Research And Implementation Of Home Iot Security Protection System Based On Edge Computing

Due to the heterogeneity and increasing number of IoT devices,traditional terminal and network security solutions cannot guarantee system security.In the majority of cases,manufacturers can not provide firewall updates and security patches on a regular basis,and it is very difficult to develop effective terminal security solutions with limited resources on IOT devices.At the same time,traditional cloud computing services in the home Internet of things environment has the limitations of insufficient real-time and bandwidth,which is not conducive to the protection of data security and privacy.Usually,hackers conduct vulnerability tests on target smart home devices through network intrusion,thereby stealing user's privacy data,or even disrupting the normal operation of smart home network systems.This article summarizes the current research status of home IoT security,analyzes and explains the new IoT security mechanisms and intrusion detection algorithms that have appeared in recent years.It focuses on network intrusion detection schemes based on machine learning,and chooses to use fuzzy C-mean(FCM)as a classification model for intrusion detection.On this basis,this paper proposes an incremental network traffic feature extraction strategy based on the statistics of the number of connections.Further more,each traffic feature is analyzed and researched,and feature clipping is performed to make the feature vector as simple as possible on the premise of simplicity to represent the real network behavior,which effectively improves the accuracy and performance of intrusion detection.For practical testing,this paper implements an edge network protection system that detects network intrusions in real time.The system uses Raspberry Pi as the experimental environment,and an edge intelligent gateway is deployed on it to run the SDN controller and Open vSwitch(OVS)virtual switch to achieve the purpose of monitoring,analyzing and filtering the traffic in the network.Utilize the computing resources on the edge gateway and use lightweight machine learning clustering model to classify the network transmission between IoT devices,devices and public networks,and detect whether there is network intrusion.The clustering model extracts the features of network traffic and distinguishes malignant traffic from benign traffic in the network.Simulation results show that the system has a high intrusion recognition rate(?95.2%)for common IoT network intrusion behaviors,which can effectively ensure the security of interaction between home IoT devices,and the impact on network performance is acceptable within the range,which means it has practicality.