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Development of Hybrid Intrusion Detection Systems for IoT Enabled Devices Utilizing Resource Constraint Learning Frameworks

P. Rachana

P. Rachana

Department of Computer Science and Engineering, Alvas Institute of Engineering and TechnologyMangalore, India
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Rachana, P.
, 
Mamatha Talakoti

Mamatha Talakoti

Department of Computer Science and Engineering, Sreenidhi Institute Of Science and TechnologyHyderabad, India
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Talakoti, Mamatha
 und 
V. Surya Narayana Reddy

V. Surya Narayana Reddy

Department of Computer Science and Engineering, Bvrit Hyderabad College of Engineering for WomenHyderabad, India
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Surya Narayana Reddy, V.
  
15. Juni 2024
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Artikel-Kategorie: Article
Online veröffentlicht: 15. Juni 2024
Seitenbereich: 60 - 76
Eingereicht: 11. Feb. 2024
Akzeptiert: 01. Mai 2021
DOI: https://doi.org/10.2478/jsiot-2024-0005
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© 2023 Rachana P et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Hybrid LSTM-GRU Intrusion Detection Framework
Hybrid LSTM-GRU Intrusion Detection Framework

Figure 2:

Long Short Term Memory Network
Long Short Term Memory Network

Figure 3:

Gated Recurrent Unit Network
Gated Recurrent Unit Network

Figure 4:

Hyperparameter tuning Grid Search Architecture
Hyperparameter tuning Grid Search Architecture

Figure 5:

Comparative Analysis of Different Deep Learning Models Based on Evaluating the Accuracy
Comparative Analysis of Different Deep Learning Models Based on Evaluating the Accuracy

Figure 6:

ROC Curve for different models in intrusion detection
ROC Curve for different models in intrusion detection

Comparitive Analysis between different models for intrusion detection

Algorithm Accuracy Precision Recall F1-Score Specificity
CNN 0.75 0.78 0.78 0.79 0.78
RNN 0.77 0.8 0.79 0.8 0.8
LSTM 0.85 0.86 0.86 0.87 0.88
GRU 0.89 0.9 0.9 0.89 0.9
Proposed Model 0.95 0.96 0.95 0.96 0.95

Hyperparameter Tuning for proposed Model

Learning Rate No. of units Dropout Rate Optimizer
0.001 16 0.2 Adam
0.01 32 0.3 SGD
0.1 64 0.5 RMSProp

Evaluation of Performance Metrics

Performance Measures Expression
Accuracy TP+TNTP+TN+FP+FN ${{TP + TN} \over {TP + TN + FP + FN}}$
Precision TNTP+FP ${{TN} \over {TP + FP}}$
Recall TPTP+FN×100 ${{{\rm{TP}}} \over {{\rm{TP}} + {\rm{FN}}}} \times 100$
F1-Score Precison ∗ RecallPrecison + Recall ${{Precison\, * \,{Recall}} \over {Precision\, + \, {Recall}}}$
Specificity TNTN+FP ${{TN} \over {TN + FP}}$
Sprache:
Englisch
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Technik, Einführungen und Gesamtdarstellungen, Technik Geschichte, Elektrotechnik, Grundlagen der Elektrotechnik, Elektronik, Informationstechnik
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