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Adaptive Policy-Oriented Cybersecurity: A Decentralized Framework Using message passing algorithms for Dynamic Threat Mitigation

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IEEE ACCESS 2025年 13卷 65216-65231页

作者： Evangelatos, Spyridon Veroni, Eleni Konidi, Mariza Karagiorgou, Sophia Dede, Georgia Baklezos, Anargyros Nikolopoulos, Christos Sarigiannidis, Panagiotis Goudos, Sotirios K. Netco Intrasoft SA Res & Innovat Dev Dept Luxembourg 71410 Luxembourg Hellen Mediterranean Univ Dept Elect Engn Khania 73133 Greece Ubitech Ltd Data & AI ML Syst DAI Res Grp CY-15231 Limassol Cyprus Harokopio Univ Athens Dept Informat & Telemat Athens 17676 Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani 50150 Greece Aristotle Univ Thessaloniki Dept Phys ELEDIA AUTH Thessaloniki 54124 Greece

In the face of increasingly sophisticated and dynamic cyber threats, policy-based cybersecurity deployment has emerged as a critical strategy for securing modern, complex network infrastructures. This approach enables organizations to enforce adaptable, scalable and context-aware security policies that can dynamically respond to evolving threats and operational demands. However, the growing complexity of distributed systems and the interdependencies between diverse security components present significant challenges in ensuring consistent and efficient policy enforcement. Static and centralized security models are no longer sufficient to address these challenges, underscoring the need for intelligent, distributed solutions that can optimize policy deployment in real-time. This paper introduces a novel framework based on message passing algorithms that enables decentralized, adapted and optimized policy deployment across complex cybersecurity infrastructures. More specifically, we extend the traditional Min-Sum algorithm by incorporating dynamic trust weights that influence policy enforcement decisions, allowing more reliable nodes to have a greater impact while mitigating the risks posed by untrusted entities. The proposed framework is designed to handle large-scale network environments with minimal computational overhead, relying on localized message exchanges instead of centralized computations. We benchmark our framework against state-of-the-art approaches, demonstrating that the proposed framework achieves lower total enforcement cost while significantly improving convergence speed, particularly in heterogeneous trust environments. Finally, we provide an in-depth evaluation of how trust heterogeneity influences security policy enforcement. Extensive simulations were conducted to evaluate the framework's effectiveness in optimizing policy deployment for varying network topologies, demonstrating significant improvements in threat detection accuracy, policy consiste

关键词： Security Computer security Heuristic algorithms Costs Adaptation models message passing Complexity theory Computational modeling Reliability Adaptive systems Adaptive cybersecurity policy-based security message passing algorithms threat detection decentralized decision-making

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A review of message passing algorithms in estimation of distribution algorithms

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NATURAL COMPUTING 2016年第1期15卷 165-180页

作者： Santana, Roberto Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp Paseo Manuel Lardizabal 1 San Sebastian 20080 Spain

message passing algorithms (MPAs) have been traditionally used as an inference method in probabilistic graphical models. Some MPA variants have recently been introduced in the field of estimation of distribution algorithms (EDAs) as a way to improve the efficiency of these algorithms. Multiple developments on MPAs point to an increasing potential of these methods for their application as part of hybrid EDAs. In this paper we review recent work on EDAs that apply MPAs and propose ways to further extend the useful synergies between MPAs and EDAs. Furthermore, we analyze some of the implications that MPA developments can have in their future application to EDAs and other evolutionary algorithms.

关键词： message passing algorithms Propagation Estimation of distribution algorithms Hybridization

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Unifying message passing algorithms Under the Framework of Constrained Bethe Free Energy Minimization

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第7期20卷 4144-4158页

作者： Zhang, Dan Song, Xiaohang Wang, Wenjin Fettweis, Gerhard Gao, Xiqi Tech Univ Dresden Vodafone Chair Mobile Commun Syst D-01062 Dresden Germany Bosch Ctr Artificial Intelligence D-71272 Renningen Germany Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this article unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.

关键词： message passing Minimization Wireless communication Signal processing algorithms Optimization Channel estimation Bayes methods Statistical inference Bethe free energy message passing algorithms constrained optimization

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Dense limit of the Dawid-Skene model for crowdsourcing and regions of sub-optimality of message passing algorithms

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JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL 2020年第12期53卷 1-1页

作者： Schmidt, Christian Zdeborova, Lenka Univ Paris Saclay CEA Saclay Inst Phys Theor F-91191 Gif Sur Yvette France CNRS F-91191 Gif Sur Yvette France

Crowdsourcing is a strategy to categorize data through the contribution of many individuals. A wide range of theoretical and algorithmic contributions are based on the model of Dawid and Skene. Recently it was shown in the work of Ok et al that, in certain regimes, belief propagation is optimal for data generated from the Dawid-Skene model. This paper is motivated by this recent progress. We analyze a noisy dense limit of the Dawid-Skene model that has so long remained open. It is shown that it belongs to a larger class of low-rank matrix estimation problems for which it is possible to express the Bayes-optimal performance for large system sizes in a simple closed form. In the dense limit the mapping to a low-rank matrix estimation problem provides an approximate message passing algorithm that solves the problem algorithmically. We identify the regions where the algorithm efficiently computes the Bayes-optimal estimates. Our analysis further refines the results of Ok et al about optimality of message passing algorithms by characterizing regions of parameters where these algorithms do not match the Bayes-optimal performance. Besides, we study numerically the performance of approximate message passing, derived in the dense limit, on sparse instances and carry out experiments on a real world dataset.

关键词： message passing algorithms Bayesian statistics signal reconstruction approximation algorithms disordered systems spin glass

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INFERENCE OF GENE-REGULATORY NETWORKS USING message-passing algorithms

INFERENCE OF GENE-REGULATORY NETWORKS USING MESSAGE-PASSING ...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Shamaiah, Manohar Lee, Sang Hyun Vikalo, Haris Univ Texas Austin Dept ECE Austin TX 78712 USA

ISBN: (纸本)9781612847924

We present an application of message-passing techniques to gene regulatory network inference. The network inference is posed as a constrained linear regression problem, and solved by a distributed computationally efficient message-passing algorithm. Performance of the proposed algorithm is tested on gold standard data sets and evaluated using metrics provided by the DREAM2 challenge [1]. Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.

关键词： Gene regulatory networks message passing algorithms L1-regularized model

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A residual-based message passing algorithm for constraint satisfaction problems

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Communications in Theoretical Physics 2022年第3期74卷 77-86页

作者： Chun-Yan Zhao Yan-Rong Fu Jin-Hua Zhao College of Science University of Shanghai for Science and TechnologyShanghai 200093China Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing CenterSouth China Normal UniversityGuangzhou 510006China

message passing algorithms,whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages,provide a powerful toolkit in tackling hard computational tasks in optimization,inference,and learning *** the context of constraint satisfaction problems(CSPs),when a control parameter(such as constraint density)is tuned,multiple threshold phenomena emerge,signaling fundamental structural transitions in their solution *** solutions around these transition points is exceedingly challenging for algorithm design,where message passing algorithms suffer from a large message fiuctuation far from *** we introduce a residual-based updating step into message passing algorithms,in which messages with large variation between consecutive steps are given high priority in the updating *** the specific example of model RB(revised B),a typical prototype of random CSPs with growing domains,we show that our algorithm improves the convergence of message updating and increases the success probability in finding solutions around the satisfiability threshold with a low computational *** approach to message passing algorithms should be of value for exploring their power in developing algorithms to find ground-state solutions and understand the detailed structure of solution space of hard optimization problems.

关键词： constraint satisfaction problems model RB message passing algorithms residuals of messages

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Sketching Sparse Low-Rank Matrices With Near-Optimal Sample- and Time-Complexity Using message passing

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第9期69卷 6071-6097页

作者： Liu, Xiaoqi Venkataramanan, Ramji Univ Cambridge Dept Engn Cambridge CB2 1PZ England

We consider the problem of recovering an n(1) x n(2) low-rank matrix with k-sparse singular vectors from a small number of linear measurements (sketch). We propose a sketching scheme and an algorithm that can recover the singular vectors with high probability, with a sample complexity and running time that both depend only on k and not on the ambient dimensions n(1) and n(2). Our sketching operator, based on a scheme for compressed sensing by Li et al. and Bakshi et al., uses a combination of a sparse parity check matrix and a partial DFT matrix. Our main contribution is the design and analysis of a two-stage iterative algorithm which recovers the singular vectors by exploiting the simultaneously sparse and low-rank structure of the matrix. We derive a nonasymptotic bound on the probability of exact recovery, which holds for any n(1) x n(2) sparse, low-rank matrix. We also show how the scheme can be adapted to tackle matrices that are approximately sparse and low-rank. The theoretical results are validated by extensive numerical simulations and comparisons with existing schemes that use convex optimization for recovery.

关键词： message passing algorithms sparse graph codes peeling decoder low-rank matrix estimation compressed sensing

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Universality of Linearized message passing for Phase Retrieval With Structured Sensing Matrices

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第11期68卷 7545-7574页

作者： Dudeja, Rishabh Bakhshizadeh, Milad Columbia Univ Dept Stat New York NY 10027 USA

In the phase retrieval problem one seeks to recover an unknown n dimensional signal vector x from m measurements of the form y(i) = vertical bar(Ax)(i)vertical bar, where A denotes the sensing matrix. Many algorithms for this problem are based on approximate message passing. For these algorithms, it is known that if the sensing matrix A is generated by sub-sampling n columns of a uniformly random (i.e., Haar distributed) orthogonal matrix, in the high dimensional asymptotic regime (m, n -> infinity, n/m -> k), the dynamics of the algorithm are given by a deterministic recursion known as the state evolution. For a special class of linearized message-passing algorithms, we show that the state evolution is universal: it continues to hold even when A is generated by randomly sub-sampling columns of the Hadamard-Walsh matrix, if the signal is drawn from a Gaussian prior.

关键词： Phase retrieval message passing algorithms Hadamard-Walsh matrix Fourier matrix universality

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The Dynamics of message passing on Dense Graphs, with Applications to Compressed Sensing

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IEEE TRANSACTIONS ON INFORMATION THEORY 2011年第2期57卷 764-785页

作者： Bayati, Mohsen Montanari, Andrea Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Stat Stanford CA 94305 USA

"Approximate message passing" (AMP) algorithms have proved to be effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive numerical experiments further showed that their dynamics is accurately tracked by a simple one-dimensional iteration termed state evolution. In this paper, we provide rigorous foundation to state evolution. We prove that indeed it holds asymptotically in the large system limit for sensing matrices with independent and identically distributed Gaussian entries. While our focus is on message passing algorithms for compressed sensing, the analysis extends beyond this setting, to a general class of algorithms on dense graphs. In this context, state evolution plays the role that density evolution has for sparse graphs. The proof technique is fundamentally different from the standard approach to density evolution, in that it copes with a large number of short cycles in the underlying factor graph. It relies instead on a conditioning technique recently developed by Erwin Bolthausen in the context of spin glass theory.

关键词： Compressed sensing density evolution message passing algorithms random matrix theory state evolution

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A New Look at Reweighted message passing

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015年第5期37卷 919-930页

作者： Kolmogorov, Vladimir IST Austria A-3400 Klosterneuburg Austria

We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted message passing (SRMP). Special cases include well-known techniques such as Min-Sum Diffusion (MSD) and a faster Sequential Tree-Reweighted message passing (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. The new family of algorithms can be viewed as a generalization of TRW-S from pairwise to higher-order graphical models. We test SRMP on several real-world problems with promising results.

关键词： Graphical models MAP estimation message passing algorithms

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