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Identifying Botnet on IoT by Using Supervised Learning Techniques


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1 Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
 

The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.

Keywords

Botnet, DDoS, IoT, Malware, Machine Learning, Supervised Learning.
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  • Identifying Botnet on IoT by Using Supervised Learning Techniques

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Authors

Amirhossein Rezaei
Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia

Abstract


The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.

Keywords


Botnet, DDoS, IoT, Malware, Machine Learning, Supervised Learning.

References