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Export Tables from Access to PostgreSQL
This article describes how to export a table from Access DB to postgresql via excel and python.
March 14, 2023
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- mukiraz@mukiraz.com
Internet of Things devices are pieces of hardware such as sensors, actuators, devices, or machines that are programmed to perform a specific function and can transfer data over the Internet or other networks. IoT devices are a collection of non-system devices interconnected over a network to perform decision-making. (Lueth, 2014 1)
In order to increase the welfare of human life, the number of IoT devices that provide communication between machines and objects, make calculations and coordinate with each of them is increasing day by day. While there were 8.7 million devices in 2012, this number increased to 50.1 million in 2020. (Burhan, Rehman, Khan, & Kim, 2018 2)
The expectation from IoT devices is cost-effectiveness. IoT devices are required to perform simple operations and transfer the data they process in large numbers. IoT devices have low memory capacities, low processor speeds, and low network communication speeds to meet this demand. Therefore, existing communication protocols may not be cost-effective for IoT devices. As a result, the need to create new communication protocols for IoT Devices has arisen.
1. Lueth, K. L. (2014, Aralık 19). iot-analytics. 2021 tarihinde https://iot-analytics.com/internet-of-things-definition/ (Back)
2. Burhan, M., Rehman, R. A., Khan, B., & Kim, B.-S. (2018). IoT Elements, Layered Architectures and Security Issues: A Comprehensive Survey. Sensors, 1-37. doi:10.3390/s18092796 (Back)
This article describes how to export a table from Access DB to postgresql via excel and python.
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.