Community-based self generation of policies and processes for assets: Concepts and research directions

Bertino, Elisa; Mel, Geeth de; Russo, Alessandra; Calo, Seraphin; Verma, Dinesh

doi:10.1109/bigdata.2017.8258265

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…These conditions thus influence the set of policies to be considered for local operation. Shared policies could be used by the AMS's GPM to leverage policies learned in different contexts by other trusted AMSs in a collaborative fashion, as is proposed in community-based policy learning (CASWiki) by Bertino, et al [16]. In the CASWiki, agents, primarily autonomous systems such as Connected and Autonomous Vehicles (CAVs) or Internet of Things (IoT) devices, contribute policies to a shared knowledge base.…”

Section: A Key Components Of Agenpmentioning

confidence: 99%

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Bertino

White²,

Lobo

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.

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Section: A Key Components Of Agenpmentioning

confidence: 99%

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Bertino

White²,

Lobo

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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“…While machine learning allows the ability for devices to determine their own models, it would provide a greater flexibility if devices could share the models that they have learnt with each other. This sharing of knowledge on how to determine policies in general has been introduced in [4] and can be used to further improve dynamic access control policy determination. Devices in this model act like a community of managers of other devices.…”

Section: Sharing Of Modelsmentioning

confidence: 99%

Self-Generation of Access Control Policies

Calo

Verma

Chakraborty

et al. 2018

Proceedings of the 23nd ACM on Symposium on Access Control Models and Technologies

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Access control for information has primarily focused on access statically granted to subjects by administrators usually in the context of a specific system. Even if mechanisms are available for access revocation, revocations must still be executed manually by an administrator. However, as physical devices become increasingly embedded and interconnected, access control needs to become an integral part of the resources being protected and be generated dynamically by the resources depending on the context in which they are being used. In this paper, we discuss a set of scenarios for access control needed in current and future systems and use that to argue that an approach for resources to generate and manage their access control policies dynamically on their own is needed. We discuss some approaches for generating such access control policies that may address the requirements of the scenarios.

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