What is anomaly detection?

Anomaly detection—also referred to as outlier detection—plays a crucial role in cybersecurity.  By leveraging advanced technologies such as machine learning (ML) and artificial intelligence (AI), anomaly detection systems can recognize deviations from normal behavior and events within a network or system, swiftly identifying unusual patterns or points that may indicate a potential threat or a cyberattack that is underway.

Integrating anomaly detection into a comprehensive cybersecurity strategy enhances an organization's ability to protect sensitive data and systems from malicious attacks, proactively address threats, and maintain the integrity of critical information and systems.

Fundamentals of anomaly detection

Anomaly detection is the process of analyzing a dataset and identifying single occurrences or patterns that deviate significantly from baseline activity.

In the context of cybersecurity, these anomalies, or outliers, can often be an early warning sign of a malicious event, such as a data breach, cyberattack or system failure. By identifying these anomalies sooner, organizations can potentially contain the security risk, thereby minimizing damages and expediting recovery.

Types of anomalies

There are three main types of anomalies detectable by advanced anomaly detection systems:

•  Point anomalies: A point anomaly is when an individual data point significantly deviates from the rest of the data set and the so-called “norm”. An example of a point anomaly may be a sudden spike in network traffic.
• Contextual anomalies: A contextual anomaly is an individual data point that differs from the rest of a data set, but only within a specific context. For example, if a user logs into a system during non-business hours or from an IP address that does not match their geographic location, that may be a contextual anomaly.
• Collective anomalies: A collective anomaly is when a group of related data points collectively deviate from the expected pattern, even though individual data points may fall within normal and acceptable use. For example, a sudden surge in network traffic from a variety of IP addresses may indicate a coordinated attack and would be an example of a collective anomaly.

Common methodologies used in anomaly detection

While traditional anomaly detection was done manually via statistical and mathematical calculations, the explosion of data in recent years has made it simply impossible for humans to perform such tasks.

Modern anomaly detection efforts employ ML and/or AI algorithms to quickly and accurately analyze massive quantities of data. Some solutions also leverage deep learning techniques, a subset of machine learning that relies on multi-layer neural networks to process data and identify more complex anomalies.

Importance of anomaly detection in cybersecurity

Most breaches have warning signs. The question is: Does your organization have the right tools to detect and act on those signals?

Anomaly detection systems play a vital role in helping an organization maintain a strong security posture. When integrated with other cybersecurity tools, an anomaly detection system can:

•  Enable the early identification of potential security incidents, including hard-to-detect threats and attacks, such as insider threats, a cybersecurity risk that comes from within the organization, or advanced persistent threats (APTs), sophisticated, sustained cyberattacks in which an intruder establishes an undetected presence in a network;
• Contain the attack earlier in the attack lifecycle, allowing the organization to minimize losses and improve remediation time;
• Maintain the integrity of critical information and systems;
• Optimize resources by focusing efforts on critical, high-priority events; and
• Improve decision-making by leveraging clear and actionable insights to initiate response efforts.

Anomaly Detection Techniques

Anomaly detection systems rely on a variety of techniques to identify outlier events and behavior. In the chart below, we explore the four main anomaly detection methods, their applications and specific techniques.

Challenges in anomaly detection

There are many significant challenges organizations may face when it comes to anomaly detection. Below we explore some of the most common issues in cybersecurity:

Accuracy

Human behavior is dynamic, complex and variable, which means that it can be difficult to determine what is “normal” even for the most advanced algorithms. The accuracy of the model depends on how the model is trained and the dataset used to train it. Gaps, errors or noise in the data collection or analysis process can produce a number of performance issues, including:

•  False positives, alerting the system to an outlier event that is harmless, which waste resources and contribute to staff fatigue.
• False negatives, failing to alert the system to a credible threat, which can result in a data breach or cyberattack that has potentially devastating consequences for the organization.
• Unintentional anomalies, a data point that naturally deviates from the norm, which can distort the data set and degrade system performance.

High reliance on training data

The effectiveness of the anomaly detection system is closely linked to the quality and volume of the data it is trained on. Insufficient or biased data can result in poor threat detection, leading to higher rates of false positives or negatives.

Ethical and privacy concerns

Anomaly detection tools require an extensive collection of user and entity behavior data. However, collection of this data may raise privacy and ethical concerns. Companies must ensure all data collection and operation of related tools complies with relevant regulations. Companies should also develop robust governance models that ensure data is used ethically.

Adversarial AI

As AI- and ML-powered systems and algorithms become integral to cybersecurity defenses, they themselves may become targets. Adversarial AI or adversarial machine learning (ML) seeks to inhibit the performance of AI/ML systems by manipulating or misleading them. Sophisticated attackers have begun to manipulate the AI training process or exploit vulnerabilities within the system. Therefore, organizations must take steps to protect their systems from such attacks.

Large data volumes

Another challenge in the world of anomaly detection is analyzing an ever-increasing volume of data in real-time. This is especially true in cybersecurity, as individual tools generate significant data streams that must them be consolidated and analyzed. This requires significant computational resources which can be difficult to maintain and scale over time. To manage this issue, it may be possible to leverage a distributed computing model or data reduction techniques to reduce the strain on the organization without compromising accuracy.

Evolving attack patterns

The cyber threat landscape is always evolving, which means that even the most effective tools must adapt to detect the latest attack patterns and techniques. AI and ML must be utilized to update and retrain models to protect against unknown threats (those that do not adhere to an existing script) and remain an effective component in the broader cybersecurity toolset.

Anomaly detection implementation best practices

Anomaly detection is not a static capability. It requires constant tuning, monitoring and training to remain effective. Here we outline some best practices that organizations should keep in mind when implementing an anomaly detection solution as part of their cybersecurity strategy:

•  Train models with high-quality data. Data is the foundation of every anomaly detection system. The old adage “junk in, junk out” applies to these solutions. To ensure the tool is effective, the models must be trained on high-quality, relevant data. It is also important to gather comprehensive datasets from a variety of sources, so that the tool can distinguish between routine events and potential threats.
• Continuous monitoring and tuning. Anomaly detection is a dynamic function. These solutions require constant evaluation and adaptation, as well as integrated feedback loops, to ensure they remain effective and accurate. This is especially important when it comes to protecting the organization from advanced and emerging threats, as well as signature-less, unknown threats that cannot be detected by traditional cybersecurity tools.
• Comprehensive integration and collaboration. Anomaly detection is but one part of a broader cybersecurity strategy. These tools must be integrated with other detection and response tools, the SIEM system and threat intelligence system to ensure alerts raised by the anomaly detection solution are acted upon.

Future trends

The integration of AI and ML, particularly those involving deep learning or neural networks, into anomaly detection has revolutionized the field of cybersecurity. Ongoing advancements of these technologies will enable the detection of complex and subtle anomalies that would be difficult, if not impossible, to accurately identify using existing methods.

Looking to the future, these systems may become sophisticated enough to operate autonomously, not only detecting outlier events, but taking predefined steps to resolve those issues. For example, if the system detects an unusual data transfer request, the anomaly detection tool could trigger an action in a supporting cybersecurity solution to block the exfiltration, quarantine the transfer or even isolate the network.

Finally, anomaly detection will likely be integrated with other advanced technologies, such as blockchain or quantum computing. In the case of blockchain, such an integration would establish an immutable ledger that could log anomalies and their associated actions. Quantum computing could potentially enable data processing at far greater speeds, which could potentially reduce detection times while maintaining or improving accuracy.

 Key considerations

Though every organization’s needs, challenges, risk tolerance and goals are unique, there are several key factors that should be considered when choosing an anomaly detection solution.

 Integration

• Does the solution integrate seamlessly with other tools within the existing cybersecurity stack and IT infrastructure?
• Can the solution effectively access and analyze data from a diverse set of sources?

 Scalability

• Does the solution have limitations on the amount of data that can be managed without compromising performance?
• Does the solution leverage a cloud-based architecture, which would theoretically allow the organization to add capacity without making infrastructure investments?

Accuracy

• What is the solution’s detection rate?
• What is its false positive/false negative rate?

 Latency

• Does the solution have limitations on the amount of data that can be managed in real-time?

 Cost

• What is the total cost of ownership for the tool for an organization of your size?
• Does the vendor offer any ROI calculations that can help establish a business case for acquiring the tool?

Support

• What support services, if any, are included with the purchase or leasing of the tool?
• Does the vendor assist with set up, configuration, deployment and integration?
• What is the process for updating and upgrading the toolset?

 Reputation

• Does the vendor have any industry awards or analyst recognitions for this tool?
• Does the organization have any public case studies or referenceable clients to establish the tool’s effectiveness and the vendor’s support services?

It is important to remember that the right anomaly detection solution depends on the needs and goals of the organization. Different industries face different challenges and often have varying degrees of risk. For example, healthcare organizations, financial services organizations and any company that has direct access to sensitive customer data should take significant measures to protect their organization and also comply with any government regulations.

Conclusion

Anomaly detection is a critical component in every cybersecurity strategy. By integrating these capabilities within the tech stack, organizations can identify, contain and remediate potential risks more effectively, reducing the likelihood and impact of a security event.

As with any high-tech industry, it is important for organizations to continuously monitor and adapt their anomaly detection capabilities to ensure that they are effective against the latest threats and help the organization maintain a strong security posture.