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Android applications are extremely popular, as they are widely used for banking, social media, e-commerce, etc. Such applications typically leverage a series of Permissions, which serve as a convenient abstraction for mediating access to security-sensitive functionality within the Android Ecosystem, e.g., sending data over the Internet. However, several malicious applications have recently deployed attacks such as data leaks and spurious credit card charges by abusing the Permissions granted initially to them by unaware users in good faith. To alleviate this pressing concern, we present DyPolDroid, a dynamic and semi-automated security framework that builds upon Android Enterprise, a device-management framework for organizations, to allow for users and administrators to design and enforce so-called Counter-Policies, a convenient user-friendly abstraction to restrict the sets of Permissions granted to potential malicious applications, thus effectively protecting against serious attacks without requiring advanced security and technical expertise. Additionally, as a part of our experimental procedures, we introduce Laverna, a fully operational application that uses permissions to provide benign functionality at the same time it also abuses them for malicious purposes. To fully support the reproducibility of our results, and to encourage future work, the source code of both DyPolDroid and Laverna is publicly available as open-source.
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Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies
Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs significant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly configure and periodically update. Security negligence and human errors often lead to misconfiguring IAM policies which may open a backdoor for attackers. To address these challenges, first, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively implies minimizing unnecessary datastore access permissions. Second, to make IAM policies interpretable, we use graph representation learning applied to historical access patterns of users to augment our CP model with similarity constraints: similar users should be grouped together and share common IAM policies. Third, we describe multiple attack models and show that our optimized IAM policies significantly reduce the impact of security attacks using real data from 8 commercial organizations, and synthetic instances.
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- Award ID(s):
- 1817020
- PAR ID:
- 10573239
- Publisher / Repository:
- International Joint Conferences on Artificial Intelligence Organization
- Date Published:
- ISBN:
- 978-1-956792-00-3
- Page Range / eLocation ID:
- 1850 to 1858
- Format(s):
- Medium: X
- Location:
- Vienna, Austria
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24963/ijcai.2022/257
