| Title | A survey on Deep Learning based Intrusion Detection Systems on Internet of Things |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Slevi, S. Tamil, Visalakshi, P. |
| Conference Name | 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) |
| Keywords | cloud computing, CNN, Deep Learning, Human Behavior, Internet of Things, Internet of Things (IoT), Intrusion detection, intrusion detection system, NSL-KDD, pubcrawl, Safety, Scalability, Social Agents, System performance, Training |
| Abstract | The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity. |
| DOI | 10.1109/I-SMAC52330.2021.9641050 |
| Citation Key | slevi_survey_2021 |
A survey on Deep Learning based Intrusion Detection Systems on Internet of Things