Anomaly and Pattern Detection

What Are Anomaly Detection and Pattern Detection?

Anomaly detection identifies data points that deviate from the expected norm. In contrast, pattern detection focuses on finding recurring or significant patterns within data sets. Combined, they help organizations proactively monitor data to detect risks and enable strategic planning. 

What Is The Difference Between Anomaly Detection and Pattern Detection?

Anomaly and pattern detection are terms that are incorrectly used interchangeably. The following table drills down into the differences between the two types of detection. 

What Are Different Types of Anomaly Detection?

There are a number of different types of anomaly detection:

• Statistical methods: identify outliers using statistical measures like z-scores, mean, or standard deviation, to detect data points outside a normal range
• Machine learning-based methods:
  • Supervised learning: uses labeled data to train models for recognizing anomalies, often applied in fraud detection
  • Unsupervised learning: clustering or isolation forests to detect anomalies without labeled data, useful when patterns are unknown
• Time-series analysis: monitors time-based data to detect sudden changes or deviations in expected trends over time
• Deep learning methods: uses neural networks, such as auto-encoders or recurrent neural networks (RNNs), to detect complex anomalies in large data sets, often for image or video data
• Graph-based anomaly detection: identifies anomalies in network structures, like unexpected connections, or behaviors in social networks and  fraud rings

What Are Different Types of Pattern Detection?

• Frequency pattern mining: identifies frequently occurring patterns, such as frequent item-sets in market basket analysis 
• Sequential pattern mining: detects patterns in sequential data where order matters 
• Temporal pattern detection: focuses on patterns over time, such as seasonal weather patterns
• Spatial pattern detection: analyzes data with a spatial component, identifying patterns like geographic hotspots for shipping route clusters
• Cluster analysis: groups similar data points into clusters to detect natural groupings
• Correlation analysis: identifies relationships between variables, useful for detecting dependencies
• Anomaly pattern detection: recognizes common abnormal patterns in data, such as repeated deceptive shipping practices 

How Do Organizations Use Anomaly and Pattern Detection to Secure Maritime Shipments? 

Organizations use anomaly and pattern detection to enhance maritime shipment security by monitoring vessel behaviors, identifying unusual events, and optimizing their responses to threats. Here’s how they apply each technique:

• Anomaly detection: anomaly detection uses ship positions, speed, and route adherence in real-time to identify deviations from normal behavior. These alerts help prevent piracy, unauthorized access, and cargo theft. Anomaly detection also flags unusual cargo loading patterns, which may indicate smuggling attempts.
• Pattern detection: organizations can predict normal vessel behaviors and identify high-risk periods and routes using pattern analysis of shipping routes, seasonal traffic trends, and historical data on cargo handling. 

Recognizing common patterns in previous security incidents allows for proactive resource allocation and preemptive security measures. Pattern detection also enables logistics optimization, reducing vulnerabilities by minimizing predictable, high-risk behaviors.

Together, anomaly and pattern detection provide a layered defense, enabling proactive risk management and incident response to secure maritime shipments effectively. In one example, Windward’s anomaly detection identified massive GPS jamming off the coast of Sudan, indicating a high likelihood of illicit maritime activity in the region.

How Are Anomaly and Pattern Detection Better Than Traditional Rule-Based Detection?

Anomaly and pattern detection offer significant advantages over traditional rule-based detection, particularly in complex fields, like maritime shipping. Unlike rule-based systems, which depend on predefined parameters, anomaly detection can adapt to new or evolving threats by recognizing unusual patterns in real-time, even if they don’t match specific rules. 

This adaptability is paired with a reduction in false positives, as anomaly detection systems adjust to normal patterns, filtering out common yet legitimate anomalies that would typically trigger alerts in a rule-based system.

Anomaly and pattern detection systems are highly scalable, processing vast amounts of data across multiple sources without an exhaustive set of rules, which makes them ideal for large-scale, dynamic operations. Pattern detection captures complex, multivariate trends and correlations, such as seasonal shipping traffic, that are challenging to define with static rules but essential for understanding long-term operational insights. 

Together, these capabilities enable a proactive approach to security, helping organizations forecast potential risks and optimize resource allocation based on emerging trends. This nuanced approach provides a more robust and efficient security solution than traditional rule-based systems, making anomaly and pattern detection ideal for evolving operational landscapes.

What Type of Data is Used in Anomaly and Pattern Detection in the Maritime Arena?

Anomaly and pattern detection rely on a variety of data types to monitor, assess, and secure operations effectively:

• Vessel tracking data: information from the Automatic Identification System (AIS) and GNSS on a vessel’s real-time location, speed, heading, and course deviations
• Weather and environmental data: weather forecasts, sea conditions, and ocean currents data help predict and assess risks
• Historical route data: past shipping routes, including stops and typical port visit patterns, aid in identifying deviations or emerging trends
• Cargo data: information on cargo type, weight, and volume assists in spotting anomalies related to unexpected weight fluctuations or potential smuggling attempts
• Port and docking data: docking schedules, times, and activity at ports are used to detect unexpected delays, unauthorized access, or unusual docking behaviors
• Communication logs: communication records between vessels and onshore teams, including times and frequencies, can highlight unusual communication patterns 
• Financial and transactional data: payment records and cargo insurance details can identify suspicious transactions that may signal fraud

These data sources enable anomaly and pattern detection systems to proactively address security, environmental, and operational risks in maritime shipments.

How Does AI-Powered Anomaly Detection and Pattern Detection Benefit Maritime Organizations? 

The spotting of irregular activities by AI solutions can empower maritime and supply chain organizations to uncover anomalies or new trends automatically, with no required user input. Stakeholders can be alerted to global events that would otherwise remain hidden, such as an increase in vessels loitering in specific locations, or a surge in ships changing their flags even before the cause is apparent.  

When anomalies or patterns are identified, a good AI system should include a strong generative AI component, to provide possible relevant, contextualized causes. These could include illegal trafficking, weather changes, or economic shifts – the Gen AI should also offer recommended actions. 

Users can understand potential underlying factors driving the unusual activities and be the first to respond to them, whether that is marking an area of interest, launching an investigation, or taking immediate action.