Edge computing solves such questions as the massive multisource data and resource consuming computing tasks in edge devices. Some new security problems especially the data security and privacy issues have been introduced into the edge computing scenario. Through analyzing the biological immune principles, a novel idea for the problem of intrusion detection in edge computing is provided. Specifically, an edge intrusion detection system (Edge IDS) with a distributed structure, which has the characteristics of an imprecise model, self-learning, and strong interactivity, is constructed in a systematic way inspired by the biological immune principles. Moreover, a newly proposed gene immune detection algorithm (GIDA) is designed. In order that Edge IDS can deal with the dynamic data problem efficiently, the key functional components such as the remaining gene, niching strategy, and extracting vaccine are embedded into the GIDA algorithm. Furthermore, extensive simulation experiments are conducted, and the results show that the proposed Edge IDS can be adapted to the domain of edge computing with comparative performance advantages.

At present, the employment situation in China is very severe, and there are both recruitment and employment difficulties in the society. There are many reasons for this problem, among which, the incompatibility between talents training and social demand in higher vocational colleges is one of the main reasons, and it is necessary for higher vocational colleges to effectively evaluate students’ skills training with the real demand. Based on this, this paper studies the study of stochastic algorithm and skill evaluation system based on the combing of the construction mechanism of higher vocational professional clusters and builds a higher vocational skill evaluation system based on a simple analysis of the skill evaluation system and related evaluation algorithms in higher vocational institutions. Principal component analysis was selected to realize the quantitative processing of skill evaluation indexes, and random forest algorithm was used to realize skill evaluation. The simulated annealing algorithm is introduced to realize parameter selection, parameter optimization, and weight setting, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the random forest algorithm is applied to skill evaluation with high accuracy, small error, and better generalization ability.