Yet another cybersecurity risk assessment framework

IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.

     

ENROUTE: An Entropy Aware Routing Scheme for Information-Centric Networks (ICN)

With the exponential growth of end users and web data, the internet is undergoing the change of paradigm from a user-centric model to a content-centric one, popularly known as information-centric networks (ICN). Current ICN research evolves around three key-issues namely (i) content request searching, (ii) content routing, and (iii) in-network caching scheme to deliver the requested content to the end user. This would improve the user experience to obtain requested content because it lowers the download delay and provides higher throughput. Existing researches have mainly focused on on-path congestion or expected delivery time of a content to determine the optimized path towards custodian. However, it ignores the cumulative effect of the link-state parameters and the state of the cache, and consequently it leads to degrade the delay performance. In order to overcome this shortfall, we consider both the congestion of a link and the state of on-path caches to determine the best possible routes. We introduce a generic term entropy to quantify the effects of link congestion and state of on-path caches. Thereafter, we develop a novel entropy dependent algorithm namely ENROUTE for searching of content request triggered by any user, routing of this content, and caching for the delivery this requested content to the user. The entropy value of an intra-domain node indicates how many popular contents are already cached in the node, which, in turn, signifies the degree of enrichment of that node with the popular contents. On the other hand, the entropy for a link indicates how much the link is congested with the traversal of contents. In order to have reduced delay, we enhance the entropy of caches in nodes, and also use path with low entropy for downloading contents. We evaluate the performance of our proposed ENROUTE algorithm against state-of-the-art schemes for various network parameters and observe an improvement of 29–52% in delay, 12–39% in hit rate, and 4–39% in throughput.

     

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Journal of Intelligent Manufacturing - In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size,...     

Self-consistent non-linear physics based predictive model for the computation of THz-signal attenuation in fog with varying visibility in tropical climatic zone

Microsystem Technologies - Absorptive and dispersive properties of atmospheric hydrometeors, affect the propagation of millimetre-wave and submillimeter wave signals. In adverse weather condition,...     

A Comparative Investigation of Optical and Structural Properties of Cu-Doped CdO-Derived Nanostructures

In this report, we studied various structural and optical properties of pure and copper-doped cadmium oxide (CdO) thin films. Nanostructured Cu-doped CdO films were deposited using sol–gel spin-coating technique. The structural and morphological changes have been observed by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and atomic force microscopy (AFM) studies. The optical and electrical properties of the pure and Cu-doped CdO thin films were studied by UV–vis spectroscopy and four-point probe method, respectively. The XRD peaks show the formation of nanocrystalline CdO with cubic face-centered crystal structure. The band gaps of the as deposited films were found in the range of 2.32–2.73 eV, while after doping, it decreases due to structural deformation. The electrical resitivity was found to decrease approximately ～10 in Cu-doped CdO thin films.     

Optimizing spatiotemporal sensors placement for nutrient monitoring: a stochastic optimization framework

Nutrient monitoring is very important for the area of food–energy–water nexus. The sensor network for nutrient monitoring requires dynamic sensing where the positions of the sensors change with time. In this work, we have proposed a methodology to optimize a dynamic sensor network which can address the spatiotemporal aspect of nutrient movement in a watershed. This is a first paper in the series where an algorithmic and methodological framework for spatiotemporal sensor placement problem is proposed. Dynamic sensing is widely used in wireless sensors, and the current approaches to solving this problem are data intensive. This is the first time we are introducing a stochastic optimization approach to dynamic sensing which is efficient. This framework is based on a novel stochastic optimization algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). A small case study of the dynamic sensor placement problem is presented to illustrate the approach. In the second paper of this series, we will present a detailed case study of nutrient monitoring in a watershed.     

Optimal design of a distribution-free quality control scheme for cost-efficient monitoring of unknown location

Traditionally, a cost-efficient control chart for monitoring product quality characteristic is designed using prior knowledge regarding the process distribution. In practice, however, the functional form of the underlying process distribution is rarely known a priori. Therefore, the nonparametric (distribution-free) charts have gained more attention in the recent years. These nonparametric schemes are statistically designed either with a fixed in-control average run length or a fixed false alarm rate. Robust and cost-efficient designs of nonparametric control charts especially when the true process location parameter is unknown are not adequately addressed in literature. For this purpose, we develop an economically designed nonparametric control chart for monitoring unknown location parameter. This work is based on the Wilcoxon rank sum (hereafter WRS) statistic. Some exact and approximate procedures for evaluation of the optimal design parameters are extensively discussed. Simulation results show that overall performance of the exact procedure based on bootstrapping is highly encouraging and robust for various continuous distributions. An approximate and simplified procedure may be used in some situations. We offer some illustration and concluding remarks.     

The privileged sensing framework: A principled approach to improved human-autonomy integration

ABSTRACT

A primary goal for human-autonomy integration (HAI) is to balance the strengths of human and autonomy in order to achieve performance objectives more efficiently and robustly than either the human or autonomous agents would independently. This paper proposes the Privileged Sensing Framework (PSF) as a novel approach to HAI. This approach is based on the concept of dynamically ‘privileging’ information during the process of integration by dynamically bestowing special rights based on the characteristics of each individual agent, the task context, and the performance goals. The proposed framework is tested through a series of simulation experiments that provide a clear demonstration of increased accuracy and throughput of human-autonomy performance. These proof-of-concept simulations provide initial evidence of the utility of the PSF. Continued development of this approach has the potential to revolutionise capabilities of multi-agent cooperative teams across a broad range of applications.     

Leveraging temporal and spatial separations with the 24-hour knowledge factory paradigm

The 24-H Knowledge Factory facilitates collaboration between geographically and temporally distributed teams. The teams themselves form a strategic partnership whose joint efforts contribute to the completion of a project. Project-related tasks are likewise distributed, allowing tasks to be completed on a continuous basis, regardless of the constraints of any one team’s working hours. However, distributing a single task between multiple teams necessitates a handoff process, where one team’s development efforts and task planning are communicated from one team ending their shift to the next that will continue the effort. Data management is, therefore, critical to the success of this business model. Efficiency in data management is achieved through a strategic leveraging of key tools, models, and concepts.