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About me

Hello,

My name is Murat Ugur KIRAZ. I am a front-end developer. I consider myself a self-disciplined, determined, hardworking, analytical web programmer. I can design, develop, and test web-based applications. I can provide high-impact web solutions with React, Angular, HTML, CSS, Bootstrap, Javascript, JQuery, UX, and UI.

I know how to work as a part of the system and the team, as my previous work experience requires management, effective communication, coordination, and teamwork.

Murat Uğur KİRAZ, Front-End Developer, Computer Programmer

Last Posts

Making Raw Data Meaningful

The information obtained from the raw data set will not be enough to apply machine learning. The raw data obtained from simulations containing weak nodes is completely different from the raw data obtained from simulations containing normal motes. It has been observed that this difference is the number of packets, message types, total packet lengths and rates. To detect this anomaly, the raw data is divided into 1-second frames. Within frames of each second, the following values were calculated, and a new data set was created.

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Simulation and Raw Data

In the previous article, I explained how to obtain nodes created for Flooding Attacks, Decreased Rank Attacks, and Version Number Increase Attacks from the RPL Attacks Framework that D’Hondt and others have done.

In this article, I will simulate these nodes using Cooja and obtain network data.
For machine learning, we will need two classified data sets. One of them is the data generated from the simulation with completely normal IoT nodes that do not contain vulnerable nodes. The other is the data generated from simulation with normal IoT nodes containing vulnerable nodes. Thus, we will classify these two data sets and detect the anomaly with classification algorithms.

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Obtaining Nodes

In my previous article, Contiki ve Cooja, I described how to set up Cooja to simulate IoT devices on a virtual computer with the Ubuntu 18.04 operating system. With this virtual computer, we will simulate the data transfer of benign and malicious IoT devices and get network information. Of course, we need ” benign ” and ” malicious ” nodes to do this simulation. I explained how to install the framework that D’Hondt et al. (2015) did under the heading D’Hondt’s RPL Framework to obtain these vulnerable nor normal nodes.
In this article, I will explain how we obtain the weak nodes and normal nodes where “Hello Flood”, “Decreased Rank” and “Version Number Increase” attacks will be made from the work done by D’Hondt and others (2015).

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D’Hondt’s RPL Framework

In an academic report by D’Hondt et al. (2015), they were able to simulate Flooding Attacks, Version Number Increase Attacks, and Decreased Rank Attacks on the RPL protocol using the Cooja IoT simulator. Here you can find information about how to set up D’Hondt’s RPL Attack Framework.

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Contiki and Cooja

How to install Contiki Operation System and Cooja on Ubuntu 18.04 ?You can find answer and a good solution for this question in this page.

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Simulation and Raw Data

Under this title, experiments will be conducted on Flooding Attacks, Version Number Increase Attacks and Decreased Rank Attacks that may occur in the RPL protocol and a data set will be created. For this purpose, the following stages will be followed:

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