Game Theory Model for Intrusion Prevention in Computer Networks
No system is completely secure, but it’s possible to analyze system vulnerabilities, take security measures and thus reduce the likelihood of system intrusions. One tool to reduce this probability of intrusion is to implement intrusion prevention systems, which analyze network traffic to determine anomalous behavior and take security measures with the agents that exhibit these behaviors. This document proposes a game theory model based in an evaluation of works related in the area that could be the basis of an algorithm for the prevention of intrusions in networks.
A Study of Dynamic Phase Adaptation Using a Dynamic Multicore Processor
Heterogeneous processors such as ARM’s big.LITTLE have become popular for embedded systems. They offer a choice between running workloads on a high performance core or a low-energy core leading to increased energy efficiency. However, the core configurations are fixed at design time which offers a limited amount of adaptation. Dynamic Multicore Processors (DMPs) bridge the gap between homogeneous and fully reconfigurable systems. Cores can fuse dynamically to adapt the computational resources to the needs of different workloads. There exists multiple examples of DMPs in the literature, yet the focus has mainly been on static partitioning. This paper conducts the first thorough study of the potential for dynamic reconfiguration of DMPs at runtime. We study how performance varies with static partitioning and what software optimizations are required to achieve high performance. We show that energy consumption is reduced considerably when adapting the number of cores to program phases, and introduce a simple online model which predicts the optimal number of cores to use to minimize energy consumption while maintaining high performance. Using the San Diego Vision Benchmark Suite as a use case, the dynamic scheme leads to ∼40% energy savings on average without decreasing performance.
An automated retinal image quality grading algorithm
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.