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Home / Learn / What is anomaly detection and what benefits does it bring?

What is anomaly detection and what benefits does it bring?

What is anomaly detection?

Anomaly detection is a process in data analysis that identifies patterns, observations, or events that deviate significantly from the expected norm or standard behavior. These anomalies, often referred to as outliers, can indicate critical issues such as ransomware attacks, network intrusions, or equipment failures in various applications. The process relies on statistical methods, artificial intelligence, and data mining techniques to distinguish between normal and abnormal data points.

 

Basic approaches to anomaly detection

Anomaly detection can be categorized into unsupervised, supervised and semi-supervised methods.

 

supervised
Supervised
Training model - unlabeled and labled data
  • Decision Boundary: Trains on labelled data to define a boundary that separates normal instances from anomalies.
  • Binary Classification: Categorizes new data as normal or anomalous based on similarity to training examples.
  • Best for Labelled Data: Ideal when labelled data is abundant, anomalies are well-defined, and high precision is required.
semisupervised
Semi-supervised
Training model - labelled data
  • Hybrid Approach: Combines labelled and unlabelled data for anomaly detection, balancing scalability and precision.
  • Anomaly Scoring: Uses deviations from the model to assign anomaly scores.
  • Optimal for Limited Data: Effective when labelled data is scarce and datasets are complex.
Unsupervised
Unsupervised
Training model - unlabelled data
  • Structure Learning: Identifies anomalies by uncovering the inherent data structure without using labelled examples.
  • Clustering and Density Estimation: Groups similar data points and flags outliers or low-probability points as anomalies.
  • Ideal for Unlabelled Data: Effective when labelled data is unavailable, automatically detecting unusual patterns.

Is anomaly detection predictive?

Anomalous behavior detection can be traditional or predictive. While traditional detection methods focus on identifying irregularities in current data, predictive modeling techniques forecast potential anomalies before they occur.

Predictive models analyze historical data to capture regular patterns and trends, which are then used to predict the likelihood of deviations occurring in future data points. Predictive detection is particularly useful in circumstances where early detection can mitigate risks, such as in fraud detection and cybersecurity.

 

What is the best method of anomaly detection?

There isn’t a single “best” method of outlier detection, as effectiveness depends on various factors – such as the nature of the data, the specific context of the problem and the available resources. However, a widely used method is the isolation forest technique.

Isolation forests can identify unusual patterns in data without needing labeled examples. They isolate anomalies quickly and ensure rapid detection of potential threats. With low linear time complexity, this method is highly efficient for large datasets. Its versatility, requiring no assumptions about data distribution, allows it to be applied across various types of data. For businesses, this means improved data protection and quick identification of threats.
best method of anomaly detection

Anomaly detection use cases

Anomaly detection has various use cases across different industries. In backup environments anomaly detection is mainly used for:

Ransomware protection

Anomalous behavior detection plays a crucial role in ransomware protection for backup data by continuously monitoring for unusual patterns or deviations from normal behavior. It helps identify potential ransomware activities early, often before significant damage occurs, protecting your last line of defense and maintaining your ability to recover data.

Capacity Planning and Storage Optimization

Anomaly detection can identify unexpected increases in storage consumption, which might indicate data growth patterns that need attention or cleanup.

Backup Policy Compliance

Anomalous backup behaviors, such as incomplete backups, backups happening at unusual times, or missing files, may indicate deviations from established backup policies. This helps ensure that backups are compliant with organizational standards and regulatory requirements.

 

How do you implement anomaly detection?

Applying anomalous behavior detection requires a few key steps.

  1. Data collection – Gather and preprocess relevant data from logs or network traffic.
  2. Select a detection model – Choose an appropriate outlier detection method based on the nature of your data. The model should be trained using labeled or unlabeled data, depending on the approach selected (supervised, unsupervised or semi-supervised).
  3. Evaluate – Validate the model’s performance and fine-tune as necessary.
  4. Integrate – Introduce the system into your existing workflow for real-time detection.
  5. Maintenance – Regularly monitor performance. Retrain the model periodically with new data and update it as needed to adapt to changing patterns and emerging anomalies.

 

Challenges when using anomaly detection

Anomaly detection can significantly bolster cyber resiliency and combat ransomware attacks. However, its effectiveness hinges on overcoming several challenges:

  1. Defining normality – Determining what constitutes "normal" behavior in backup data is essential. This baseline varies widely depending on context and specific datasets.
  2. Data quality – Outlier detection is highly sensitive to data quality. Incomplete, incorrect, or noisy data can result in false positives (normal behavior flagged as anomalous) or false negatives (actual anomalies missed). High data quality is necessary for reliable detection.
  3. Dynamic data – Normal behavior definitions can change over time, influenced by trends, seasons, or events. Outlier detection models must adapt to these changes to remain effective. Continuously updating your baseline ensures the system can accurately identify anomalies.
Challenges when using anomaly detection

What are the benefits of anomaly detection?

Anomaly detection is vital for ensuring the security and reliability of backup environments. By identifying unusual patterns, such as unexpected data deletions or unauthorized encryption activities, it enables the early detection of serious threats like ransomware attacks or data corruption, reducing the risk of compromised backups. This proactive approach not only helps prevent costly downtime but also protects against significant data loss, ensuring that your backup systems remain dependable when they are most needed.

Moreover, anomaly detection incorporates adaptive learning, continuously evolving with your backup data to identify new threats and patterns. This ongoing, intelligent protection is crucial for maintaining the integrity of your backups, ensuring that your data recovery processes are always up to date and effective. In the realm of cybersecurity and fraud prevention, outlier detection enhances the security of your backup environment by safeguarding sensitive information, such as personally identifiable information (PII), financial records, and intellectual property (IP). This added layer of protection ensures that your backups are not only complete but also secure from unauthorized access and potential data breaches, helping you avoid costly financial and reputational damage.

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