Sherwin, M., Brown, K. J., Medal, H., and Mackenzie, C. (2017) An optimized resource allocation approach to identify and mitigate supply chain risks using fault-tree analysis. Technical report, Mississippi State University.
An infrastructure system usually contains a number of inter-connected infrastructure links that connect users to services or products. Where to locate these infrastructure links is a challenging problem that largely determines the efficiency and quality of the network. This paper studies a new location design problem that aims to maximize the total weighted benefits between users and multiple services that are measured by the amount of connectivity between users and links in the network. This problem is investigated from both analytical and computational points of view. First, analytical properties of special cases of the problem are described. Next, two integer programming model formulations are presented for the general problem. We also test intuitive heuristics including greedy and interchange algorithms, and find that the interchange algorithm efficiently yields near-optimum solutions. Finally, a set of numerical examples demonstrate the proposed models and reveal interesting managerial insights. In particular, we found that a more distance-dependent utility measure and a higher concentration of users help achieve a better total utility. As the population becomes increasingly concentrated, the optimal link design evolves from a linear path to a cluster of links around the population center. As the budget level increases, the installed links gradually sprawl from the population center towards the periphery, and in the case of multiple population centers, they grow and eventually merge into one connected component.
We present an approach for measuring the vulnerability of a wireless network. Our metric, n-Robustness, measures the change in a network’s total signal strength resulting from the optimal placement of n jammers by an attacker. Toward this end, we develop a multi-period mixed-integer programming interdiction model that determines the movement of n jammers over a time horizon so as to minimize the total signal strength of users during a sustained jamming attack. We compared several solution approaches for solving our model including a Lagrangian relaxation heuristic, a genetic algorithm, and a stage decomposition heuristic. We tested our approach on a wireless trace dataset developed as part of the Wireless Topology Discovery project at the University of California San Diego. We found that the Lagrangian approach, which performed best overall, finds a close-to-optimal solution while requiring much less time than solving the MIP directly. We then illustrate the behavior of our model on a small example taken from the dataset as well as a set of experiments. Through our experiments we conclude that the total signal power follows a sigmoid curve as we increase the number of jammers and access points. We also found that increasing access points only improves network robustness initially; after that the benefit levels off. In addition, we found that the problem instances we considered have an n-Robustness of between 39 and 69%, indicating that the value of the model parameters (e.g., number of jammers, number of time periods) has an effect on robustness.
In this research, we study the vulnerability of landscapes to wildfires based on the impact of the worst-case scenario ignition locations. Using this scenario, we model wildfires that cause the largest damage to a landscape over a given time horizon. The landscape is modeled as a grid network, and the spread of wildfire is modeled using the minimum travel time model. To assess the impact of a wildfire in the worst-case scenario, we develop a mathematical programming model to optimally locate the ignition points so that the resulting wildfire results in the maximum damage. We compare the impacts of the worst-case wildfires (with optimally located ignition points) with the impacts of wildfires with randomly located ignition points on three landscape test cases clipped out from three national forests located in the western U.S. Our results indicate that the worst-case wildfires, on average, have more than twice the impact on landscapes than wildfires with randomly located ignition points.
Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-by-day, and most of the existing rule based and flow based detection methods may not be capable of detecting bot activities in an efficient and effective manner. Hence, designing a robust and fast botnet detection method is of high significance. In this study, we propose a novel botnet detection methodology based on topological features of nodes within a graph: in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality. A self-organizing map clustering method is applied to establish clusters of nodes in the network based on these features. Our method is capable of isolating bots in clusters of small sizes while containing the majority of normal nodes in the same big cluster. Thus, bots can be detected by searching a limited number of nodes. A filtering procedure is also developed to further enhance the algorithm efficiency by removing inactive nodes from consideration. The methodology is verified using the CTU-13 datasets, and benchmarked against a classification-based detection method. The results show that our proposed method can efficiently detect the bots despite their varying behaviors.
Defense against jamming attacks has been an increasing concern for the military and disaster response authorities. The military uses jamming attacks as a tool to attack and disrupt terrorist׳s communications, because the open nature of wireless networks makes them vulnerable to various attacks. Many studies and a few survey papers are available in the literature, but none of these papers classify the attacks or the defense strategies by the type of wireless network affected, the attacker or defender׳s perspective, the type of game used to model the problem, such as Bayesian game, Stackelberg game, or the type of solution methodology, such as mathematical programming model and algorithm. This paper provides a comprehensive survey and a taxonomic classification to help interested researchers find the gaps in the literature and guide them to research areas that need to be explored.
In this paper we use a well-accepted methodology, fault-tree analysis, to identify delay risks and proactively propose a cost-effective mitigation strategy within a low volume high value supply chain. The basis for the assessment is the bill of materials of the product being studied. The top-level event of interest represents the delay in delivering a product to a customer and lower-level events represent the probabilities associated with delays caused by quality and capability deficiencies within the supply chain of the product being studied. Supply chain risk mitigation strategies have been well documented in academic literature. However, much of what has been documented addresses such topics as facility location, inventory buffers, and is generally focused on response strategies once the risk has been realized. This paper presents a robust method to reduce the likelihood of delays in material flow by representing the system of suppliers within a supply chain as a fault-tree and proactively determining the optimum mitigation strategy for the portfolio. The approach is illustrated via real-world numerical scenarios based on hypothetical data sets and the results are presented.
We study the effect that installing sidewalks and crosswalks, as traffic calming facilities, has on the safety and usability of a transportation network with automobile, public transit and walking as modes of transportation. A mathematical programming model is proposed for this problem whose objective is to minimize the safety hazard for pedestrians and the total transportation cost of the network. We utilize a customized greedy heuristic and a simulated annealing algorithm for solving the problem. The computational results indicate that installing sidewalks and crosswalks at proper locations can reduce the overall transportation cost and improve pedestrians’ safety.