Big Data and the Application of IoT Systems

As IoT devices become more common, security must be a priority. Many devices lack built-in protection due to design oversights and resource constraints. These limitations make them vulnerable to hacking and unable to support sophisticated security software or automatic updates. Attackers exploit these weaknesses to access and manipulate customer settings despite some devices using SSL/TLS protocols. With over 25 trillion IoT devices, achieving complete security is nearly impossible. Intrusion detection and prevention systems struggle to keep up with new attack methods, leaving devices vulnerable to various attacks like Mirai Botnet, Denial of Service, Synflood, and Man in the Middle. Furthermore, intelligent home automation systems, medical Internet devices, and building automation equipment handle sensitive user information that must be appropriately monitored. As a result, proposing an intrusion detection system for Internet of Things devices is vital to limiting the threat surface and protecting consumer data. Many literature reviews have proposed a solution using Deep Learning (DL) models. However, we found that Deep Learning requires high processing power, which is unsuitable for constraint devices. This work proposes a framework using machine learning classifiers to detect various attacks. We evaluated the classifiers against a variety of attack data. The accuracy of each classifier was recorded as follows: LR (Logistic Regression) has 97%, SVC (Support Vector Machine) has 98%, Extreme Gradient Boosting Random Forest (XGBRF) has 99%, K-Nearest Neighbors (KNN) has 99%, AdaBoost (AB) is 99% accurate, Random Forest (RF) has 99%, Decision Tree (DT) has 99%, and Extreme Gradient Boosting (XGB) has 99%.