A Method for Internet of Things Intrusion Behavior Detection Based on Cascaded Filtering
A method for detecting intrusion behavior in the Internet of Things based on cascaded fil-tering has been proposed.A multi-scale sample set for denial of service attacks was established,and Naive Bayes network was used to mine the characteristics of denial of service attacks,obtaining fuzzy statistical feature quantities for Internet of Things intrusion behavior forensics.The cascaded filtering analysis method was used to gradually screen out the most relevant features to intrusion behavior,en-hancing the relevant intrusion information.By combining the information concentration,behavior distribution,and envelope amplitude of Internet of Things intrusion behavior,Internet of Things intrusion behavior detection has been achieved.The experimental results show that the proposed method can effectively extract the characteristics of denial of service attacks and accurately detect the number of times the Internet of Things has been subjected to denial of service attacks.The response time is within 1.5 s.This method has strong adaptability and can effectively improve the intrusion detection capability of the Internet of Things.
Naive BayesInternet of Thingsinvasion behaviordenial of service attackscascade filteringfeature extraction
万方数据
维普
