To study the organizational structure of particles in particle swarm optimization (PSO), we have proposed the family PSO (FPSO) previously. To further study the internal structure of FPSO, this paper introduced the animal collective behavior into the FPSO. It made the interaction ruling among particles was not based on random selection but topological distance. Each family interacted on average with a fixed number of neighbors, rather than with all neighbors within a fixed metric distance. Simulations for four benchmark functions demonstrated that the interaction ruling based on topological distance among particles was more reasonable than that on random selection.
Enterprises needs to realize the unity of economic and social benefits. From the marketing theory, we know that the economic and social benefits form a symbiotic effect, which determines that enterprises need to pay attention to their own service quality. The current risks are in the areas of “customer relationship management” and “customer technical support,” specifically, the inability to effectively meet the needs of customers in terms of corporate infrastructure and product details, as well as the increase in costs for other customer services. The article firstly completes the construction of marketing risk early warning system, analyzes marketing risk types and formation factors, constructs indicators and mathematical modeling, then proposes the ANN combined with Petri algorithm, which is used to realize marketing risk early warning, completes sample selection and indicator quantification, sets up the early warning model, and conducts false alarm and false alarm degree analysis.
Malware evolves for the same reasons that ordinary software evolves. Like any other software product, the standard genetic operators selection, crossover and mutation are applied to evolve new malware. Recognizing and modeling how these malware evolve and are related is an important problem in the area of malware analysis. Grouping individual malware samples into malware families is not a new idea, and content-based comparison approaches have been proposed. Content-based approaches are hard to identify the real behavior of malware and it is inherently susceptible to inaccuracies due to polymorphic and metamorphic techniques. In this paper, we leveraged dynamic analysis approach to classify malware variants. The results demonstrate that our technique is able to recognize and group malware programs that behave similarly, achieving a better precision than previous approaches. The major advantage of our approach is that it can precisely tracks the sensitive information of malware behavior and is immune to obfuscation attempts. Our research is conducive to study the problem of malware classification, malware naming, and the phylogeny of malware.
With the development of Internet technology and social model, game products have become an important product of people’s life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market is the guarantee of innovative development of game companies. For the research on the above problem, based on the SEMAS process of data mining, this paper proposes a mining model based on recurrent neural network, which is named as Dynamic Attention GRU (DAGRU) with multiple dynamic attention mechanisms, and evaluates it on two self-built data sets of user behavior samples. The results demonstrate that the mining method can effectively analyze and predict the player behavior goals. The game marketing system based on data mining can indeed provide more accurate and automated marketing services, which greatly reduces the cost investment under the traditional marketing model and achieves accurate targeting marketing services and has certain application value.
The API calls reflect the functional levels of a program, analysis of the API calls would lead to an understanding of the behavior of the malware. Malware analysis environment has been widely used, but some malware already have the anti-virtual, anti-debugging and anti-tracking ability with the evolution of the malware. These analysis environments use a combination of API hooking and/or API virtualization, which are detectable by malware running at the same privilege level. In this work, we develop the fully automated platform to trace the native API calls based on secondary development of Xen and have obtained the most transparent and similar system to a Windows OS as possible in order to obtain an execution trace of a program as if it was run in an environment with no tracer present. In contrast to other approaches, the hardware-assisted nature of our approach implicitly avoids many shortcomings that arise from incomplete or inaccurate system emulation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.