Secure Enterprise Network Research Articles

Ensuring Data Compliance: The role of AI and ML in securing Enterprise Networks

This work describes data compliance complexities and the preventive architecture principles required to identify and thwart associated information breaches. The fundamental quest in the elaboration of data compliance mechanisms is not only to identify those fires but to circumvent or contain the wildfires before and after they erupt. Machine learning (ML) and artificial intelligence (AI) technologies that augment cybersecurity technologies can play a major role here by learning, simulating, and analyzing adverse information scenario potentials. Various architecture patterns emphasizing preventive cautionary methods and practices related to data compliance at facilities entrusted with sensitive information are previewed using data sensitivity, risk, severity, continuity/integrity, and examination/audibility compliance prerequisite patterns.

To Design a Network That Delivers Reliable Performance 24 Hours a Day 7 Days a Week for Higher Education in Uganda

It aims at existing defects of traditional VPN (Virtual Private Network) in constructing enterprise network, analyses problems which must be considered in designing secure enterprise network, puts forward solution of DMVPN (Dynamic Multipoint VPN) technique to solve the problems that traditional VPN has not solved by now. At the same time, it expatiates on the implementation mechanism of DMVPN, puts forward a concrete case that to adopts the DMVPN technique constructs secure enterprise network of some universities and business chain organization, and network performance indexes are tested. From the results of the test, the DMVPN network entirely satisfies the actual requirements that an enterprise uses a network. It offers a mode that is a convenient and economical investment to an enterprise for building a secure network.

Design and Deployment of Secure Enterprise Network Framework

Design and Deployment of Secure Enterprise Network Framework

Knowledge management across multi-tier enterprises: the promise of intelligent software in the auto industry

The automotive industry is at a critical juncture in its evolution. Vehicle manufacturers are merging horizontally into large portfolio-oriented companies focused on assembly and marketing while reducing their in-house development and manufacturing depth in favor of a multi-tier supplier base. This realignment has increased organizational flexibility and global technology access, but concerns have been raised about the industry's ability to manage complex product programs across multi-tier interfaces — and about knowledge leaks to competitors. In this paper we argue that these virtually integrated enterprises are not well served by hierarchical planning tools and dedicated communications networks. Instead, distributed intelligence in an open-architecture and secure enterprise network provides the connectivity needed for successful product development and delivery. Emerging software tools such as intelligent agents or applets, common in Internet applications such as assisted personal computing, are well-suited for coordinating complex product management processes in multi-tier enterprises. As these new information technologies advance, competitive success will depend on the intertwined evolution of these embedded software tools and the new business processes that they are creating.