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This chapter discusses and concludes the overall outcomes obtained in this project. In fact, it is the last and final phase in studying the project and it is used to see whether the objectives have been met and fulfilled. The project can be summarized by stating its objectives and how it is achieved. Apart from that, the contribution of the project, how it is analyzed and the limitation of the project will also be further explained in this chapter. These are all the important traits that will be discussed so that improvements can be done for future studies and researches.

6.2 Project Summarization

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IoT devices have become an attractive substitute device because of the rapid development in compute intensive device technologies. Ultimately, these trends have opened the door for cybercriminals to expand their malevolent motivations towards recent evolving platform. In order to be able to detect the possible attack in IoT devices such as DDoS attack proper analysis is needed. The first objective is to study possible attacks which are used to infect IoT devices. The IoT botnet phenomenon is aiming to gain illegitimate access to IoT devices to carryout various malicious activities. Once the behavior has been identified, the objective of this project which is to analyze the behavior of IoT botnet attack on basic mode of operations and communications.

This project approach Weka, a collection of machine learning algorithms for data mining tasks. The framework is decomposed into two components such as dynamic analysis component and learning component. During dynamic analysis, applications are required to be executed in a secure Wireshark environment and the results are collected for further classification in Weka. Finally, in the learning component the sample of a known botnet dataset are trained with the help of five classifiers such as RandomForest, J48, JRip, NaiveBayes and BayesNet. Various machine learning classifiers are applied to determine the most suitable classification algorithm to draw a clear line between botnet and other types of malicious applications.

Analysis of botnet with its threat can be done with experiments. This machine learning should be able to detect an botnet attack. Once the signature of attack have been identified, the last objective which is to measure the best method of machine learning network based on botnet detection which is using Weka. Therefore, it can be summarized that, this signature-based detection has better prediction and capabilities to distinguish between the normal and attack events reached for thousands of dataset for each variant.

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