |
DoS | 99.06 | 97.37 | 96.28 | 96.82 | 97.91 |
Analysis | 98.78 | 97.12 | 95.89 | 96.50 | 97.64 |
Exploits | 98.83 | 95.58 | 97.74 | 96.65 | 98.40 |
Generic | 99.00 | 97.65 | 96.36 | 97.00 | 97.95 |
Average | 99.09 | 97.30 | 97.28 | 97.28 | 98.36 |
Technology | ||||
---|---|---|---|---|
GA-LR | 81.42 | 83.03 | 85.93 | 85.95 |
TS-RF | 83.12 | 83.28 | 83.63 | 85.06 |
LSO-FNN | 95.42 | 94.03 | 94 | 95.98 |
SCM3-RF | 95.87 | 94.08 | 94.22 | 93.89 |
RHF-ANN | 97.60 | 95.62 | 95.98 | 96.71 |
EAFS-RF | 98.36 | 94.08 | 95.41 | 97.01 |
BHPO-MLPAD | 99.11 | 97.35 | 97.33 | 97.33 |
Technology | Computational Time (s) |
---|---|
GA-LR | 0.30 |
TS-RF | 0.28 |
LSO-FNN | 0.25 |
SCM3-RF | 0.30 |
RHF-ANN | 0.27 |
EAFS-RF | 0.28 |
BHPO-MLPAD | 0.17 |
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Khadidos, A.O.; AlKubaisy, Z.M.; Khadidos, A.O.; Alyoubi, K.H.; Alshareef, A.M.; Ragab, M. Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment. Sensors 2023, 23, 7207. https://doi.org/10.3390/s23167207
Khadidos AO, AlKubaisy ZM, Khadidos AO, Alyoubi KH, Alshareef AM, Ragab M. Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment. Sensors. 2023; 23(16):7207. https://doi.org/10.3390/s23167207
Chicago/Turabian StyleKhadidos, Adil O., Zenah Mahmoud AlKubaisy, Alaa O. Khadidos, Khaled H. Alyoubi, Abdulrhman M. Alshareef, and Mahmoud Ragab. 2023. "Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment" Sensors 23, no. 16: 7207. https://doi.org/10.3390/s23167207
APA StyleKhadidos, A. O., AlKubaisy, Z. M., Khadidos, A. O., Alyoubi, K. H., Alshareef, A. M., & Ragab, M. (2023). Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment. Sensors, 23(16), 7207. https://doi.org/10.3390/s23167207