Maritime anomaly detection deals with identifying unlikely and rare events in the patterns of vessel movements.

1. Maritime Anomaly Detection
Zahra Sadeghi

2. Motivation and importance
• Safe and secure maritime navigation
by identifying suspicious activities
• Marine transportation protection
• preventing dangerous situations:
• Collision
• illegal fishing
• Smuggling
• Pollution
• piracy
AIS data from marine traffic around the ports of Seattle
and Vancouver
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Maritime Anomaly Detection - By: Zahra Sadeghi

3. Time series analysis of AIS data
• AIS is an automated tracking and monitoring system used by marine vessels
• It is facilitated by the continuous transmission of data with other nearby vessels, as well as AIS base
stations and satellites
• By analyzing the time series of space-time coordination, we can assess the situation and maintain a
situation awareness
• A time series is a collection of a sequential series of transmitted data points (AIS messages) measured
at successive points over time.
• The study of these data points, in order to extract meaningful feature, behavior, characteristics, and
statistics, is called time series analysis.
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4. Anomaly and outlier detection
• Anomaly detection deals with identifying unlikely and rare events.
• Finding observations that do not fit the typical/normal statistical
distribution of a dataset.
• Movement behavior deviation from other vessels of the same type
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Maritime Anomaly Detection - By: Zahra Sadeghi

5. Challenges
• AIS data is unreliable, noisy and inaccurate
• AIS information not updated in a timely manner
• long gaps between messages
• AIS Transmitters and satellite receivers' noise
• Vessel operators have to input codes to their AIS by hand (erroneous manual
input)
• AIS signals can be easily spoofed and manipulated by attackers or parties willing to
obscure their locations
• There is a lack of well-known anomalous AIS situations
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Maritime Anomaly Detection - By: Zahra Sadeghi

6. Lack of annotated public dataset
• labeling sequence data for the task of anomaly detection is an
expensive manual task.
• abundance of unknown and undefined anomalous events
• lack of well-studied anomalous AIS situations to represent a reliable ground
truth.
• common ML approaches require training a model on an annotated
dataset for learning the distinction between groups of normal and
abnormal data
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Maritime Anomaly Detection - By: Zahra Sadeghi

7. Anomalous AIS behavior
Deviation
from
standard
route
Unexpected
activity
Unexpected port
arrival
Close
approach
Zone entry
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Maritime Anomaly Detection - By: Zahra Sadeghi

8. ML techniques for anomaly detection
• supervised anomaly detection – modeling both the normal and
anomalous behaviour.
• it requires labeled data.
• includes classification-based methods.
• semi-supervised anomaly detection – modeling just one type of behaviour
• the model could be incrementally trained, as new instances appear.
• Normal behavior learning
• unsupervised anomaly detection – searching for anomalies with no
previous knowledge of the data.
• analogical to clustering, where similar instances are grouped into clusters, based on
some similarity measure (distance, density, …)
• the assumption is that anomalies are well separated from the rest of the data
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Maritime Anomaly Detection - By: Zahra Sadeghi

9. Prediction-based methods
• forecasting the next time step
• Autoregression models (ARIMA/SARIMA)
• Pros
• No labeled data is required.
• Cons
• sensitive to parameter selection
• Poor performance for long trajectories
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Maritime Anomaly Detection - By: Zahra Sadeghi

10. Clustering-based methods
• Data that doesn't fit well to clusters are considered as anomaly/outlier
• K-means, spectral clustering, hierarchical clustering, ...
• Pros:
• Can be applied in unsupervised way
• Cons:
• Hyperparameter tuning
• Is not optimized for finding anomalies
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Maritime Anomaly Detection - By: Zahra Sadeghi

11. Network-based methods
• traditional Machine Learning algorithms are not capable of finding efficient
answers due to unpredictability and complexity of maritime navigation
• Supervised and unsupervised approaches
• Sequential models (RNN, LSTM, AE)
• Pros
• Learn high-level and complex features automatically
• Automatic feature engineering and self-learning capabilities
• High performance, efficiency and accuracy
• Cons
• Requires large amount of data
• Computationally expensive
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Maritime Anomaly Detection - By: Zahra Sadeghi

12. Q&A
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