24 research outputs found

    Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion

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    With the burgeoning advancements of computing and network communication technologies, network infrastructures and their application environments have become increasingly complex. Due to the increased complexity, networks are more prone to hardware faults and highly susceptible to cyber-attacks. Therefore, for rapidly growing network-centric applications, network resilience is essential to minimize the impact of attacks and to ensure that the network provides an acceptable level of services during attacks, faults or disruptions. In this regard, this thesis focuses on developing effective approaches for enhancing network resilience. Existing approaches for enhancing network resilience emphasize on determining bottleneck nodes and edges in the network and designing proactive responses to safeguard the network against attacks. However, existing solutions generally consider broader application domains and possess limited applicability when applied to specific application areas such as cyber defense and information diffusion, which are highly popular application domains among cyber attackers. These solutions often prioritize general security measures and may not be able to address the complex targeted cyberattacks [147, 149]. Cyber defense and information diffusion application domains usually consist of sensitive networks that attackers target to gain unauthorized access, potentially causing significant financial and reputational loss. This thesis aims to design effective, efficient and scalable techniques for discovering bottleneck nodes and edges in the network to enhance network resilience in cyber defense and information diffusion application domains. We first investigate a cyber defense graph optimization problem, i.e., hardening active directory systems by discovering bottleneck edges in the network. We then study the problem of identifying bottleneck structural hole spanner nodes, which are crucial for information diffusion in the network. We transform both problems into graph-combinatorial optimization problems and design machine learning based approaches for discovering bottleneck points vital for enhancing network resilience. This thesis makes the following four contributions. We first study defending active directories by discovering bottleneck edges in the network and make the following two contributions. (1) To defend active directories by discovering and blocking bottleneck edges in the graphs, we first prove that deriving an optimal defensive policy is #P-hard. We design a kernelization technique that reduces the active directory graph to a much smaller condensed graph. We propose an effective edge-blocking defensive policy by combining neural network-based dynamic program and evolutionary diversity optimization to defend active directory graphs. The key idea is to accurately train the attacking policy to obtain an effective defensive policy. The experimental evaluations on synthetic AD attack graphs demonstrate that our defensive policy generates effective defense. (2) To harden large-scale active directory graphs, we propose reinforcement learning based policy that uses evolutionary diversity optimization to generate edge-blocking defensive plans. The main idea is to train the attacker’s policy on multiple independent defensive plan environments simultaneously so as to obtain effective defensive policy. The experimental results on synthetic AD graphs show that the proposed defensive policy is highly effective, scales better and generates better defensive plans than our previously proposed neural network-based dynamic program and evolutionary diversity optimization approach. We then investigate discovering bottleneck structural hole spanner nodes in the network and make the following two contributions. (3) To discover bottleneck structural hole spanner nodes in large-scale and diverse networks, we propose two graph neural network models, GraphSHS and Meta-GraphSHS. The main idea is to transform the SHS identification problem into a learning problem and use the graph neural network models to learn the bottleneck nodes. Besides, the Meta-GraphSHS model learns generalizable knowledge from diverse training graphs to create a customized model that can be fine-tuned to discover SHSs in new unseen diverse graphs. Our experimental results show that the proposed models are highly effective and efficient. (4) To identify bottleneck structural hole spanner nodes in dynamic networks, we propose a decremental algorithm and graph neural network model. The key idea of our proposed algorithm is to reduce the re-computations by identifying affected nodes due to updates in the network and performing re-computations for affected nodes only. Our graph neural network model considers the dynamic network as a series of snapshots and learns to discover SHS nodes in these snapshots. Our experiments demonstrate that the proposed approaches achieve significant speedup over re-computations for dynamic graphs.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

    Frontiers in psychodynamic neuroscience

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    he term psychodynamics was introduced in 1874 by Ernst von Brücke, the renowned German physiologist and Freud’s research supervisor at the University of Vienna. Together with Helmholtz and others, Brücke proposed that all living organisms are energy systems, regulated by the same thermodynamic laws. Since Freud was a student of Brücke and a deep admirer of Helmholtz, he adopted this view, thus laying the foundations for his metapsychology. The discovery of the Default Network and the birth of Neuropsychoanalysis, twenty years ago, facilitated a deep return to this classical conception of the brain as an energy system, and therefore a return to Freud's early ambition to establish psychology as natural science. Our current investigations of neural networks and applications of the Free Energy Principle are equally ‘psychodynamic’ in Brücke’s original sense of the term. Some branches of contemporary neuroscience still eschew subjective data and therefore exclude the brain’s most remarkable property – its selfhood – from the field, and many neuroscientists remain skeptical about psychoanalytic methods, theories, and concepts. Likewise, some psychoanalysts continue to reject any consideration of the structure and functions of the brain from their conceptualization of the mind in health and disease. Both cases seem to perpetuate a Cartesian attitude in which the mind is linked to the brain in some equivocal relationship and an attitude that detaches the brain from the body -- rather than considering it an integral part of the complex and dynamic living organism as a whole. Evidence from psychodynamic neuroscience suggests that Freudian constructs can now be realized neurobiologically. For example, Freud’s notion of primary and secondary processes is consistent with the hierarchical organization of self-organized cortical and subcortical systems, and his description of the ego is consistent with the functions of the Default Network and its reciprocal exchanges with subordinate brain systems. Moreover, thanks to new methods of measuring brain entropy, we can now operationalize the primary and secondary processes and therefore test predictions arising from these Freudian constructs. All of this makes it possible to deepen the dialogue between neuroscience and psychoanalysis, in ways and to a degree that was unimaginable in Freud's time, and even compared to twenty years ago. Many psychoanalytical hypotheses are now well integrated with contemporary neuroscience. Other Freudian and post-Freudian hypotheses about the structure and function of the mind seem ripe for the detailed and sophisticated development that modern psychodynamic neuroscience can offer. This Research Topic aims to provide comprehensive coverage of the latest advances in psychodynamic neuroscience and neuropsychoanalysis. Potential authors are invited to submit papers (original research, case reports, review articles, commentaries) that deploy, review, compare or develop the methods and theories of psychodynamic neuroscience and neuropsychoanalysis. Potential authors include researchers, psychoanalysts, and neuroscientists

    Coronavirus disease (Covid-19): psychoeducational variables involved in the health emergency

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    This monograph has allowed us to present a psychoeducational view of the effects of the COVID-19 pandemic. We confirm here that research in education contributes its own evidence and specific models for identifying this problem

    African Border Disorders

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    Since the end of the Cold War, the monopoly of legitimate organized force of many African states has been eroded by a mix of rebel groups, violent extremist organizations, and self-defence militias created in response to the rise in organized violence on the continent. African Border Disorders explores the complex relationships that bind states, transnational rebels and extremist organizations, and borders on the African continent. Combining cutting edge network science with geographical analysis, the first part of the book highlights how the fluid alliances and conflicts between rebels, violent extremist organizations and states shape in large measure regional patterns of violence in Africa. The second part of the book examines the spread of Islamist violence around Lake Chad through the lens of the violent Nigerian Islamist group Boko Haram, which has evolved from a nationally-oriented militia group, to an internationally networked organization. The third part of the book explores how violent extremist organizations conceptualize state boundaries and territory and, reciprocally, how do the civil society and the state respond to the rise of transnational organizations. The book will be essential reading for all students and specialists of African politics and security studies, particularly those specializing on fragile states, sovereignty, new wars, and borders as well as governments and international organizations involved in conflict prevention and early intervention in the region

    Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

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    The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969

    Minimizing and Computing the Inverse Geodesic Length on Trees

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    For any fixed measure H that maps graphs to real numbers, the MinH problem is defined as follows: given a graph G, an integer k, and a target tau, is there a set S of k vertices that can be deleted, so that H(G - S) is at most tau? In this paper, we consider the MinH problem on trees. We call H balanced on trees if, whenever G is a tree, there is an optimal choice of S such that the components of G - S have sizes bounded by a polynomial in n / k. We show that MinH on trees is Fixed-Parameter Tractable (FPT) for parameter n / k, and furthermore, can be solved in subexponential time, and polynomial space, whenever H is additive, balanced on trees, and computable in polynomial time. A particular measure of interest is the Inverse Geodesic Length (IGL), which is used to gauge the efficiency and connectedness of a graph. It is defined as the sum of inverse distances between every two vertices: IGL(G) = sum_{{u,v} subseteq V} 1/d_G(u,v). While MinIGL is W[1]-hard for parameter treewidth, and cannot be solved in 2^{o(k + n + m)} time, even on bipartite graphs with n vertices and m edges, the complexity status of the problem remains open in the case where G is a tree. We show that IGL is balanced on trees, to give a 2^O((n log n)^(5/6)) time, polynomial space algorithm. The distance distribution of G is the sequence {a_i} describing the number of vertex pairs distance i apart in G: a_i = |{{u, v}: d_G(u, v) = i}|. Given only the distance distribution, one can easily determine graph parameters such as diameter, Wiener index, and particularly, the IGL. We show that the distance distribution of a tree can be computed in O(n log^2 n) time by reduction to polynomial multiplication. We also extend the result to graphs with small treewidth by showing that the first p values of the distance distribution can be computed in 2^(O(tw(G))) n^(1 + epsilon) sqrt(p) time, and the entire distance distribution can be computed in 2^(O(tw(G))) n^{1 + epsilon} time, when the diameter of G is O(n^epsilon\u27) for every epsilon\u27 > 0

    Proceedings of the 21st International Congress of Aesthetics, Possible Worlds of Contemporary Aesthetics Aesthetics Between History, Geography and Media

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    The Faculty of Architecture, University of Belgrade and the Society for Aesthetics of Architecture and Visual Arts of Serbia (DEAVUS) are proud to be able to organize the 21st ICA Congress on “Possible Worlds of Contemporary Aesthetics: Aesthetics Between History, Geography and Media”. We are proud to announce that we received over 500 submissions from 56 countries, which makes this Congress the greatest gathering of aestheticians in this region in the last 40 years. The ICA 2019 Belgrade aims to map out contemporary aesthetics practices in a vivid dialogue of aestheticians, philosophers, art theorists, architecture theorists, culture theorists, media theorists, artists, media entrepreneurs, architects, cultural activists and researchers in the fields of humanities and social sciences. More precisely, the goal is to map the possible worlds of contemporary aesthetics in Europe, Asia, North and South America, Africa and Australia. The idea is to show, interpret and map the unity and diverseness in aesthetic thought, expression, research, and philosophies on our shared planet. Our goal is to promote a dialogue concerning aesthetics in those parts of the world that have not been involved with the work of the International Association for Aesthetics to this day. Global dialogue, understanding and cooperation are what we aim to achieve. That said, the 21st ICA is the first Congress to highlight the aesthetic issues of marginalised regions that have not been fully involved in the work of the IAA. This will be accomplished, among others, via thematic round tables discussing contemporary aesthetics in East Africa and South America. Today, aesthetics is recognized as an important philosophical, theoretical and even scientific discipline that aims at interpreting the complexity of phenomena in our contemporary world. People rather talk about possible worlds or possible aesthetic regimes rather than a unique and consistent philosophical, scientific or theoretical discipline

    Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

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    Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018
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