Abstract: The disclosure provides a security analysis method and system based on protocol state, which relates to the technical field of protocol security protection. The method includes the following: a node traversal table is built, the node traversal table is scanned and analyzed according to the protocol trigger sequence rule, a first security evaluation factor of a protocol stack is determined, and a second security evaluation factor of each protocol is determined based on protocol normal application rule, and the trustworthiness degree of the second security factor is determined based on the first security factor, and the second security factor is revised based on the trustworthiness degree, and the security state of the protocol is determined according to the revised second security factor, thus the analysis of the protocol state is realized, and the security of the protocol can be accurately determined.

Abstract: The disclosure provides a security analysis method and system based on protocol state, which relates to the technical field of protocol security protection. The method includes the following: a node traversal table is built, the node traversal table is scanned and analyzed according to the protocol trigger sequence rule, a first security evaluation factor of a protocol stack is determined, and a second security evaluation factor of each protocol is determined based on protocol normal application rule, and the trustworthiness degree of the second security factor is determined based on the first security factor, and the second security factor is revised based on the trustworthiness degree, and the security state of the protocol is determined according to the revised second security factor, thus the analysis of the protocol state is realized, and the security of the protocol can be accurately determined.

Abstract: The functions and capabilities of a computer are improved by programming the computer to provide market demand forecasts for a new product that are more accurate than forecasts generated using conventional approaches. A demand forecast for a new product is generated by pairing or associating a set of one or more existing products with the new product, receiving historical sales data for the existing product, separating the historical sales data into a plurality of discrete components, constructing a respective feature-based predictive model for each of corresponding components of the plurality of discrete components, generating a corresponding prediction from each respective feature-based predictive model for each of the plurality of discrete components, and aggregating each corresponding prediction for each of the plurality of discrete components to generate the demand forecast for the new product.