A NOVEL IOT-HONEY POT SYSTEM WITH BLOOM FILTER ENCRYPTION AND CONVOLUTION NEURAL NETWORK

In the current digital era where all the data is stored digitally over the cloud, the risk of mishandling the data and cyber-attacks has increased drastically. Cloud storage companies have used several techniques to safeguard the data, but the number of attacks keeps increasing every year. One such technique is the use of Honeypot in the Internet of Things (IoT) (HIoTPOT) domain. The Honeypot analyses different recent threats that are risky for IoT devices as it has a capability of continuous monitoring of IoT devices. The research on honeypot with implementation is presented in this paper that can be used to learn the used recent trends and tactics of the intruders. This paper proposes a hybrid model using data encryption and Artificial Intelligence. The data stored in the cloud is encrypted using Bloom Filter which is very fast and efficient. The input user request is fed to a pre-trained Convolution Neural Network model to detect the attacks. If the model classifies the user as an attacker, false data from honeypot is fed to the user while gathering crucial information about the attacker. Actual data is only released when the AI detects the identifies the user as authentic. The proposed AI model has been compared with the existing techniques to prove the efficiency of the system.