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A data base developed from the Memorial Institute for the Prevention of Terrorism’s (MIPT) Terrorism Knowledge Base for the years 1998–2005 was provided to participants in the workshop. The distribution of fatalities in terrorist attacks is, like many outcomes of human social and economic processes, heavily right-skewed. We propose an agent based model to analyse this, and to enable generalisations to be made from the historical data set. The model is inspired by modelling developments in cultural evolutionary theory. We argue that a more appropriate ‘null’ model of behaviour in the social sciences is on based upon the principle of copying, rather than the economic assumption of rationality in the standard social science model.

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially “viral” ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network’s community structure and core-periphery structure . This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political “memes” over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks.

Today’s security forces must operate in environments of increasing complexity, uncertainty and change, a fact that has led to increased stress levels along with the challenge to adapt. For many people, such stressful conditions can lead to a range of health problems and performance decrements. But others remain healthy, showing resilience under stress. What accounts for such resilience? This paper focuses on psychological hardiness, a set of mental qualities that has been found to distinguish resilient from non-resilient people. Those high in psychological hardiness show greater commitment – the abiding sense that life is meaningful and worth living; control – the belief that one chooses and can influence his/her own future; and challenge – a perspective on change in life as something that is interesting and exciting. This paper begins with a brief discussion of the major stress sources in modern military and security operations, and the broad range of factors that can influence resilience in organizations. Next the concept of psychological hardiness is described, including theoretical background, representative research findings, and biological underpinnings. Finally, some strategies are suggested for how psychological hardiness can be built up in organizations, primarily through leader actions and policies. By focusing more attention on increasing psychological hardiness, security organizations can realize enhanced health and performance in the workforce, while also preventing many stress-related problems.

Biological metaphors abound in computational modeling and simulation, inspiring creative and novel approaches to conceptualizing, representing, simulating and analyzing a wide range of phenomena. Proponents of this research suggest that biologically-inspired informatics have practical national security importance, because they represent a new way to analyze sociopolitical dynamics and trends, from terrorist recruitment to cyber warfare. However, translating innovative basic research into useful, usable, adoptable, and trustworthy tools that benefit the daily work of national security experts is challenging. Drawing on several years’ worth of ethnographic fieldwork among national security experts, this paper suggests that information ecology, activity theory, and participatory modeling provide theoretical frameworks and practical suggestions to support design and development of useful, usable, and adoptable modeling and simulation approaches for complex national security challenges.

Attacks on computers are increasingly sophisticated, automated and damaging. We take inspiration from the diversity and adaptation of the immune system to design a new kind of computer security system utilizing automated repair techniques. We call the principles of effective immune system design Scalable RADAR: Robust Adaptive Decentralized Search and Automated Response. This paper explores how node diversity is maintained on a network that can generate software variants at individual nodes and make local decisions about sharing variants between nodes. We explore the effects of different network topologies on software diversity and resource trade-offs. We examine how the architecture of the lymphatic network balances trade-offs between local and global search for pathogens in order to improve our design. Experiments are performed on model networks of connected computers able to automatically generate repairs to their own software in response to an attack, bug, or vulnerability. We find that increased connectivity leads to increased overhead, but decreased time to repair, and that small world networks more efficiently distribute repairs. Diversity is diminished by increased connectivity, but has a more complex relationship with network structure, for example, a highly connected network may exhibit low overall diversity but maintain high diversity in a small number of low degree nodes in the periphery of the network.

The collection and assessment of national security related information often involves an arduous process of detecting relevant associations between people, events, and locations—typically within very large data sets. The ability to more effectively perceive these connections could greatly aid in the process of knowledge discovery. This same process—pre-consciously collecting and associating multimodal information—naturally occurs in mammalian brains. With this in mind, this effort sought to draw upon the neuroscience community’s understanding of the relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism for the analyst community. Using the biology and functionality of the hippocampus as an analogy for inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations are formed. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a potential novel approach to data mining for intelligence-related knowledge discovery. This paper describes the neurobiology, architecture, and operational characteristics, as well as provides a simple intelligence-based example to illustrate the model’s functionality.

In this article, we analyze biological evolutionary systems to develop a framework for applying lessons of natural adaptability to security concerns in society. Biological systems do not waste resources attempting to predict future states of an inherently unpredictable and risk filled environment. Rather, biological organisms utilize adaptability to respond efficiently to a wide range of potential challenges, not just those that are known or anticipated. Adaptability is a powerful, but often misused concept. Typically, dimensionless claims about adaptability, such as, “insurgents are more adaptable than us” are made without clear benchmarks against which to measure adaptability. Our framework for adaptability, which was developed over the course of several multi-disciplinary working groups of life scientists and security practitioners focused on what we can learn about security from biological systems, can be applied broadly to societal approaches to improving security. Here we outline the “rules of engagement” for natural adaptable systems, which state that evolutionary systems do not predict, plan, or perfect the development of biological organisms. Given these constraints, we then outline four nearly universal features of adaptable biological organisms: 1. They are organized semi-autonomously with little central control 2. They learn from success 3. They use information to mitigate uncertainty 4. They extend their natural adaptability by engaging in a diverse range of symbiotic partnerships For each of these attributes we identify how they work in nature and how we have failed to apply them in our responses to security concerns. Finally, we describe a pathway by which adaptable strategies can be incorporated into security analysis, planning and implementation.