Automatic attack detection in IOT environment using relational auto encoder with enhanced ANFIS

Abstract

The Internet of Things (IoT) has recently become an important innovation in building smart environments. With any technology that relies on the Internet of Things model, security and privacy are seen as key issues. Many privacy and security concerns arise due to the various possibilities of intruders to attack the system. Due to the dynamic and heterogeneous nature of IoT devices and networks, we propose a novel approach for attack detection in IoT environments by combining two modifications based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). For the efficient extraction of features from input datasets, we use a Relational Auto Encoder (RAE) Network, followed by an enhanced version of the ANFIS model. ANFIS parameters have been optimized to use Gaussian kernel membership functions and the Enhanced Osprey optimization algorithm (EOOA) has been used to optimize initial ANFIS parameters. As part of the experimental analysis, two sets of datasets are used; these are NSL-KDD 99 and UNSW-NB15 datasets, which contain different kinds of attack labels such as DoS, probing, U2R, and R2L attacks. Performance metrics including accuracy, precision, recall, and F-measure are used to assess the effectiveness of our proposed scheme. As a result of this approach, we have demonstrated promising results in identifying attackers for IoT security applications, while also offering robustness and scalability.