Internet of Things malicious traffic detection method based on fog computing platform

Abstract

The invention relates to an Internet of Things malicious traffic detection method based on a fog computing platform, and the detection method is operated based on the fog computing platform, and comprises the steps: (1) building an experimental network, and building a feature extraction module which is used for collecting a traffic data packet of IoT equipment under jurisdiction; building a Miraibotnet environment, and obtaining a traffic data packet when a DDoS attack is initiated; (2) performing feature value classification and extraction on the traffic data packet by using a feature extraction module to generate feature data; (3) by a data processing module, performing data dimension reconstruction on the feature data; and (4) building a convolutional neural network on the fog computing node. According to the method, the convolutional neural network anomaly detection algorithm is transplanted to the field of IoT botnets, so that the identification rate of DDoS malicious traffic ofIoT equipment is improved; based on fog computing platform architecture construction, real-time detection of abnormal traffic of IoT equipment under jurisdiction is realized, and consequences of DDoSattacks are reduced.

Description

technical field [0001] The invention relates to a method for detecting malicious traffic of the Internet of Things based on a fog computing platform, and belongs to the technical field of network security. Background technique [0002] Distributed denial of service attack (DDoS, distributed denial of service) is a highly harmful distributed and large-scale coordinated network attack method. , referred to as IoT) devices, while launching a denial of service attack (DoS, denial of service) to the attacked target, which eventually causes the system resources of the attacked target to be exhausted or even collapse, and the attacked target "refuses" to provide the required services for normal users. DDoS attacks mainly target the system resources and network bandwidth of the attacked target, and the attack range includes the network layer to the application layer. In October 2016, the Mirai botnet controlled more than 100,000 IoT devices to carry out distributed denial-of-servic...

AI Technical Summary

Problems solved by technology

Compared with the fourth quarter of 2016, the total number of DDoS attacks increased by 14% in the fourth quarter of 2017. DDoS attacks have caused serious security threats to systems and networks. According to the latest research, almost all DDoS attackers will use More than two vector attack methods are used to carry out attack activities, which makes the detection of malicious traffic more difficult

However, due to practical reasons such as the high complexity of the algorithm and the limited hardware level of the deployment framework, few scholars have applied CNN to the field of IoT malicious traffic monitoring.

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