140 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

    Learning Concept Interestingness for Identifying Key Structures from Social Networks

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIdentifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.Fundamental Research Funds for the Central Universitie

    FACH: Fast algorithm for detecting cohesive hierarchies of communities in large networks

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    Vertices in a real-world social network can be grouped into densely connected communities that are sparsely connected to other groups. Moreover, these communities can be partitioned into successively more cohesive communities. Despite an ever-growing pile of research on hierarchical community detection, existing methods suffer from either inefficiency or inappropriate modeling. Yet, some cut-based approaches have shown to be effective in finding communities without hierarchies. In this paper, we study the hierarchical community detection problem in large networks and show that it is NP-hard. We then propose an efficient algorithm based on edge-cuts to identify the hierarchy of communities. Since communities at lower levels of the hierarchy are denser than the higher levels, we leverage a fast network sparsification technique to enhance the running time of the algorithm. We further propose a randomized approximation algorithm for information centrality of networks. We finally evaluate the performance of the proposed algorithms by conducting extensive experiments using real datasets. Our experimental results show that the proposed algorithms are promising and outperform the state-of-the-art algorithms by several orders of magnitude.This work is supported by the grant of Australian Research Council Discovery Project No. DP120102627

    Measuring and improving the readability of network visualizations

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    Network data structures have been used extensively for modeling entities and their ties across such diverse disciplines as Computer Science, Sociology, Bioinformatics, Urban Planning, and Archeology. Analyzing networks involves understanding the complex relationships between entities as well as any attributes, statistics, or groupings associated with them. The widely used node-link visualization excels at showing the topology, attributes, and groupings simultaneously. However, many existing node-link visualizations are difficult to extract meaning from because of (1) the inherent complexity of the relationships, (2) the number of items designers try to render in limited screen space, and (3) for every network there are many potential unintelligible or even misleading visualizations. Automated layout algorithms have helped, but frequently generate ineffective visualizations even when used by expert analysts. Past work, including my own described herein, have shown there can be vast improvements in network visualizations, but no one can yet produce readable and meaningful visualizations for all networks. Since there is no single way to visualize all networks effectively, in this dissertation I investigate three complimentary strategies. First, I introduce a technique called motif simplification that leverages the repeating patterns or motifs in a network to reduce visual complexity. I replace common, high-payoff motifs with easily understandable glyphs that require less screen space, can reveal otherwise hidden relationships, and improve user performance on many network analysis tasks. Next, I present new Group-in-a-Box layouts that subdivide large, dense networks using attribute- or topology-based groupings. These layouts take group membership into account to more clearly show the ties within groups as well as the aggregate relationships between groups. Finally, I develop a set of readability metrics to measure visualization effectiveness and localize areas needing improvement. I detail optimization recommendations for specific user tasks, in addition to leveraging the readability metrics in a user-assisted layout optimization technique. This dissertation contributes an understanding of why some node-link visualizations are difficult to read, what measures of readability could help guide designers and users, and several promising strategies for improving readability which demonstrate that progress is possible. This work also opens several avenues of research, both technical and in user education

    A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

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    Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy

    Influence Analysis towards Big Social Data

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    Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation
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