Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier

Detection of intrusions in Internet Things networks is essential to maintain the availability and integrity data generated transmitted by connected devices. Such a procedure paramount when originate from critical activities, such as military, financial, industrial, health sectors. In last decades, machine learning (ML)-based approaches have become one most suitable adopted procedures for task, providing automatic, fast, accurate results. Despite success, literature still presents gap regarding valid applications intrusion detection IoT environments, which usually stands challenging task composed different types attacks. this context, work applies recent technique based on graphs logic fuzzy, namely Fuzzy Optimum-Path Forest (Fuzzy OPF), detect threats that escape an network’s regular traffic. We evaluate our model against five well-known ML algorithms, i.e., Linear Discriminant Analysis, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, standard Forest. Experimental results show OPF outperforms baselines considering accuracy, recall, F1 metrics. As result, proposal had hit rate 98 99%.