Hosting virtual network function (VNF) on the edge devices in modern IoT networks significantly reduces the number of appliance hardware needed. It also expedites significant routing and load-balancing processes and reduces power consumption and maintenance costs for an extensive network. Therefore, placing VNFs on edge devices with efficient resource utilization to serve the growing number of IoT devices is an important research question for the network community. This research proposes a many-to-many VNF placement model with fairness built in. One user (IoT device) can access multiple VNFs, and many VNFs can be hosted into a single host (edge device). We give an optimization model and valid inequalities for the optimal placement of VNFs to minimize the total latency. A branch and cut algorithm is also developed here for solving the model optimally. We evaluate the model with synthetic data for a many-to-many and one-to-one mapping of VNFs and hosts. A detailed simulation shows that the proposed algorithm(s) can produce an optimal and fair placement in near real-time on realistically sized instances.

Efficient propagation of data across mobile nodes is essential in wireless networks. A minimum connected dominating set (MCDS) of nodes is typically used to reduce redundant transmission in broadcasts. If a group of nodes wants to transmit over a shared channel simultaneously, then contention occurs. Contending nodes then defer transmissions for a random time. A contention aware connected dominating set (CACDS) that minimizes transmission conflict is therefore essential. We study integer programming formulations computationally for MCDS and CACDS. We use Benders decomposition to solve them and propose a new method to compute Bender’s feasibility cut based on the number of connected components.We evaluate the state-of-art approach computationally for MCDS and CACDS based on the shortest paths with our approach. The detailed experiments show that the new method takes less time and minimizes contention better in large networks.

With the rapid advances in computing and information technologies, traditional access control models have become inadequate in terms of capturing fine-grained, and expressive security requirements of newly emerging applications. An attribute-based access control (ABAC) model provides a more flexible approach to addressing the authorization needs of complex and dynamic systems. While organizations are interested in employing newer authorization models, migrating to such models pose as a significant challenge. Many large-scale businesses need to grant authorizations to their user populations that are potentially distributed across disparate and heterogeneous computing environments. Each of these computing environments may have its own access control model. The manual development of a single policy framework for an entire organization is tedious, costly, and error-prone. In this article, we present a methodology for automatically learning ABAC policy rules from access logs of a system to simplify the policy development process. The proposed approach employs an unsupervised learning-based algorithm for detecting patterns in access logs and extracting ABAC authorization rules from these patterns. In addition, we present two policy improvement algorithms, including rule pruning and policy refinement algorithms to generate a higher quality mined policy. Finally, we implement a prototype of the proposed approach to demonstrate its feasibility.

An Attribute-Based Access Control (ABAC) model provides a flexible and promising approach for large, dynamic systems/applications and helps overcome the limitations of other prevalent AC approaches. However, the cost of migrating to an ABAC based system is a significant obstacle for organizations. Many large enterprises/applications need to grant access privileges to a huge number of users distributed across disparate computing environments and applications including legacy systems. Each of these applications may have its own access control model. Manual development of a single access control policy through a set of attribute-based policy rules is expensive and time consuming. In this paper, we present a methodology for automatically learning ABAC policy rules from access logs in a system to facilitate the AC policy development process. The proposed approach uses an unsupervised learning-based technique for detecting patterns in a set of access records and extracting ABAC policy rules from these patterns. We present two algorithms, rule pruning, and policy refinement, to improve the quality of the mined policy. Policy refinement algorithms are useful in ABAC policy maintenance, as well. We evaluate our proposed approach on three different sample policies as well as a randomly synthesized policy to show its effectiveness.