Internet of Things attack detection using hybrid Deep Learning Model - Sahu, Amiya Kumar; Sharma, Suraj; Tanveer, M.; Raja, Rohit (Computer communications, 2021)
The Internet of Things (IoT) has become a very popular area of research due to its large-scale implementation and challenges. However, security is the key concern while witnessing the rapid growth in its size and applications. It is a tedious task to individually put security mechanisms in each IoT device and update it as per newer threats. Moreover, machine learning models can best utilize the colossal amount of data generated by IoT devices. Therefore, many Deep Learning (DL) based mechanisms have been proposed to detect attacks in IoT. However, the existing security mechanisms addressed limited attacks, and they used limited and outdated datasets for evaluations. This paper presents a novel security framework and an attack detection mechanism using a Deep Learning model to fill in the gap, which will efficiently detect malicious devices. The proposed mechanism uses a Convolution Neural Network (CNN) to extract the accurate feature representation of data and further classifies those by Long Short-Term Memory (LSTM) Model. The dataset used in the experimental evaluation is from twenty Raspberry Pi infected IoT devices. The accuracy of the empirical study for attack detection is 96 percent. In addition, it is observed that the proposed model outperformed various recently proposed DL-based attack detection mechanisms.