An Application of Social Network Analysis on Military Strategy, System Networks and the Phases of War

The research developed in this study will utilize Social Network and Graph Theory terminology and methodology applied to groups of systems, rather than individuals within a given system, in order to shape strategic level goals. With regard to military operations, Social Network Analysis has been used to show that enemy networks and relationships can be accurately represented using weighted layers with weighted relationships in order to identify the key player(s) that must be influenced and/or removed so that a particular effect on the enemy might be realized. Social Network Analysis is therefore a significant tool concerning tactical level of operations that aids in developing a targeting methodology which aids tactical commanders in mission planning, however has never been applied to strategic levels of Command. Like previous key player problems, this research will utilize system attributes and global relational strengths as inputs. The output results will rank order representative systems of interest that satisfy the constraints and desired objectives within a particular Phase of War. This work will apply and extend the tools of Social Network Analysis structure and techniques to a theater level mission

A Dynamic Game on Network Topology for Counterinsurgency Applications

Successful military operations are increasingly reliant upon an advanced understanding of relevant networks and their topologies. The methodologies of network science are uniquely suited to inform senior military commanders; however, there is a lack of research in the application of these methods in a realistic military scenario. This study creates a dynamic game on network topology to provide insight into the effectiveness of offensive targeting strategies determined by various centrality measures given limited states of information and varying network topologies. Improved modeling of complex social behaviors is accomplished through incorporation of a distance-based utility function. Moreover, insights into effective defensive strategies are gained through incorporation of a hybrid model of network regeneration. Model functions and parameters are thoroughly presented, followed by a detailed sensitivity analysis of factors. Two designed experiments fully investigate the significance of factor main effects and two-factor interactions. Results show select targeting criteria utilizing uncorrelated network measures are found to outperform others given varying network topologies and defensive regeneration methods. Furthermore, the attacker state of information is only significant given certain defending network topologies. The costs of direct relationships significantly impact optimal methods of regeneration, whereas restructuring methods are insignificant. Model applications are presented and discussed

Screening Heuristics for the Evaluation of Covert Network Node Insertion Scenarios

The majority of research on covert networks uses social network analysis (SNA) to determine critical members of the network to either kill or capture for the purpose of network destabilization. This thesis takes the opposite approach and evaluates potential scenarios for inserting an agent into a covert network for information gathering purposes or future disruption operations. Due to the substantial number of potential insertion scenarios in a large network, this research proposes three screening heuristics that leverage SNA measures to reduce the solution space before applying a simple search heuristic

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A Risk Based Approach to Node Insertion within Social Networks

Social Network Analysis (SNA) is a primary tool for counter-terrorism operations, ranging from resiliency and influence to interdiction on threats stemming from illicit overt and clandestine network operations. In an ideal world, SNA would provide a perfect course of action to eliminate dangerous situations that terrorist organizations bring. Unfortunately, the covert nature of terrorist networks makes the effects of these techniques unknown and possibly detrimental. To avoid potentially harmful changes to enemy networks, tactical involvement must evolve, beginning with the intelligent use of network in filtration through the application of the node insertion problem. The framework for the node insertion problem includes a risk-benefit model to assess the utility of various node insertion scenarios. This model incorporates local, intermediate and global SNA measures, such as Laplacian centrality and assortative mixing, to account for the benefit and risk. Application of the model to the Zachary Karate Club produces a set of recommended insertion scenarios. A designed experiment validates the robustness of the methodology against network structure and characteristics. Ultimately, the research provides an SNA method to identify optimal and near-optimal node insertion strategies and extend past node utility models into a general form with the inclusion of benefit, risk, and bias functions

Analysis of a Voting Method for Ranking Network Centrality Measures on a Node-aligned Multiplex Network

Identifying relevant actors using information gleaned from multiple networks is a key goal within the context of human aspects of military operations. The application of a voting theory methodology for determining nodes of critical importance—in ranked order of importance—for a node-aligned multiplex network is demonstrated. Both statistical and qualitative analyses on the differences of ranking outcomes under this methodology is provided. As a corollary, a multilayer network reduction algorithm is investigated within the context of the proposed ranking methodology. The application of the methodology detailed in this thesis will allow meaningful rankings of relevant actors to be produced on a multiplex network

Medical cyber-physical systems: A survey

Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included

A Statistical Approach to Characterize and Detect Degradation Within the Barabasi-Albert Network

Social Network Analysis (SNA) is widely used by the intelligence community when analyzing the relationships between individuals within groups of interest. Hence, any tools that can be quantitatively shown to help improve the analyses are advantageous for the intelligence community. To date, there have been no methods developed to characterize a real world network as a Barabasi-Albert network which is a type of network with properties contained in many real-world networks. In this research, two newly developed statistical tests using the degree distribution and the L-moments of the degree distribution are proposed with application to classifying networks and detecting degradation within a network. The feasibility of these tests is shown by using the degree distribution for network and sub-network characterization of a selected scale-free real world networks. Further, sensitivity to the level of network degradation, via edge or node deletion, is examined with recommendation made as to the detectable size of degradation achievable by the statistical tests. Finally, the degree distribution of simulated Barabasi-Albert networks is investigated and results demonstrate that the theoretical distribution derived previously in the literature is not applicable to all network sizes. These results provide a foundation on which a statistically driven approach for network characterization can be built for network classification and monitoring

Statistical L-moment and L-moment Ratio Estimation and their Applicability in Network Analysis

This research centers on finding the statistical moments, network measures, and statistical tests that are most sensitive to various node degradations for the Barabási-Albert, Erdös-Rényi, and Watts-Strogratz network models. Thirty-five different graph structures were simulated for each of the random graph generation algorithms, and sensitivity analysis was undertaken on three different network measures: degree, betweenness, and closeness. In an effort to find the statistical moments that are the most sensitive to degradation within each network, four traditional moments: mean, variance, skewness, and kurtosis as well as three non-traditional moments: L-variance, L-skewness, and L-kurtosis were examined. Each of these moments were examined across 18 degrade settings to highlight which moments were able to detect node degradation the quickest. Closeness and the mean were the most sensitive measures to node degradation across all scenarios. The results showed L-moments and L-moment ratios were less sensitive than traditional moments. Subsequently sample size guidance and confidence interval estimation for univariate and joint L-moments were derived across many common statistical distributions for future research with L-moments