CN111625820A - Federal defense method based on AIoT-oriented security - Google Patents
Federal defense method based on AIoT-oriented security Download PDFInfo
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Abstract
The invention discloses an AIoT safety-oriented federal defense method, which is characterized by comprising the following steps: each edge end node collects the confrontation attack samples encountered locally, performs the confrontation training independently by using the normal samples and the confrontation samples, sends the gradient information updated by each pair of the confrontation training to the cloud, and performs parameter updating once by using the aggregated gradient, thereby completing the model parameter synchronization in the whole distributed defense architecture. Compared with the prior art, the method has certain defense universality, when the neural network on any AIoT equipment is attacked by the countercheck sample, the attacked AIoT equipment can be defensively repaired on the premise of ensuring the privacy security of the AIoT terminal, the problems of unbalanced training data, diversified potential countercheck attack types, easy privacy disclosure in the local data uploading process and the like of the specific AIoT environment are effectively solved, and the method is simple and convenient and has good effect.
Description
Technical Field
The invention relates to the technical field of federal learning and confrontation training, in particular to a federal defense method for resisting attack of various types of confrontation samples based on AIoT oriented safety.
Background
Neural networks (neural networks) are models often used in various scenes such as computer vision, natural language processing, semantic segmentation, and the like in the field of artificial intelligence. Due to the good classification effect, the method is widely applied to various tasks related to deep learning. AIoT refers to an application that integrates with the internet of things (IoT) in the course of the fierce development of Artificial Intelligence (AI). Typically, IoT devices provide data support, while AI is responsible for computational power support. The countermeasure sample (adverialExamples) is an emerging research hotspot in recent years, and means that the purpose of trying to 'cheat' the neural network is achieved by adding tiny disturbance which is difficult to be perceived by human eyes to an original picture, so that the neural network makes wrong judgment, and the purpose of attacking is achieved. Federal learning (federal learning) is a distributed deep learning training framework proposed by Google in 2016. The method is used for solving the core problems of uneven data distribution, privacy disclosure in the local data uploading process and the like in the distributed scene.
At present, classical countersample attacks include fast gradient notation (FGSM), Basic Iterative Methods (BIM), jacobian-based saliency map attacks (JSMA), deep spoofing (DeepFool), criny-wagner attacks (C & W), simple black box attacks (SimBA), and the like. Confrontational training is a defense method that is more common at present. The method is characterized in that in the training process of the neural network, corresponding confrontation samples are added to participate in training together while normal samples are used.
In the prior art, the countermeasure training usually only uses the FGSM type of countermeasure sample attack method, the countermeasure training lacks certain defense universality, and the problems of unbalanced training data specific to the AIoT environment, potential diversification of countermeasure attack types, easy privacy disclosure in the local data uploading process and the like exist.
Disclosure of Invention
The invention aims to provide a federal defense method facing AIoT safety, which aims at overcoming the defects of the prior art, adopts the combination of federal learning and countermeasure training to construct the federal defense method facing AIoT safety, when the neural network on any AIoT device is attacked by resisting samples, the attacked AIoT device is defended and repaired on the premise of ensuring the privacy of the AIoT terminal, the method utilizes the characteristic that AIoT terminals are distributed in different environments to collect various original data and countercheck samples generated by different countercheck attack methods, gradient information which is learned by different AIoT terminal countermeasure training and resists different types of countermeasure samples is aggregated through federal learning, so that the privacy safety of AIoT equipment is greatly protected, the communication overhead between the terminal and the cloud is reduced, the transmission efficiency is improved, and the problem that the traditional countermeasure training is lack of universality and is not enough is effectively solved.
The purpose of the invention is realized as follows: a federal defense method based on AIoT safety is characterized in that federal learning and countermeasure training are combined to construct a federal defense method based on AIoT safety, when the neural network on any AIoT device is attacked by resisting samples, the attacked AIoT device is defended and repaired on the premise of ensuring the privacy of the AIoT terminal, the method collects respective training data of distributed AIoT terminal nodes at local respectively, then, each AIoT terminal model independently performs a confrontational training by using the collected training data, and after each training round is finished, each AIoT terminal will obtain the gradient information of the respective model that needs to be updated in the current round, then all the AIoT terminals send respective gradient information to the cloud end, the cloud end utilizes a federal aggregation algorithm to aggregate model gradient information sent by the AIoT terminals to obtain a final gradient, and the gradient is used for carrying out parameter updating on a cloud end model for one time; and finally, the cloud sends the updated model parameters back to each AIoT terminal device to complete the synchronization of the model parameters, and the specific federal defense of the AIoT terminal device comprises the following steps:
a) sample collection phase
Each AIoT terminal node collects different original data and encountered anti-attack samples at a local end respectively; the samples collected by each AIoT terminal have different types of countersamples caused by different positions of the AIoT terminals, different original samples and different attackers.
b) Confrontational training phase
Each AIoT terminal node performs countermeasure training independently by using respective normal samples and countermeasure samples.
c) Federal learning phase
Each AIoT terminal node sends updated gradient information to the cloud after each pair of anti-training stages, the cloud collects the collected gradient information of each AIoT terminal by using a federal aggregation algorithm, and updates parameters for one time by using the aggregated gradient; after each pair of anti-training stages of the AIoT terminal nodes, in order to protect the privacy of the AIoT terminals, the AIoT terminal nodes send to the cloud not only training data but also specific model information, but also gradient information for updating the model. Meanwhile, the method greatly reduces the communication bandwidth between the terminal and the cloud end and improves the transmission efficiency. d) Model synchronization phase
And the cloud end sends the updated model parameter information to all AIoT terminal nodes to complete the model parameter synchronization in the whole distributed defense framework.
Compared with the prior art, the method has certain defense universality, further improves the defense effect, constructs a federal defense method facing AIoT safety through the combination of federal learning and countermeasure training, carries out defensive repair on the AIoT equipment suffering attack on the premise of ensuring the privacy safety of an AIoT terminal when a neural network on any AIoT equipment is attacked by a countermeasure sample, effectively solves the problems of unbalanced training data, diversified potential countermeasure attack types, easy privacy disclosure in the local data uploading process and the like of the AIoT environment, and is simple and convenient and has good effect.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of the framework of the present invention.
Detailed Description
Referring to fig. 1, the federal defense of the present invention specifically includes the following four stages:
a) sample collection phase
The raw data that the AIoT terminal nodes distributed in different environments are exposed to is different, and the types of potential counterattack attacks that they face may be different due to different attackers. Thus, in the sample collection phase, different AIoT terminal nodes will first collect raw data and challenge samples generated by a particular type of challenge attack in their respective environments. The diversity of the original data and the confrontation samples in the AIoT scene refers to: the AIoT terminals differ in their local original samples due to their location and in the type of challenge samples due to the different attackers.
b) Confrontational training phase
When each AIoT terminal obtains enough training data and challenge samples, a separate challenge training process is started locally. The essence of the confrontation training is that corresponding confrontation samples are added into a data set of the traditional neural network training to cooperatively participate in the training. Therefore, when calculating the loss function in the reverse propagation of the neural network, the calculation needs to be divided into two parts: one part is the loss function generated by the normal training samples, the other part is the loss function generated by the confrontation samples, and the final loss function of the confrontation training is the weighted sum of the two.
c) Federal learning phase
After each pair of anti-training wheels is independently completed by each AIoT terminal, the updated gradient information of the current round is sent to the cloud server. After receiving the gradient information sent by all the terminal devices, the cloud end aggregates all the collected gradients through a federal aggregation algorithm to obtain a final gradient. The cloud server then updates the cloud model with the gradient. The AIoT terminal sends gradient information instead of training data or specific model information to the cloud server after each pair of anti-training wheels finishes, so that privacy data on each terminal device are protected from being leaked, and communication bandwidth between the terminal and the cloud is greatly reduced.
d) Model synchronization phase
After the cloud model is updated, in order to keep the cloud model and all AIoT terminal models synchronous, the cloud end sends the model parameters updated through federal learning to all AIoT terminals, and parameter synchronization of all models in the system is completed.
The present invention is further illustrated by the following specific examples.
Example 1
Referring to FIG. 2, the present invention performs normal training and joint countermeasure training as follows:
a) sample collection phase
Each edge device collects the challenge sample data set generated by the respective encountered challenge attack, and the specific implementation adopts the data set partitioning algorithm of the following algorithm 1:
the input of the algorithm comprises a total data set, the number of AIoT terminals and the serial number of each AIoT terminal node; the output of the algorithm is the raw data set Bucket obtained at each AIoT terminal node. The algorithm firstly determines the size of a Bucket according to the number of AIoT terminals; determining the range of a data set which is required to be obtained by the terminal node in a total data set according to the serial number of the terminal node; then, a random function is used for carrying out scattering treatment on the data set in the range, so that data enhancement is realized; and finally, acquiring a corresponding data set, namely the Bucket, in a subscript random access mode.
b) Confrontational training phase
Each edge device (AIoT terminal node) performs the independent countermeasure training by utilizing the collected countermeasure sample and the normal training sample;
c) federal learning phase
The specific implementation of the federal learning phase employs the federal confrontation training algorithm of algorithm 2 below:
the federal confrontation training algorithm describes in detail the specific implementation of the present invention during the sample collection phase, the confrontation training phase, the federal learning phase, and the model synchronization phase. The inputs to the algorithm include a set of known types of counterattack, cloud servers and their model parameters, AIoT terminal nodes and their model parameters, and a data set. The algorithm comprises the steps that firstly, each AIoT device sends a model initialization request to a cloud server, a data set partitioning algorithm is used for completing a sample collection stage locally, and respective original data set buckets are obtained; then, different counterattack algorithms are used for counterattacking the model on the AIoT terminal and the original data set Bucket to generate different types of countersamples; then, each AIoT device performs the confrontation training independently locally using the respective original data set Bucket and the confrontation sample, parameters required by the confrontation training are the normal sample, the confrontation sample and the correct data label, and a return value is the gradient information calculated by the model in the backward propagation through the loss function after the round of the confrontation training.
d) Model synchronization phase
Each edge device (AIoT terminal node) sends the gradient generated by the countermeasure training to the cloud server, after receiving the gradients sent by all terminals, the cloud server performs one-time aggregation on all gradients through a federal aggregation algorithm (the most common federal average algorithm is used in the experiment) to obtain a final gradient, and the cloud server model updates the model parameters by using the gradient. And finally, the cloud model sends the trained model parameters, namely the latest model parameters, to all the AIoT terminal devices, all the edge device models are updated, and the terminal models and the cloud model complete parameter synchronization so as to ensure the consistency of all the model parameters in the system.
The experimental results show that the method can effectively resist various types of attack of resisting samples, ensures the privacy security of the AIoT terminal, improves the defense performance of the model by about 20 percent on average, and has simple and convenient method and good defense effect.
The above embodiments are only for further illustration of the present invention and are not intended to limit the present invention, and all equivalent implementations of the present invention should be included in the scope of the claims of the present invention.
Claims (2)
1. A federal defense method facing AIoT security is characterized in that federal learning and countermeasure training are combined to construct a federal defense method facing AIoT security, when a neural network on any AIoT device is attacked by countermeasure samples, defensive repair is carried out on the attacked AIoT device on the premise of ensuring privacy security of an AIoT terminal, and specific federal defense comprises the following steps:
a) sample collection phase
Each AIoT terminal node collects different original data and a confrontation sample data set generated by confrontation attack at a local end respectively;
b) confrontational training phase
Each AIoT terminal node performs countermeasure training independently by using respective normal samples and countermeasure samples;
c) federal learning phase
Each AIoT terminal node sends updated gradient information to the cloud after each pair of anti-training stages, the cloud collects the collected gradient information of each AIoT terminal by using a federal aggregation algorithm, and updates parameters for one time by using the aggregated gradient;
d) model synchronization phase
And the cloud end sends the updated model parameter information to all AIoT terminal nodes to complete the model parameter synchronization in the whole distributed defense framework.
2. The method for federal defense against AIoT security as claimed in claim 1, wherein the samples collected by each AIoT terminal have different types of samples against the AIoT terminal due to different positions of the AIoT terminal, and different types of samples against the AIoT terminal due to different attackers.
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Cited By (8)
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| CN112257063A (en) * | 2020-10-19 | 2021-01-22 | 上海交通大学 | Cooperative game theory-based detection method for backdoor attacks in federal learning |
| CN112560059A (en) * | 2020-12-17 | 2021-03-26 | 浙江工业大学 | Vertical federal model stealing defense method based on neural pathway feature extraction |
| CN112668044A (en) * | 2020-12-21 | 2021-04-16 | 中国科学院信息工程研究所 | Privacy protection method and device for federal learning |
| CN112738035A (en) * | 2020-12-17 | 2021-04-30 | 杭州趣链科技有限公司 | Block chain technology-based vertical federal model stealing defense method |
| CN114283341A (en) * | 2022-03-04 | 2022-04-05 | 西南石油大学 | High-transferability confrontation sample generation method, system and terminal |
| CN114978899A (en) * | 2022-05-11 | 2022-08-30 | 业成科技（成都）有限公司 | AIoT equipment updating method and device |
| WO2023038220A1 (en) * | 2021-09-07 | 2023-03-16 | Samsung Electronics Co., Ltd. | Method and apparatus for performing horizontal federated learning |
| CN116644802A (en) * | 2023-07-19 | 2023-08-25 | 支付宝(杭州)信息技术有限公司 | Model training method and device |
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| CN114978899A (en) * | 2022-05-11 | 2022-08-30 | 业成科技（成都）有限公司 | AIoT equipment updating method and device |
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Application publication date: 20200904 |