In this blog post, the Flooding Attack, Decreased Rank Attack and Version Number Increase Attack in the RPL protocol were trained and detected by “Decision Tree”, “Logistic Regression”, “Random Forest”, “Naive Bayes”, “K Nearest Neighbor” and “Artificial Neural Networks” algorithms.

The test results for the attacks were compared, as a result of the comparison, the Artificial Neural Networks algorithm with an accuracy rate of 97.2% in the detection of Flooding Attacks, the K Nearest Neighbor algorithm with an accuracy rate of 81% in the detection of Version Number Increase Attacks, and the Artificial Neural Networks with an accuracy rate of 58% in the detection of Decreased Rank attacks algorithm has been found to show success.

I continue to share how I did my master’s thesis titled Comparison of Machine Learning Algorithms for the Detection of Vulnerability of RPL-Based IoT Devices, my experiences in this process, and the codes in this thesis in a series of articles on my blog.

So far, I have provided detailed information about the RPL protocol and the attacks that take place in the RPL protocol. Then, I experimented with Flooding Attacks, Version Number Increased Attack, and Decreased Rank Attack, extracting the raw data and making sense of that raw data. I compared the results of experiments with weak knots with statistical methods.

In this section, I will interpret the numerical results of the attacks we detect with machine learning algorithms.