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Conference object . 2018
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https://doi.org/10.1109/dasc/p...
Article . 2018 . Peer-reviewed
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Conference object . 2024
Data sources: DBLP
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Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data

Authors: Ioannis Kontopoulos; Giannis Spiliopoulos; Dimitrios Zissis; Konstantinos Chatzikokolakis 0002; Alexander Artikis;

Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data

Abstract

Well poisoning is an ancient war stratagem which was frequently used as a "scorched earth tactic". Today this tactic has been adapted by malicious attackers to the digital world and evolved into "stream poisoning", in which corrupt or fallacious data is injected into a data lake, so as to corrupt the integrity of the information stored there. Numerous maritime surveillance systems nowadays rely on the Automatic Identification System (AIS), which is compulsory for vessels over 299 Gross Tones, for vessel tracking purposes. Ship AIS spoofing involves creating a nonexistent vessel or masquerading a vessel's true identity, resulting in hiding or transmitting false positional data, so that a vessel appears to behave legitimately, thus deceiving stakeholders and authorities. Due to the volume and velocity of data received traditional approaches fail to automatically detect these spoofing events in real time. We focus on an industrial use case of detecting spoofing events in AIS streams and validate our approach in real world conditions.

Keywords

AIS vessel monitoring, big data, distributed stream processing, anomaly detection

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
20
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Top 10%
Top 10%
3
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