Open source software has been widely used in various industries due to its openness and flexibility, but it also brings potential security problems. Therefore, security analysis is required before using open source software. The current mainstream open source software vulnerability analysis technology is based on source code, and there are problems such as false positives, false negatives and restatements. In order to solve the problems, based on the further study of behavior feature extraction and vulnerability detection technology, a method of using dynamic behavior features to detect open source software vulnerabilities is proposed. Firstly, the relationship between open source software vulnerability and API call sequence is studied. Then, the behavioral risk vulnerability database of open source software is proposed as a support for vulnerability detection. In addition, the CNN-IndRNN classification model is constructed by improving the Independently Recurrent Neural Net-work (IndRNN) algorithm and applies to open source software security vulnerability detection. The experimental results verify the effectiveness of the proposed open source software security vulnerability detection method based on dynamic behavior features.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarm optimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
Background:
In smart grid, a flexible demand response management mechanism is used
to achieve the purpose of stabilizing the power grid, optimizing the power market, and rationally allocating
resources. There are two types of demand response management in the demand response
management mechanism: Price-based Demand Response (PDR) and Incentive-based Demand Response
(IDR).
Methods:
The paper studied the problem of privacy protection in IDR, and proposed a method based
on database digital watermark to protect user privacy. Segment the time, and then embed watermarks
in the user’s consumption data of each time segment. At the end of each billing period, extract the
watermarks from the data of each segment time, and then send the total consumption data of the user
of this billing period to the power supply company. The power supply company only knows the total
consumption data of the user, the company does not have any information regarding the users consumption
data which can prevent them from snooping the user privacy. The proposed digital watermarking
algorithm is based on K-Means clustering and wavelet transform, the K-Means algorithm is
used to cluster the database tuple data, and then wavelet transform is carried out on the available attribute
values within the clusters, and the watermark is embedded in the transformed attribute values.
Results:
The experimental results show that the proposed method is more robust when the database is
under subset deletion attacks, subset substitution attacks and subset addition attacks. Besides, the
computational cost is very low.
Conclusion:
The proposed digital watermark algorithm can embed the watermarks more decently
and overcome the burden of watermark embedding caused by statistical feature control. Besides, the
proposed method can protect the user privacy better than the other two methods.
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