A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things
Abstract
:1. Introduction
- We propose an AI model-based DL and different machine and ensemble learning classifiers to detect cyber-attacks on the IoT with SMOTE (Synthetic Minority Over-sampling Technique) implementation to yield significant results [17];
- We improve the accuracy and confidence of cybersecurity attack detection in IoT environments compared to current works;
- We produce more accurate and reliable predictions, leading to improved IoT security by preventing unauthorized access, data breaches, and service interruptions;
- We enhance the generalization capabilities of the developed models by addressing the class imbalance issues commonly observed in IoT cybersecurity datasets through the application of SMOTE [18];
- We bring an understanding of the optimal application of DL and ensemble learning models as cybersecurity attack prediction classifiers.
2. Literature Review
3. Materials and Methods
3.1. Bot-IoT Dataset
- Benign category: normal, legitimate IoT network activity without malicious intent; DDoS TCP attacks flood a network with TCP requests, rendering it inaccessible to authorized users;
- UDP-focused DDoS attacks: these flood networks with packets, causing disruptions and service outages;
- DDoS HTTP attacks: these flood web servers with HTTP requests, causing degraded performance or service disruption;
- TCP DoS attacks: these exploit TCP stack vulnerabilities to exhaust device/network resources and render them unresponsive/unavailable;
- UDP DoS attacks: these flood the target with many packets, leading to resource exhaustion and service disruptions;
- HTTP-based DoS attacks: these overload web servers with excessive requests, causing degraded performance or unavailability;
- Keylogging: the covert monitoring and recording of keystrokes on a compromised device, used for malicious purposes to steal sensitive information;
- Capture of data: the unauthorized capture and exfiltration of information from compromised IoT networks/devices.
3.2. The Proposed Model
3.2.1. Data Pre-Processing
3.2.2. Feature Engineering Techniques
- Correlation Coefficient
- 2.
- Feature Importance using Random Forest
- 3.
- SMOTE Approach
3.3. Ensemble Learning
- Extra Trees classifier
- Histogram-based Gradient Boosting classifier
- Adaptive Boosting classifier
- LGBM classifier
- CatBoosting classifier
3.4. Evaluation Metrics
- True positive rate (TPR): ratio of observed positives to expected positives;
- False positive rate (FPR): ratio of values that are truly negative but are expected to be positive;
- False negative rate (FNR): ratio values that are in fact positive but are projected to be negative;
- True negative rate (TNR): ratio values that are negative and anticipated to become negative;
- Precision: the capacity of a system to accurately detect the existence of an attack or security breach; it illustrates the relationship between precisely predicted attacks and actual consequences:
- Recall: the system’s ability to correctly recognize a botnet attack when it occurs on a network:
- Accuracy: the system’s ability to effectively classify attack and non-attack packets; it represents the percentage of accurate predictions relative to the total number of samples:
- F1-score: average of recall and precision; it provides the percentage of normal and attacking flow samples accurately anticipated in the testing sample:
- Time complexity: how quickly or slowly an algorithm performs in the same relation to the amount of data.
4. Results
4.1. Experimental Settings
4.2. Experimental Results
4.2.1. Experiments without Using the SMOTE Algorithm
4.2.2. Experiments Using the SMOTE Algorithm
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Target | Count |
---|---|---|
BENIGN | Benign | 9543 |
DDoS TCP | Attack | 19,547,603 |
DDoS UDP | Attack | 18,965,106 |
DDoS HTTP | Attack | 19,771 |
DoS TCP | Attack | 12,315,997 |
DoS UDP | Attack | 20,659,491 |
DoS HTTP | Attack | 29,706 |
Keylogging | Keylogging | 1469 |
Data theft | Data theft | 118 |
Total | - | 73,370,443 |
Metric | Accuracy | Precision | Recall | F1-Score | CPU Time | Model Size (MB) |
---|---|---|---|---|---|---|
Random Forest | 0.9518 | 0.9538 | 0.9284 | 0.9403 | 21.6 s | 23.6 |
Extra Trees | 0.9674 | 0.9652 | 0.9517 | 0.9582 | 47.6 s | 598.7 |
KNN | 0.9083 | 0.9036 | 0.8869 | 0.8947 | 3.29 s | 13.6 |
SVM | 0.6121 | 0.6280 | 0.3598 | 0.3695 | 21 min 50 s | 12.2 |
HistGBoost | 0.9560 | 0.7488 | 0.7332 | 0.7321 | 13.4 s | 1.2 |
AdaBoost | 0.1211 | 0.4552 | 0.3482 | 0.0826 | 1 min 19 s | 0.31 |
LGBM | 0.9323 | 0.4665 | 0.4739 | 0.4690 | 36.1 s | 1.8 |
CatBoost | 0.9819 | 0.9686 | 0.9608 | 0.9646 | 2 min 55 s | 3.5 |
XGBoost | 0.9852 | 0.9806 | 0.9654 | 0.9727 | 2 min 43 s | 1.1 |
MLP | 0.7539 | 0.3031 | 0.2942 | 0.2850 | 31.5 s | 0.005 |
ANN | 0.8308 | 0.3308 | 0.4789 | 0.3701 | 13 min 48 s | 0.027 |
LSTM | 0.7701 | 0.4887 | 0.3476 | 0.3682 | 10 min 10 s | 7.7 |
GRU | 0.8536 | 0.6058 | 0.4517 | 0.4902 | 11 min 1 s | 7.7 |
RNN | 0.8682 | 0.9189 | 0.7631 | 0.8013 | 10 min 50 s | 1.6 |
Bagging | 0.9398 | 0.9324 | 0.9160 | 0.9238 | 2 min 54 s | 240.5 |
Metric | Accuracy | Precision | Recall | F1-Score | CPU Time | Model (MB) |
---|---|---|---|---|---|---|
CatBoost | 0.97661 | 0.91249 | 0.9815 | 0.94369 | 7 min 43 s | 3.48 |
XGBoost | 0.97986 | 0.94868 | 0.98084 | 0.96383 | 7 min 53 s | 1.22 |
MLP | 0.53336 | 0.31119 | 0.63571 | 0.32423 | 4 min 47 s | 0.02 |
ANN | 0.76594 | 0.61794 | 0.89682 | 0.63602 | 31 min 41 s | 0.03 |
LSTM | 0.83418 | 0.75511 | 0.92699 | 0.76773 | 30 min 6 s | 7.69 |
GRU | 0.87806 | 0.78463 | 0.93476 | 0.83175 | 29 min 50 s | 7.69 |
RNN | 0.87147 | 0.77572 | 0.94066 | 0.8257 | 27 min 3 s | 1.62 |
Bagging | 0.94099 | 0.91357 | 0.93127 | 0.92205 | 9 min 31 s | 350.73 |
Random Forest | 0.9425 | 0.90961 | 0.9635 | 0.9304 | 1 min 7 s | 29.60 |
Extra Trees | 0.90922 | 0.88756 | 0.8952 | 0.8906 | 3.43 s | 35.19 |
KNN | 0.90922 | 0.88756 | 0.8952 | 0.8906 | 3.43 s | 35.19 |
SVM | 0.59398 | 0.4853 | 0.63258 | 0.48259 | 1 h 18 min 19 s | 25.34 |
HistGboost | 0.97437 | 0.97758 | 0.97437 | 0.97511 | 47.6 s | 1.90 |
AdaBoost | 0.43068 | 0.32098 | 0.34041 | 0.25093 | 3 min 55 s | 0.31 |
LGBM | 0.98242 | 0.96029 | 0.98055 | 0.96986 | 4 min 5 s | 11.05 |
Ref. | Data Used | Methodology Used | Accuracy (%) |
---|---|---|---|
Mendonça et al. [23] | DS2OS, CICIDS2017 | Deep learning | 98 |
Popoola et al. [26] | BoT-IoT | LAE for dimensionality reduction and BLSTM classifier | 91.89 |
Alharbi et al. [27] | N-BaIoT | A Local–Global best Bat Algorithm for Neural Networks | 90 |
Saharkhizan et al. [30] | Modbus/TCP network traffic | LSTM and Ensemble learning | 98.99 |
Pokhrel et al. [31] | BoT-IoT | Deep learning | 87.4 |
Proposed | BoT-IoT | CatBoosting XGBoosting | 98.19 |
98.52 |
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Alkhudaydi, O.A.; Krichen, M.; Alghamdi, A.D. A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things. Information 2023, 14, 550. https://doi.org/10.3390/info14100550
Alkhudaydi OA, Krichen M, Alghamdi AD. A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things. Information. 2023; 14(10):550. https://doi.org/10.3390/info14100550
Chicago/Turabian StyleAlkhudaydi, Omar Azib, Moez Krichen, and Ans D. Alghamdi. 2023. "A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things" Information 14, no. 10: 550. https://doi.org/10.3390/info14100550