Attack Provenance Tracing in Cyberspace: Solutions, Challenges and Future Directions

Tan, Cheng; Wang, Qian; Wang, Lina; Zhao, Lei

doi:10.1109/mnet.2018.1700469

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Causality Preserved Reduction (CPR) [19], on the other hand, leverages the dependencies among the system events to reduce the number of log entries. The key idea is that some events have identical contributions to the dependency analysis and can be aggregated [43] without damaging inter-dependencies.…”

Section: Offline Reduction Schemesmentioning

confidence: 99%

A comprehensive survey on data provenance: State-of-the-art approaches and their deployments for IoT security enforcement

Alam

Wang

2021

JCS

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Data provenance collects comprehensive information about the events and operations in a computer system at both application and kernel levels. It provides a detailed and accurate history of transactions that help delineate the data flow scenario across the whole system. Data provenance helps achieve system resilience by uncovering several malicious attack traces after a system compromise that are leveraged by the analyzer to understand the attack behavior and discover the level of damage. Existing literature demonstrates a number of research efforts on information capture, management, and analysis of data provenance. In recent years, provenance in IoT devices attracts several research efforts because of the proliferation of commodity IoT devices. In this survey paper, we present a comparative study of the state-of-the-art approaches to provenance by classifying them based on frameworks, deployed techniques, and subjects of interest. We also discuss the emergence and scope of data provenance in IoT network. Finally, we present the urgency in several directions that data provenance needs to pursue, including data management and analysis.

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Section: Offline Reduction Schemesmentioning

confidence: 99%