finding multi-step attacks in computer networks using heuristic search and mobile ambients

Franqueira, Virginia N. L.

doi:10.3990/1.9789036529235

Cited by 6 publications

(2 citation statements)

References 130 publications

(287 reference statements)

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“…Finding steps of possible attacks turns out to be a rather challenging problem due to the complexity and size of networks, and the high number of possible combinations among steps which represent opportunities for potential attackers. To address the problem of proactively finding possible multi-step attacks in networks, Franqueira's thesis [7] contains the MsAMS (Multistep Attack Modeling and Simulation) solution: an approach that uses a variation of Mobile Ambients as modeling paradigm, and Heuristic Search as simulation paradigm, supported by Link Analysis Ranking algorithms as a source of metrics. As an evidence of feasibility of this approach in networks of realistic size, this thesis introduces the MsAMS proof-of-concept tool, and shows that this is scalable to realistic networks.…”

Section: Modeling Penetration Testingmentioning

confidence: 99%

Theory of modern penetration testing expert system

Стефінко¹,

Piskozub²

2017

СОІ

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The paper considers the models and algorithms of intelligent components intended for active analyzing. The offered approach is based on simulation of computer attacks by using attack graphs, logical trees and solving the probabilistic questions. This article discusses developing of the security expert system by using new methods for efficient and automated penetration testing. Methodologies of automated planning and partially observable Markov decision processes are suggested in order to improve results and build flexible security evaluation expert system.

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Section: Modeling Penetration Testingmentioning

confidence: 99%

Theory of modern penetration testing expert system

Стефінко¹,

Piskozub²

2017

СОІ

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“…For instance, cell-to-switch assignment is developed using heuristic search for mobile calls in [17]. Heuristic search is further used in [18] to simulate possible attackers searching for attacks in modelled network for proactive and continuous identification of network attacks. Recursive Random Search (RRS) strategy is used in [19] to build an online simulation framework to aid generic large scale network protocol and parameter configuration.…”

Section: Motivationmentioning

confidence: 99%

VOIP Call Optimization in Diverse Network Scenarios Using Learning Based State-Space Search Technique

Chakraborty¹,

Mukhopadhyay²,

Misra³

et al. 2011

IJWMN

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A VoIP based call has stringent QoS requirements with respect to delay, jitter, loss, MOS and R-Factor. Various QoS mechanisms implemented to satisfy these requirements must be adaptive under diverse network scenarios and applied in proper sequence, otherwise they may conflict with each other. The objective of this paper is to address the problem of adaptive QoS maintenance and sequential execution of available QoS implementation mechanisms with respect to VoIP under varying network conditions. In this paper, we generalize this problem as state-space problem and solve it. Firstly, we map the problem of QoS optimization into state-space domain and apply incremental heuristic search. We implement the proposed algorithm under various network and user scenarios in a VoIP test-bed for QoS enhancement.Then learning strategy is implemented for refinement of knowledge base to improve the performance of call quality over time. Finally, we discuss the advantages and uniqueness of our approach.

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Fast and reliable online learning to rank for information retrieval

Hofmann

2013

SIGIR Forum

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The amount of digital data we produce every day far surpasses our ability to process this data, and finding useful information in this constant flow of data has become one of the major challenges of the 21st century. Search engines are one way of accessing large data collections. Their algorithms have evolved far beyond simply matching search queries to sets of documents. Todays most sophisticated search engines combine hundreds of relevance signals to provide the best possible results for each searcher.Current approaches for tuning the parameters of search engines can be highly effective. However, they typically require considerable expertise and manual effort. They rely on supervised learning to rank, meaning that they learn from manually annotated examples of relevant documents for given queries. Obtaining large quantities of sufficiently accurate manual annotations is becoming increasingly difficult, especially for personalized search, access to sensitive data, or search in settings that change over time.In this thesis, I develop new online learning to rank techniques, based on insights from reinforcement learning. In contrast to supervised approaches, these methods allow search engines to learn directly from users interactions. User interactions can typically be observed easily and cheaply, and reflect the preferences of real users. Interpreting user interactions and learning from them is challenging, because they can be biased and noisy. The contributions of this thesis include a novel interleaved comparison method, called probabilistic interleave, that allows unbiased comparisons of search engine result rankings, and methods for learning quickly and effectively from the resulting relative feedback.The obtained analytical and experimental results show how search engines can effectively learn from user interactions. In the future, these and similar techniques can open up new ways for gaining useful information from ever larger amounts of data.The thesis is available online at http://katja-hofmann.de and http://dare.uva.nl/record/ 446342. Software written for this thesis, including reference implementations of the developed interleaving and online learning methods, is available at

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finding multi-step attacks in computer networks using heuristic search and mobile ambients

Cited by 6 publications

References 130 publications

Theory of modern penetration testing expert system

Theory of modern penetration testing expert system

VOIP Call Optimization in Diverse Network Scenarios Using Learning Based State-Space Search Technique

Fast and reliable online learning to rank for information retrieval

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