Detection of IOT botnet attack based on machine learning

Abstract

The paper discussed the process of data processing and algorithm selection for three different scenarios in order to improve accuracy in detecting DDOS attacks, SPAM emails, and malware. It provided detailed descriptions of each process involved in the simulation. For the DDOS attack detection simulation, three different datasets were used, and missing data was removed to ensure the quality of the data. In addition, features were processed to make sure they could be applied to specific algorithms. Both decision tree and random forest algorithms were selected and tuned to obtain maximum accuracy. Similarly, for the SPAM email detection simulation, binary was used to represent whether an email was spam or not, and Count Vectorizer function was applied to convert mail contents into feature vectors. The KNN and decision tree algorithms were chosen, and emphasis was given on parameter adjustment to eliminate overfitting and ensure optimal model accuracy. The paper also discussed the importance of considering multiple factors when selecting and tuning algorithms, such as accuracy, complexity, and computational efficiency. These factors must be balanced to achieve the best overall performance. Overall, the paper provided a comprehensive overview of the methods and processes involved in data processing and algorithm selection to improve detection accuracy for DDOS attacks, SPAM emails, and malware. This research can greatly benefit organizations that are looking to enhance their security measures and minimize the risks associated with these cyber threats.