Title:IoT-Shield: A Novel DDoS Detection Approach for IoT-Based DevicesAuthors with Affiliation:Ghazaleh Shirvani , Department of Computer Engineering Iran University of Science and Technology Saeid Ghasemshirazi , Department of Industrial Engineering Iran University of Science and TechnologyBehzad Beigzadeh , Department of Electrical and Computer Engineering Tarbiat Modares UniversityPresenter :Ghazaleh Shirvani11th Smart Grid Conference (SGC 2021) So Today I’m going to talk about a novel DdoS detection approach for IoT devices But before I get to that out I’ll share with you some of the work that have been done in this area.

1. Title:
IoT-Shield: A Novel DDoS Detection Approach for IoT-Based Devices
Authors with Affiliation:
Ghazaleh Shirvani , Department of Computer Engineering Iran University of Science and Technology
Saeid Ghasemshirazi , Department of Industrial Engineering Iran University of Science and Technology
Behzad Beigzadeh , Department of Electrical and Computer Engineering Tarbiat Modares University
Presenter :Ghazaleh Shirvani
11th Smart Grid Conference (SGC 2021)
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2. Introduction:
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An IoT device is simply an electronic device that is connected to the Internet.
There are several basic properties that qualify a device as an “IoT” device:
▪ A physical device/object
▪ Contains controller(s), sensor(s), and or actuator(s)
▪ Connects to the Internet
Denial of Service Attack: an attack on a computer or network that prevents legitimate use of its resources
DDoS Attacks Increasing in Size, Frequency & Complexity.

3. Background:
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Data Mining vs. Process Mining
Process Mining Phases:
• Discovery
• Monitoring
• Optimization

4. PROBLEM STATEMENT:
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❖ Almost every piece of technology we buy is “Connected” to the internet.
❖ IoT devices appear to be more vulnerable to security attacks
❖ Security management is difficult because of IoT devices characterized by limited resources.

5. Contribution:
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❖ We are proposed a combination model of machine learning & process mining approach
named IoT-Shield.
❖ IoT-Shield can predict DDoS attacks and misbehavior on IoT Devices.

6. Proposed method:
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7. Detailed view of process mining block
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8. Dataset:
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❖ We used the NSL-KDD Dataset
❖ Improvements to the KDD'99 dataset
Type Number of features
Categorical 4
Binary 6
Discrete 23
Continuous 10

9. Results:
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Algorithm
Evaluation Metrics
Accuracy F1-Score
Training
Time
XGBoost 99.58% 0.99 10s
ADABoost 98.99% 0.98 13s
Decision Tree 99.38% 0.97 2s
KNN 99.61% 0.99 21s
Random
Forest
94.89% 0.94 4s
Naïve Bayes 53.04% 0.34 <1s
MLP 96.11% 0.96 15s
Experimental setup:
❖ A two-core Xeon processor with 2.2 GHz
❖ 33 GB HDD
❖ 13 GB RAM

10. CONCLUSION AND FUTURE WORK
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✓ Security threats are a big issue with IoT devices due to limited resources (CPU, battery, and
memory).
✓ We are inspired to create a Real-Time DDoS detection with multiple-class classification and
mitigation platform for IoT and IIoT devices in the future.
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95
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Data Mining
Process Mining
Proposed Method
Performance Comparison
Without Feedback With Feedback

11. Thank You for
Attention
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