Paper Title
Attack Detection usingCNN based on Genetic Algorithm for IoT

Abstract
Today there are more than 4.39 billion internet users that almost 70 percent of them use social media on mobile devices. Network security is one of the most important aspects to consider whileworking over the internet, LAN or other networks, no matter how small or big your business is. We know many zeroday attacks are continuously emerging because of the addition of various protocols mainly from Internet of Things (IoT). previously known attacks in cyberattacks can detect by usingArtificial Intelligence (AI) solutions such as Neural Networks, Machine Learning (ML), Support Vector Machine (SVM), decision tree, Hidden Markov Model (HMM), Hierarchical Clustering, Game Theory (GT), and Natural Language Processing (NLP). But advanced mechanisms of AI are not able to detect all of attacks. On the other hand Deep Learning (DL) techniques which are capable of providing embedded intelligence in the IoT devices and networks, are emerged to cope with different security problems. Then we used Convolutional Nneural Network (CNN) and Genetic Algorithm. Keywords - Network Security, Convolutional Nneural Network (CNN), Deep Learning and Genetic Algorithm.