Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
Abstract
:1. Introduction
- Using the combination of deep learning and Aquila optimizer (AQU) to enhance IoT security.
- A feature extractor technique based on CNN is applied to extract relevant features from the datasets,
- A binary version of the Aquila optimizer is adopted as an FS technique that is used to select optimal features and enhance the classification accuracy.
- Extensive evaluation is carried out with four public datasets and extensive comparisons to other methods to confirm the quality of the developed approach.
2. Related Works
3. Background
Aquila Optimizer (AQU)
4. Proposed Model
4.1. Representation of Collect IoT Dataset
4.2. Convolutional Neural Network for Feature Extraction
4.3. Feature Selection
4.3.1. Generation Initial Population
4.3.2. Updating Population
4.3.3. Terminal Criteria
4.3.4. Validation Stage
Algorithm 1 Proposed FS For IoT security. |
|
5. Experiment Results and Discussion
5.1. Performance Measures
- Average accuracy : The accuracy metric represents the rate of correct detection of the intrusion, and it is formulated as:
- Average Recall : or true positive rate (TPR), represents the percentage of predicting positive intrusion. It can be computed as:
- Average Precision : this illustrates the percentage of true positive cases among all the the positive cases. The can be calculated as:
- Performance Improvement Rate (PIR): This measure is applied to estimate the improvement rates obtained by the proposed technique. it can be computed as:
5.2. Experimental Setup
5.3. Dataset Description
- KDDCup-99 and NSL-KDD: The two datasets are described in Figure 4 with their detailed statistics. The first dataset is KDDCup-99, collected from the DARPA intrusion detection challenge (1998), incorporating 100’s users after monitoring the network traffic on 1000’s machines using UNIX operating system. The challenge period lasts for ten weeks by the MIT Lincon laboratory to store the collected traffic data in TCP dump format. Our experiments used 10% of the collected traffic data to build the KDDCup-99 dataset, which contains five attack types and 41 features. The KDDCup-99 dataset features are classified into three categories, including basic, content, and time-based traffic features. The second dataset is NSL-KDD, a derived copy from the full KDDCup-99 dataset after performing deduplication of the duplicated traffic records.
- BoT-IoT: the Bot-IoT dataset [60] was collected in The center of UNSW Canberra Cyber using smart home appliances in a laboratory environment (the Cyber Range Lab). The dataset contains Industrial IoT (IIoT) traffic samples collected for IIoT experiments. The smart home appliances include weather monitoring systems, thermostats, kitchen appliances, and freezers and motion-controlled lights to record the traffic data. In our experiments, we used the 5% of the full Bot-IoT dataset, which consists of 3.6 million records, where the full dataset contains over 72 million records. The 5% of the entire dataset contains the best ten features extracted from the raw data and categorized into five main classes as described in Figure 5.
- CICIDS-2017: The CICIDS-2017 [61] dataset is a collection of network traffic samples collected in CIC (The Canadian Institute for Cybersecurity at the University of New Brunswick.) for the intrusion detection task. The dataset consists of more than 1.5M PCAPs data simulating traffic data transferred in real-world using the CICFlowMeter software after analyzing 25 user behaviors covering various network protocols such as HTTP and SSH protocols. The collected data were categorized into eight main attack classes as described in Figure 6. Our experiments used the following collected CSV files: Tuesday-working hours, Friday-WorkingHours-Afternoon-PortScan, Friday-WorkingHours-Afternoon-DDos, and Thursday-WorkingHours-Morning-WebAttacks.
5.4. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Predicted Label | ||
---|---|---|
Actual Label | Positive | Negative |
Postive | TP | FN |
Negative | FP | TN |
Training | Testing | ||||||||
---|---|---|---|---|---|---|---|---|---|
F1 | F1 | ||||||||
KDD99 | PSO | 90.447 | 93.458 | 90.358 | 90.358 | 82.783 | 85.793 | 84.640 | 83.109 |
WOA | 92.275 | 93.126 | 92.414 | 97.304 | 84.375 | 85.225 | 82.501 | 87.351 | |
BAT | 98.007 | 98.247 | 94.847 | 97.337 | 90.347 | 90.587 | 89.134 | 90.093 | |
TSO | 95.439 | 94.919 | 91.027 | 97.437 | 87.536 | 87.016 | 80.791 | 87.479 | |
GWO | 95.513 | 92.383 | 94.062 | 98.482 | 87.618 | 84.488 | 84.131 | 88.533 | |
FFA | 91.988 | 93.368 | 97.328 | 91.538 | 84.318 | 85.698 | 91.609 | 84.285 | |
MVO | 99.515 | 92.835 | 96.483 | 94.433 | 91.615 | 84.935 | 86.649 | 84.480 | |
MFO | 96.073 | 97.123 | 97.631 | 98.371 | 88.175 | 89.225 | 87.763 | 88.420 | |
AQU | 99.920 | 99.917 | 97.542 | 99.920 | 99.919 | 92.042 | 89.824 | 89.987 | |
BIoT | PSO | 99.483 | 99.483 | 99.483 | 99.483 | 98.942 | 98.972 | 98.941 | 98.940 |
WOA | 99.472 | 99.472 | 99.472 | 99.472 | 98.956 | 98.964 | 98.957 | 99.005 | |
BAT | 99.475 | 99.475 | 99.475 | 99.474 | 99.019 | 99.021 | 98.987 | 99.012 | |
TSO | 99.460 | 99.460 | 99.459 | 99.459 | 98.986 | 98.981 | 98.941 | 99.005 | |
GWO | 99.477 | 99.477 | 99.476 | 99.476 | 98.990 | 98.959 | 98.975 | 99.019 | |
FFA | 99.479 | 99.479 | 99.478 | 99.478 | 98.954 | 98.968 | 99.007 | 98.949 | |
MVO | 99.468 | 99.468 | 99.468 | 99.468 | 99.031 | 98.964 | 99.000 | 98.980 | |
MFO | 99.480 | 99.480 | 99.480 | 99.480 | 98.998 | 99.009 | 99.013 | 99.020 | |
AQU | 98.925 | 98.925 | 98.904 | 98.925 | 98.926 | 98.904 | 98.905 | 98.904 | |
NSL-KDD | PSO | 90.118 | 93.128 | 90.020 | 90.019 | 66.092 | 69.102 | 68.913 | 61.940 |
WOA | 91.947 | 92.797 | 92.080 | 96.968 | 67.951 | 68.801 | 71.131 | 68.907 | |
BAT | 97.669 | 97.909 | 94.501 | 96.989 | 73.671 | 73.911 | 73.501 | 68.905 | |
TSO | 95.078 | 94.558 | 90.657 | 97.067 | 71.330 | 70.810 | 71.298 | 69.697 | |
GWO | 95.182 | 92.052 | 93.724 | 98.143 | 71.066 | 67.936 | 72.151 | 69.948 | |
FFA | 91.660 | 93.040 | 96.991 | 91.201 | 67.437 | 68.817 | 75.873 | 62.944 | |
MVO | 99.182 | 92.502 | 96.145 | 94.093 | 75.224 | 68.544 | 75.200 | 66.098 | |
MFO | 95.745 | 96.795 | 97.297 | 98.035 | 71.626 | 72.676 | 76.122 | 69.844 | |
AQU | 99.344 | 99.344 | 99.298 | 99.315 | 76.002 | 76.002 | 81.719 | 71.602 | |
CIC2017 | PSO | 99.650 | 99.370 | 99.590 | 99.750 | 99.380 | 99.100 | 99.320 | 99.480 |
WOA | 99.690 | 99.690 | 99.490 | 99.450 | 99.430 | 99.430 | 99.240 | 99.190 | |
BAT | 99.490 | 99.640 | 99.630 | 99.440 | 99.230 | 99.380 | 99.360 | 99.180 | |
TSO | 99.680 | 99.710 | 99.750 | 99.680 | 99.420 | 99.450 | 99.480 | 99.420 | |
GWO | 99.370 | 99.560 | 99.430 | 99.380 | 99.110 | 99.300 | 99.180 | 99.120 | |
FFA | 99.450 | 99.740 | 99.480 | 99.600 | 99.200 | 99.490 | 99.220 | 99.350 | |
MVO | 99.530 | 99.370 | 99.390 | 99.410 | 99.270 | 99.110 | 99.120 | 99.150 | |
MFO | 99.360 | 99.430 | 99.370 | 99.480 | 99.100 | 99.170 | 99.120 | 99.220 | |
AQU | 99.911 | 99.909 | 99.889 | 99.910 | 99.911 | 99.910 | 99.910 | 99.888 |
Training | Testing | ||||||||
---|---|---|---|---|---|---|---|---|---|
F1 | F1 | ||||||||
KDD99 | PSO | 90.449 | 93.459 | 90.359 | 90.359 | 82.775 | 85.785 | 84.638 | 92.702 |
WOA | 92.278 | 93.128 | 92.418 | 97.308 | 84.608 | 85.458 | 86.699 | 92.705 | |
BAT | 94.992 | 98.662 | 92.922 | 91.782 | 87.384 | 91.055 | 87.280 | 92.751 | |
TSO | 95.298 | 94.592 | 90.825 | 97.332 | 87.593 | 87.090 | 85.280 | 92.541 | |
GWO | 95.518 | 92.388 | 94.068 | 98.488 | 87.860 | 84.730 | 88.357 | 92.716 | |
FFA | 91.987 | 93.367 | 97.327 | 91.537 | 84.327 | 85.707 | 91.614 | 92.713 | |
MVO | 99.519 | 92.839 | 96.489 | 94.439 | 91.844 | 85.164 | 90.765 | 92.701 | |
MFO | 96.079 | 97.129 | 97.639 | 98.379 | 88.413 | 89.463 | 91.922 | 92.710 | |
AQU | 99.922 | 99.922 | 92.256 | 99.922 | 99.922 | 92.256 | 94.283 | 92.683 | |
BIoT | PSO | 99.899 | 99.929 | 99.898 | 99.898 | 99.898 | 99.928 | 99.896 | 99.896 |
WOA | 99.918 | 99.926 | 99.919 | 99.967 | 99.916 | 99.924 | 99.916 | 99.965 | |
BAT | 99.975 | 99.977 | 99.943 | 99.968 | 99.973 | 99.975 | 99.941 | 99.966 | |
TSO | 99.949 | 99.944 | 99.905 | 99.969 | 99.947 | 99.942 | 99.903 | 99.967 | |
GWO | 99.950 | 99.919 | 99.935 | 99.979 | 99.948 | 99.917 | 99.933 | 99.977 | |
FFA | 99.915 | 99.928 | 99.968 | 99.910 | 99.913 | 99.927 | 99.966 | 99.908 | |
MVO | 99.990 | 99.923 | 99.959 | 99.939 | 99.989 | 99.922 | 99.958 | 99.937 | |
MFO | 99.956 | 99.966 | 99.971 | 99.978 | 99.954 | 99.964 | 99.969 | 99.976 | |
AQU | 99.995 | 99.994 | 99.993 | 99.995 | 99.994 | 99.993 | 99.992 | 99.992 | |
NSL-KDD | PSO | 90.133 | 93.143 | 90.043 | 90.043 | 67.575 | 70.585 | 73.882 | 67.163 |
WOA | 91.959 | 92.809 | 92.099 | 96.989 | 69.409 | 70.259 | 75.972 | 74.115 | |
BAT | 97.693 | 97.933 | 94.533 | 97.023 | 75.192 | 75.432 | 78.473 | 74.197 | |
TSO | 95.091 | 94.571 | 90.681 | 97.091 | 72.078 | 71.558 | 73.656 | 73.786 | |
GWO | 95.202 | 92.072 | 93.753 | 98.172 | 72.944 | 69.814 | 77.801 | 75.609 | |
FFA | 91.673 | 93.053 | 97.013 | 91.223 | 69.218 | 70.598 | 80.944 | 68.451 | |
MVO | 99.197 | 92.517 | 96.167 | 94.117 | 76.466 | 69.786 | 79.835 | 71.059 | |
MFO | 95.760 | 96.810 | 97.320 | 98.060 | 73.187 | 74.237 | 81.176 | 75.162 | |
AQU | 99.348 | 99.348 | 99.350 | 99.348 | 77.382 | 77.382 | 83.692 | 77.077 | |
CIC2017 | PSO | 99.687 | 99.407 | 99.627 | 99.387 | 99.687 | 99.407 | 99.627 | 99.787 |
WOA | 99.730 | 99.531 | 99.537 | 99.470 | 99.737 | 99.737 | 99.537 | 99.497 | |
BAT | 99.537 | 99.647 | 99.667 | 99.472 | 99.537 | 99.687 | 99.667 | 99.487 | |
TSO | 99.724 | 99.654 | 99.744 | 99.436 | 99.725 | 99.755 | 99.785 | 99.725 | |
GWO | 99.417 | 99.607 | 99.477 | 99.427 | 99.417 | 99.607 | 99.477 | 99.427 | |
FFA | 99.497 | 99.601 | 99.517 | 99.470 | 99.497 | 99.787 | 99.517 | 99.647 | |
MVO | 99.577 | 99.417 | 99.427 | 99.457 | 99.577 | 99.417 | 99.427 | 99.457 | |
MFO | 99.407 | 99.477 | 99.417 | 99.427 | 99.407 | 99.477 | 99.417 | 99.527 | |
AQU | 99.996 | 99.996 | 99.996 | 99.996 | 99.997 | 99.997 | 99.997 | 99.997 |
PSO | MVO | GWO | MFO | WOA | FFA | BAT | AQU | TSO | |
---|---|---|---|---|---|---|---|---|---|
Binary classification | |||||||||
Accuracy | 1 | 8 | 5.33 | 6.33 | 3 | 2 | 6 | 9 | 4.33 |
Recall | 4.66 | 1.66 | 1.33 | 7 | 3 | 4.33 | 8 | 9 | 6 |
Precision | 1.33 | 6 | 4.33 | 8 | 3 | 7 | 4.66 | 9 | 1.66 |
F1-Measure | 1.66 | 2.66 | 7.66 | 6.33 | 4.33 | 3.33 | 6.33 | 9 | 3.66 |
Multi classification | |||||||||
Accuracy | 1 | 8 | 4.66 | 6 | 3 | 2 | 7 | 9 | 4.33 |
Recall | 5 | 2.16 | 1 | 7 | 2.83 | 4 | 8 | 9 | 6 |
Precision | 2.16 | 5.66 | 3.66 | 7.33 | 2.33 | 7.66 | 5.66 | 8.66 | 1.83 |
F1-Measure | 1 | 3 | 7.33 | 7 | 4.33 | 2 | 6.5 | 8.66 | 5.16 |
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Fatani, A.; Dahou, A.; Al-qaness, M.A.A.; Lu, S.; Abd Elaziz, M. Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System. Sensors 2022, 22, 140. https://doi.org/10.3390/s22010140
Fatani A, Dahou A, Al-qaness MAA, Lu S, Abd Elaziz M. Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System. Sensors. 2022; 22(1):140. https://doi.org/10.3390/s22010140
Chicago/Turabian StyleFatani, Abdulaziz, Abdelghani Dahou, Mohammed A. A. Al-qaness, Songfeng Lu, and Mohamed Abd Elaziz. 2022. "Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System" Sensors 22, no. 1: 140. https://doi.org/10.3390/s22010140