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The Internet of Things (IoT) offers immense benefits by enabling devices to leverage networked resources thereby making intelligent decisions. The numerous heterogeneous connected devices that exist throughout the IoT system creates new security and privacy concerns. Some of these concerns can be overcome through trust, transparency, and integrity, which can be achieved with data provenance. Data provenance, also known as data lineage, provides a history of transformations that occurs on a data object from the time it was created to its current state. Data provenance has been explored in the areas of scientific computing, business, forensic analysis, and intrusion detection. Data provenance can help in detecting and mitigating malicious cyber attacks. In this paper, we explore the integration of provenance within the IoT. We introduce Provenance Aware Internet of Things System (PAIoTS), a provenance collection framework for IoT devices. We evaluate the effectiveness of our framework by developing a prototype system for proof of concept.
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Runtime Analysis of Whole-System Provenance
Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security. Digital provenance provides a detailed history of the flow of information within a computing system, connecting suspicious events to their root causes. Although existing provenance-based auditing techniques provide value in forensic analysis, they assume that such analysis takes place only retrospectively. Such post-hoc analysis is insufficient for realtime security applications; moreover, even for forensic tasks, prior provenance collection systems exhibited poor performance and scalability, jeopardizing the timeliness of query responses. We present CamQuery, which provides inline, realtime provenance analysis, making it suitable for implementing security applications. CamQuery is a Linux Security Module that offers support for both userspace and in-kernel execution of analysis applications. We demonstrate the applicability of CamQuery to a variety of runtime security applications including data loss prevention, intrusion detection, and regulatory compliance. In evaluation, we demonstrate that CamQuery reduces the latency of realtime query mechanisms, while imposing minimal overheads on system execution. CamQuery thus enables the further deployment of provenance-based technologies to address central challenges in computer security.
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- PAR ID:
- 10087200
- Date Published:
- Journal Name:
- 2018 ACM SIGSAC Conference on Computer and Communications Security
- Page Range / eLocation ID:
- 1601 to 1616
- Format(s):
- Medium: X
- 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/10.1145/3243734.3243776
