3results about How to "Effective and informative" patented technology

The method comprises the following steps: firstly, collecting flow information of an algorithm generated domain name (DGA domain name) and a benign domain name of a known classification label, wherein the flow information comprises an analysis IP address and Flow data of the domain name; performing primary processing on the collected flow data, counting the occurrence frequency of each resolution IP of a domain name, and screening out information such as a source address, a destination address and time from the Flow data; calculating eight characteristic values of the domain name according to the flow information, wherein the eight characteristic values comprise the user IP access number, the active frequency, the standard variance mean value ratio of the number of times of occurrence of each analysis IP and the like; putting the feature data into a C4.5 supervised classification model for training to obtain a classification model with classification capability; and finally, collecting flow information of to-be-classified domain names, calculating feature data, and putting the feature data into the trained classification model for classification. According to the method, the difference between the flow behavior expression and the benign domain name when the domain name is generated by an algorithm to carry out malicious activities is utilized, the two types of domain names are effectively distinguished, the antagonistic attack is resisted, and the security of the network is maintained.

This invention belongs to the field of data processing technology, specifically disclosing a safety assessment method for energy storage power stations based on real-time monitoring data. The method includes: performing multimodal feature analysis on voltage and environmental data sequences of the energy storage power station to obtain static voltage feature tensors and static environmental feature tensors; conducting collaborative analysis of the static voltage and environmental feature tensors to obtain an environmental voltage feature correlation group; performing entropy weight dynamic analysis on the environmental voltage feature correlation group to construct a multidimensional state vector; performing time-series deduction on the multidimensional state vector to obtain a safety situation evolution matrix; and performing residual analysis between the safety situation evolution matrix and real-time monitoring time-series data to obtain a comprehensive deviation index; and performing root cause assessment on the comprehensive deviation index to obtain comprehensive risk information. This invention enables more accurate, dynamic, and efficient safety assessment of energy storage power stations, allowing for comprehensive situation perception and rapid identification of risk root causes.

The present invention discloses a sample generation method, device, equipment and medium for unbalanced user attack data, the method comprising obtaining a user attack behavior data set; generating a minority class feature map according to a minority class set of the user attack behavior data set; constructing and training an adversarial network of dual generators and dual discriminators according to the minority class feature map; using the trained adversarial network to generate a minority class prediction feature map, summarizing the minority class feature map, the minority class prediction feature map and the majority class feature map, and obtaining a user attack feature sample data set with a balanced quantity. The present invention generates an RGBA feature map of minority class attack behavior, which can provide more effective information and accurately characterize the internal feature information of user attack behavior data; the network is optimized and trained according to the continuous improvement of the objective function of the dual generator and the loss function of the dual discriminator, the similarity between the generated data and the real data is improved, and the problem of unbalanced feature samples of user attack data is solved.