Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City
Abstract
:1. Introduction
- A proposed model (IADM) produces model verification to detect and predict attacks using multi-class classification with high performance rates. Consider an intrusion detection model that is used to identify online threats. When a zero-day assault occurs on the system, the IDS repeatedly transmits the session records to a cybersecurity administrator to investigate the behaviors of users. The cybersecurity administrator marks the records as malicious or normal activities. This leads to introducing a false positive rate. Additionally, when the limits between the observation of normal and abnormal behaviors are not clear, normal activity changes quickly, which is difficult to learn offline.
- IADAM produces the detection model, which is used to prove this ability through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The proposed model has the ability to autoconfigure and render any changed behaviors associated with the smart city environment as outdated. Autoconfiguration reduces false decisions in the system as it makes the system aware of zero-day attacks. Further, the model is adapted to the changed strategies based on a low number of instances. The reinforce learning method is used in the model to enable it to adapt to new and changed instances. One-shot learning is integrated into the model to create a comprehensive approach to a real-world system for categorizing major events from a limited number of training instances using computational power with constrained resources. Therefore, IADAM can use one-shot learning to achieve auto-adaptation rapidly using a lower number of instances.
2. Literature Review
3. Methodology
3.1. System Design
3.2. First Phase: An IADM Design Model Integrated within the IoT-Based Smart City
3.2.1. Dataset Collection and Pre-Processing Module
3.2.2. Intelligent Automation Detection Module
3.2.3. Analysis Module
3.2.4. Detection Rules and Action Module
3.2.5. Database Module
3.3. The Second Phase: Dynamic Adaptation of the Attack Detection Model Based on the Reinforcement of One-Shot Learning
4. Evaluation and Experimental Results
4.1. Datasets
4.2. Evaluation Criteria
4.3. Experimental Results of Dataset Pre-Processing
4.3.1. Experimental Results of the First Phase
4.3.2. Experimental Results of the Second Phase
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Proposed Model | Methodology | Multiple Classification (MC)/Binary Classification (BC) | Auto-Adaptively Based on a Few Numbers of Instances | IoT Scalability Support | Performance Metrics |
---|---|---|---|---|---|
[1] M. M. Rashid et al., 2020 | Machine learning algorithms | MC | × | × | AC, precision, recall, and F1-score: approximately 95%, 94%, and 94%, respectively. |
[4] C. Liang et al., 2020 | Multi-module system and blockchain using reinforcement algorithms | BC | × | × | AC, precision, recall, and F-scores of 83.9%, 85.53%, 84.14%, and 83.94%, respectively. |
[7] I. Alrashdi et al., 2019. AD-IoT | Machine learning algorithm | BC | × | × | AC, false positive rate, and DR: 99.34%, 2%, and 82%, respectively. |
[9] E. Anthi et al., 2019 | Machine learning algorithms | BC | × | × | Precision, F-measure, and recall: 96.2%, 90.0%, and 98.0%, respectively. |
[10] Q. A. Al-Haija and S. Zein-Sabatto, 2020. IoT-IDCS-CNN | Deep learning | BC and MC | × | × | AC of the binary and multi-class classifications of 99.3% and 98.2%, respectively. |
IADM | Machine learning techniques and deep one-shot learning | MC | Support auto-adaptivity by applying one-shot learning | Support the scalability manner of IoT | AC, error, precision, recall, F-measure: 98.8%. 2.4%, 97%, and 97%, respectively. |
Attributes | Correlation | Attributes | Correlation |
---|---|---|---|
ct_dst_sport_ltm | 80.35% | is_sm_ips_ports | 59.04% |
ct_src_dport_ltm | 71.01% | ct_ftp_cmd | 57.6% |
ct_dst_src_ltm | 69.95% | Service | 56.3% |
ct_srv_dst | 67.34% | response_body_len | 55.02% |
ct_srv_src | 66.83% | Sbytes | 54.9% |
ct_dst_ltm | 64.90% | Rate | 52.7% |
Id | 63.7% | Sttl | 51.7% |
is_ftp_login | 60.9% |
The Performance Metrics | RFT | K-NN | J48 | AdaBoost | Bagging |
---|---|---|---|---|---|
AC | 97.99% | 97.99% | 85.5% | 97.99% | 91.4% |
Error | 2.1% | 2.1% | 14.5% | 2.1% | 8.6% |
Precision | 98% | 98% | 80.9% | 98% | 91.6% |
Recall | 98% | 98% | 82.5% | 98% | 91.4% |
F-measure | 97.5% | 97.5% | 79.5% | 97.5% | 91.1% |
TP Rate | FP Rate | Precision | Recall | F-Measure | Class |
---|---|---|---|---|---|
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Analysis |
97.5% | 2.5% | 97.5% | 97.5% | 97.5% | Backdoor |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | DoS |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Exploits |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Fuzzers |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Reconnaissance |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Shellcode |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Worms |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Normal |
TP | FP | Precision | Recall | F-Measure | Class |
---|---|---|---|---|---|
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Analysis |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Backdoor |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | DoS |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Exploits |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Fuzzers |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Reconnaissance |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Shellcode |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Worms |
97.9% | 2.1% | 97.9% | 97.9% | 98.9% | Normal |
TP | FP | Precision | Recall | F-Measure | Class |
---|---|---|---|---|---|
23.4% | 14.7% | 90.2% | 23.4% | 37.2% | Analysis |
48.8% | 11.7% | 72.1% | 48.8% | 58.2% | Backdoor |
79% | 1.4% | 76.2% | 70% | 14.3% | DoS |
97.4% | 0.1% | 67.4% | 97.4% | 79.7% | Exploits |
89.9% | 0.7% | 96.3% | 89.9% | 93.0% | Fuzzers |
77.5% | 0.3% | 95.2% | 77.5% | 85.4% | Reconnaissance |
80.2% | 0.2% | 82.6% | 80.2% | 81.4% | Shellcode |
58.1% | 9.1% | 58.1% | 69.4% | 70.8% | Worms |
99.7% | 0.2% | 99.6% | 99.7% | 99.6% | Normal |
TP | FP | Precision | Recall | F-Measure | Class |
---|---|---|---|---|---|
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Analysis |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Backdoor |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | DoS |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Exploits |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Fuzzers |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Reconnaissance |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Shellcode |
97.9% | 2.1% | 97.9% | 97.9% | 97.9% | Worms |
97.9% | 2.1% | 97.9% | 98.9% | 97.9% | Normal |
TP | FP | Precision | Recall | F-Measure | Class |
---|---|---|---|---|---|
49.8% | 13.1% | 90.2% | 49.8% | 64.4% | Analysis |
67.9% | 7.82% | 84.0% | 67.9% | 75.1% | Backdoor |
58.4% | 11.7% | 58.4% | 66.5% | 64.4% | DoS |
95.7% | 0.8% | 81.8% | 95.7% | 88.2% | Exploits |
93.5% | 0.5% | 97.0% | 93.5% | 95.3% | Fuzzers |
86.0% | 0.3% | 96.1% | 86.0% | 90.7% | Reconnaissance |
88.6% | 0.1% | 90.3% | 88.6% | 89.4% | Shellcode |
60.5% | 9.7% | 92.9% | 60.5% | 73.2% | Worms |
99.8% | 0.1% | 99.9% | 99.8% | 99.8% | Normal |
Machine Learning Algorithms | RFT | K-NN | J48 | AdaBoost | Bagging |
---|---|---|---|---|---|
Time | 1.6 msec. | 5.0 msec. | 6 msec. | 14 msec | 37 msec |
Class | Algorithms | ||||
---|---|---|---|---|---|
RFT | K-NN | J48 | AdaBoost | Bagging | |
Analysis | 98.9% | 98.9 | 36.4% | 98.9 | 83.2% |
Backdoor | 98.9% | 98.9 | 47.8% | 98.9 | 85.4% |
DoS | 98.9% | 98.9 | 37.5% | 98.9 | 81.6% |
Exploits | 98.9% | 98.9 | 83.4% | 98.9 | 96.5% |
Fuzzers | 98.9% | 98.9 | 94.8% | 98.9 | 99.3% |
Recon-Naissance | 98.9% | 98.9 | 87.6% | 98.9 | 97.8% |
Shellcode | 98.9% | 98.9 | 85.3% | 98.9 | 95.6% |
Worms | 98.9% | 98.9 | 64.7% | 98.9 | 91.7% |
Normal | 98.9% | 98.9 | 100% | 98.9 | 100% |
Weighted Average | 98.9% | 98.9 | 86.0% | 98.9 | 96.6% |
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Alotaibi, N.S.; Ahmed, H.I.; Kamel, S.O.M. Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City. Sensors 2023, 23, 7135. https://doi.org/10.3390/s23167135
Alotaibi NS, Ahmed HI, Kamel SOM. Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City. Sensors. 2023; 23(16):7135. https://doi.org/10.3390/s23167135
Chicago/Turabian StyleAlotaibi, Nouf Saeed, Hassan Ibrahim Ahmed, and Samah Osama M. Kamel. 2023. "Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City" Sensors 23, no. 16: 7135. https://doi.org/10.3390/s23167135
APA StyleAlotaibi, N. S., Ahmed, H. I., & Kamel, S. O. M. (2023). Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City. Sensors, 23(16), 7135. https://doi.org/10.3390/s23167135