Machine Learning based Intrusion Detection for Cyber-Security in IoT Networks

IoT network is a promising technology, implementation growing rapidly but cybersecurity still loophole, detection of attacks in IOT infrastructures concern the field IoT. With increased use Internet Things different areas, cyber-attacks are also increasing proportionately and can cause failures system. IDS becomes leading security solution. Anomaly based intrusion (IDS) plays major role protecting networks against various malicious activities. Improving loT has become one most critical issues. This due to large-scale development deployment devices insufficiency Intrusion Detection Systems be deployed for special purpose networks. In this article, performance several machine learning models been compared accurately predict on systems, case imbalanced classes was subsequently treated using SMOTE technique. The Nystrom kernel SVM first time used detect results promising. evaluation metrics comparison accuracy, precision, recall, f1 score, auc-roc curve.