Python Programming
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
According to the originality report of my thesis work, which consists of a) Cover page, b) Introduction, c) Main sections, and ç) Conclusion, which consists of a total of 99 pages, I received on 14/01/2022 from the plagiarism detection program called Turnitin by applying the following filters, The similarity rate of my thesis is 9%.
Applied filters:
1- Except for the bibliography
2- Excluding/including citations
Except for parts of the text with less than 5 words of overlap
I have examined the Application Procedures and Principles for Obtaining and Using the Originality Report of the Graduate Thesis Study of the National Defense University Hezârfen Aerospace Technologies Institute and according to the maximum similarity rates specified in these Application Principles, my thesis does not contain any plagiarism; I declare that I accept all kinds of legal responsibility that may arise in a possible situation where the opposite is determined and that the information I have given above is correct.
Murat Ugur KIRAZ
January 18, 2022
In this thesis study, which I prepared in accordance with the rules in the National Defense University Institutes Master’s Degree Thesis Preparation Guide; I have obtained the data, information and documents I have presented in the thesis within the framework of academic and ethical rules, that I have presented all information, documents, evaluations and results in accordance with scientific ethical and moral rules, that I have cited all the works I have used in the thesis by making appropriate references, that I have not made any changes in the data used. declares that the work I have presented in this thesis is original; Otherwise, I declare that I accept all loss of rights that may arise against me.
The thoughts, opinions, assumptions, arguments, or theses in this thesis are my own; Ministry of National Defense, National Defense University and Hezarfen Aerospace Technologies Institute cannot be held responsible.
Murat Ugur KIRAZ
January 18, 2022
To my precious mother, father and wife who have not spared me their financial and moral support so far.
Computer science had a tremendous impact on the world, like the Conquest of Istanbul, the French Revolution, and the industrial revolution changed the world order and reshaped the world. The person/institution/state or community that best shapes, manage, and advances this reality will retain advantages over others, which is an undeniable fact.
The progress of the computer science revolution has gained tremendous momentum with the “internet of things” devices that are currently used in our daily life and industry and communicate with each other and go to the internet. Especially in today’s world, when there is an energy problem. We need to use energy most efficiently. A protocol that works in low-power and lossy networks to achieve the maximum energy efficiency of IoT devices was established in March 2012 by the Internet Engineering Task Force (IETF-Internet Engineering Task Force) with RPL (Routing). Protocol for Low-Power and Lossy Networks – IPv6 Routing Protocol for Low-Power and Lossy Networks) was revealed.
With the RPL protocol, IoT devices work in a stable, fast, and energy-saving manner. The complexity of this protocol and the low-security nature of 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks- IPv6) devices running on this protocol as such, this protocol is vulnerable to attacks inside or outside the network. Therefore, researchers developed many intrusion detection systems to detect attacks on devices running on the RPL protocol with machine learning algorithms. We should ask the following question to find the system that gave the best results among these developed systems or develop a better one. Which machine learning algorithm is the most effective in detecting attacks in the RPL protocol? This thesis work was born with this question.
The first chapter is the aim of the thesis, the existing studies in the literature, and which this thesis study explains deficiencies in the literature. The second chapter contains general information about the protocols operating in IoT devices, especially the RPL protocol, and we explain the attacks on this protocol in detail. The third chapter explains how we did the experiments and which methods we followed. In the conclusion, we interpreted the data obtained from the experiments.
Istanbul, January 2022
Murat Uğur KİRAZ
Comparison of Machine Learning Algorithms to Detect RPL-Based IoT Devices Vulnerability
Murat Ugur KIRAZ
National Defense University, Hezârfen Aeronautics and Space Technologies Institute
Istanbul, January 2022
The RPL protocol (Routing Protocol for Low-Power and Lossy Networks) is a network protocol designed by IETF (Winter, et al. March 2012 1 ) to optimize power consumption in the Internet of Things (IoT) devices. IoT devices have limited processing power, limited memory, and limited energy because they usually run on batteries. Designed to solve the energy problem in lossy networks, RPL aims to establish the shortest distance by creating a DAG (Directed Acyclic Graph) of n number of IoT devices over each other. Thus, it optimizes the energy expended. However, due to the complex infrastructure of the RPL protocol and the low capacity of IoT devices, this protocol is vulnerable to attacks. Therefore, it is crucial to develop a fast, practical, uncomplicated, and reliable intrusion detection system at the network layer. An anomaly will occur in layer three network packets in the event of an attack on RPL-powered IoT devices. Processing these packets with machine learning algorithms will make it extremely easy to detect the attack. (D’Hondt, Bahmad, Vanhee, & Sadre, 2015 2 ) succeeded in simulating Flooding, Version Number Increase, and Decreased Rank Attacks. With the normal and malicious IoT motes that they created; the raw data sets of each attack were obtained. After the raw data sets were made meaningful, these data sets were separated as 1/3 test and 2/3 training data set. Datasets were trained and tested by “Decision Tree,” “Logistic Regression,” “Random Forest,” “Naive Bayes,” “K Nearest Neighbor,” and “Artificial Neural Networks” algorithms. As a result of the comparison, the Deep Learning algorithm detected Flooding Attacks with a 97.2% accuracy rate. The K Nearest Neighbor algorithm detected Version Number Increasing Attack with an 81% accuracy rate. The Deep Learning algorithm detected decreased rank attacks with a 58% accuracy rate.
Keywords : RPL, Machine Learning, Flooding Attacks, Version Number Increase Attacks, Decreased Rank Attacks
1. Winter, T., Thubert, P., Brandt, A., Hui, J., Kelsey, R., Levis, P., . . . Alexander, R. (Mart 2012). RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks. Internet Engineering Task Force. https://www.hjp.at/doc/rfc/rfc6550.html (Back)
2. D’Hondt, A., Bahmad, H., Vanhee, J., & Sadre, R. (2015). RPL attacks framework. Louvain-la-Neuve, Belgium: Universit catholique de Louvain. https://github.com/dhondta/rpl-attacks (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.