An Exploratory Study on Application of Machine Learning in Detecting Cyberattacks

Intrusion detection systems are vital cybersecurity systems in the modern world. IoT devices have taken over systems around us. Due to the limited computational capability coupled with the open endpoint nature of IoT devices, they are susceptible to attacks. Such attacks jeopardize the security of every device on the network, including vital servers and databases. In this work, we survey and experiment with some famous and widely used machine learning algorithms to detect and mitigate these attacks. We employ a divide and conquer approach to classify the incoming traffic into different types of attacks to counter the skewness in the dataset used. We use the results to evaluate the models on markers like precision, recall, accuracy, and balanced accuracy. The observations are tabulated and concluded based on computational costs and their efficiency. We study the effectiveness of modern machine learning algorithms like decision trees, rotation trees, K ‐means, ridge, hidden Markov models, gradient boosting, and multiple bagging techniques in differentiating among incoming network traffic.