Determining cost-effective intrusion detection approaches for an advanced metering infrastructure deployment using advise

Utilities responsible for Advanced Metering Infrastructure (AMI) networks must be able to defend themselves from a variety of potential attacks so they may achieve the goals of delivering power to consumers and maintaining the integrity of their equipment and data. Intrusion detection systems (IDSes) can play an important part in the defense of such networks. Utilities should carefully consider the strengths and weaknesses of different IDS deployment strategies to choose the most cost-effective solution. Models of adversary behavior in the presence of different IDS deployments can help with making this decision as we demonstrate through a case study that uses a model created in the ADversary VIew Security Evaluation (ADVISE) formalism (which calculates metrics used to compare different IDSes). We show how these metrics give valuable insight into the selection of the appropriate IDS architecture for an AMI network

Addressing challenges to quantitative security modeling

Quantitative state-based models can help those responsible for designing, maintaining, or insuring cyber systems make informed decisions. However, there are a number of difficulties that discourage the use of quantitative cybersecurity models in practice. We identify four significant challenges to quantitative security modeling, (1) cybersecurity models are difficult to build by hand, particularly for system architects that are not experts in cybersecurity, (2) it is challenging to model the complex interplay between the cyber system and the many human entities that interact with it with current modeling formalisms, (3) the uncertainty that comes from the model’s input variables should be managed and explored with sensitivity analysis (SA) and uncertainty quantification (UQ), but many models run too slowly to complete traditional SA and UQ analyses, and (4) there is a lack of appropriate frameworks, guidance on metrics, and advice on common modeling issues with regards to quantitative cybersecurity models. In this dissertation, we address each of the four challenges. To address the first challenge, we present an ontology-assisted automatic cybersecurity model generation approach that modelers can use to make cybersecurity models quickly and easily. Using this approach, a system architect would first create a system diagram of the components of the system and their relationships to one another. Then, a model generation algorithm would convert the system diagram (with the aid of an ontology) into a sophisticated cybersecurity model that can be executed to obtain metrics. We implemented the tool in Mobius and demonstrated its use with an AMI test case. To address the second challenge, we designed a new agent-based modeling formalism called GAMES that allows the modeler to explicitly model the system and all of the human entities that interact with the system in a modular and intuitive fashion, and show its strengths with a worked example. To address the third challenge, we proposed an indirect stacking-based metamodeling approach. Using the metamodeling approach, we are able to accomplish sensitivity analysis and uncertainty quantification hundreds to thousands of times faster than traditional approaches and with better accuracy than current metamodel approaches. We demonstrate the approach’s efficacy with eight worked examples. Finally, to address the fourth challenge, we present a high-level framework to guide the modeling process, give guidance on what metrics to calculate and how to calculate them, and share advice on common issues with cybersecurity modeling. The theoretical and practical contributions presented in this dissertation will help make quantitative cybersecurity modeling easier to use and more useful, which will, in turn, help protect society’s most critical and valuable infrastructure from cyber threats

IRMA via SDN: Intrusion Response and Monitoring Appliance via Software-Defined Networking

Recent approaches to network intrusion prevention systems (NIPSs) use software-defined networking (SDN) to take advantage of dynamic network reconfigurability and programmability, but issues remain with system component modularity, network size scalability, and response latency. We present IRMA, a novel SDN-based NIPS for enterprise networks, as a network appliance that captures data traffic, checks for intrusions, issues alerts, and responds to alerts by automatically reconfiguring network flows via the SDN control plane. With a composable, modular, and parallelizable service design, we show improved throughput and less than 100 ms average latency between alert detection and response.Roy J. Carver FellowshipOpe

FUN3D Manual: 12.6

This manual describes the installation and execution of FUN3D version 12.6, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status

FUN3D Manual: 12.5

This manual describes the installation and execution of FUN3D version 12.5, including optional dependent packages. FUN3D is a suite of computational uid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables ecient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status

Detection of Novel SARS-like and Other Coronaviruses in Bats from Kenya

Diverse coronaviruses have been identified in bats from several continents but not from Africa. We identified group 1 and 2 coronaviruses in bats in Kenya, including SARS-related coronaviruses. The sequence diversity suggests that bats are well-established reservoirs for and likely sources of coronaviruses for many species, including humans

FUN3D Manual: 12.9

This manual describes the installation and execution of FUN3D version 12.9, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status

FUN3D Manual: 12.7

This manual describes the installation and execution of FUN3D version 12.7, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status

FUN3D Manual: 13.3

This manual describes the installation and execution of FUN3D version 13.3, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status

FUN3D Manual: 12.8

This manual describes the installation and execution of FUN3D version 12.8, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status