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What is anomaly detection in AI?
Anomaly detection in AI is a technique used to identify unusual patterns or outliers in a dataset that deviate from a normal baseline.
These outliers could signify a problem such as performance degradation of digital systems, an unexpected system error, security intrusions, or a rare event of interest. AI and machine learning algorithms are typically used in anomaly detection to process large volumes of data collected with specific meta tags, labels and other properties to automatically identify such anomalies. These AI models and algorithms can be trained to recognize what’s considered “normal” and then flag anything that deviates from that normal baseline. This is particularly useful in industries such as enterprise networking, cybersecurity, finance, healthcare and manufacturing, where early detection of anomalies can prevent further issues or losses.
Why do we need artificial intelligence in anomaly detection?
The incorporation of artificial intelligence (AI) into anomaly detection addresses the complex and dynamic nature of modern network environments. Traditional methods of identifying outliers often rely on predefined thresholds and rules are increasingly inadequate due to the volume, velocity, and variety of data generated by enterprise networks, including cloud orchestration and management systems and security platforms.
Processing large datasets in real-time
AI requires efficient processing, labeling and categorization of large datasets in real-time, ensuring that organizations can identify and respond to anomalies as they occur, thus maintaining optimal digital system (e.g. network connectivity) performance.
Automated, real-time detection
AI enables automated, real-time detection of anomalies by consistently monitoring and learning patterns so that AI can quickly detect anomalies as they occur. This instant anomaly detection drastically reduces the impact of potential disruptions, providing organizations with valuable time to address the anomaly before it escalates.
Effective pattern recognition
One primary reason AI works in anomaly detection is its capability in pattern recognition, thanks to a variety of machine learning (ML) techniques that are part of its architecture. Large datasets, such as the ones generated from an enterprise network, exhibit complex behavior that traditional systems may struggle to identify. AI-powered solutions that rely on an effective data architecture, however, excels in recognizing patterns, learning from them, and accurately identifying any deviations or anomalies.
Proactive anomaly management
Traditionally, anomalies were handled reactively, often after a disruption had already occurred. AI-powered systems change this approach, enabling a proactive methodology to detect anomalies and predictive maintenance techniques to resolve via closed-loop automation. AI’s predictive capabilities can forecast potential anomalies based on observed changes in patterns, helping organizations prevent disruptions from impacting their network service.
How does anomaly detection work?
Anomaly detection works through the identification of items, events, or observations that raise suspicions by differing significantly from the majority of the data. The specifics of how anomaly detection works often depend on the techniques being used, but most methods involve these general steps:
1. Data collection and preprocessing
The anomaly detection journey begins with the collection of relevant data. This could be a variety of data, such as network logs, sensor outputs, or transactional records. Once collected, this data is preprocessed – a process that involves cleaning the data and making it suitable for analysis. This robust dataset is the foundation on which AI builds its understanding of ‘normal’ behavior.
2. Feature selection
With the data prepared, the next step involves distinguishing the informative attributes or ‘features’ in the dataset. These selected features are indicative of behavior patterns. The AI’s focus on these attributes helps streamline the anomaly detection process, reducing noise and enhancing precision.
3. Modeling
The heart of the anomaly detection process lies in creating a dynamic model of ‘normal’ behavior or baseline. This often involves applying machine learning or statistical algorithms to the selected features. The choice of approach – whether it’s a density, clustering, or different methodology – is dictated by the objectives and system requirements of the organization.
4. Anomaly identification
With the model established, it’s used to compare against new incoming data. Any significant deviation from the model is identified as an anomaly. It’s important to note that not all anomalies are indicative of a problem. They may be results of harmless statistical fluctuations. But in cases where they signify underlying issues like errors or fraud, swift recognition is vital.
5. Post-processing
Once anomalies are identified, they undergo a post-processing phase. Here, the anomalies are evaluated and prioritized based on their divergence from normal behavior, potential impact, and the confidence in the detection. This ranking enables organizations to focus their resources effectively.
6. Interpretation
In the final step, the identified anomalies and their rankings are interpreted into actionable insights. Depending on the specifics, this could involve further investigations, corrective actions, or even system improvements to prevent such anomalies in the future. The learnings from this step are feedback to enhance the entire process of anomaly detection.
What are the models of anomaly detection?
There are various models used for anomaly detection. Here are some of the most widely used models:
Statistical anomaly detection models
Statistical models for anomaly detection are grounded in the law of large numbers. These models hypothesize that ‘normal’ behavior can be represented using a stochastic process, an approach that provides a statistical basis for observing patterns. Any deviation from the model might be classified as an anomaly.
Density-based anomaly detection models
Density-based models account for the data distribution within a dataset. The principle here is that data residing in highly dense regions represent ‘normal’ behavior, whereas anomalies are data points that lie in low-density regions. This approach is particularly useful in scenarios where outliers are clearly separated from usual patterns.
Clustering-based anomaly detection models
These models group similar data instances into clusters. Normal data instances belong to a certain cluster, whereas anomalies are instances that can’t be grouped within any cluster or are far removed from their closest ones. This model is ideal for data sets with distinct groupings, such a specific type of location.
Classifier-based anomaly detection models
Classifier-based models are built on the premise of classifying data instances into ‘normal’ and ‘anomalous’. This typically involves supervised machine learning, where the model is trained on labeled data to discern between normal and anomalous behavior. This technique offers a fine degree of control and precision.
Neural network-based anomaly detection models
Models such as Autoencoders are used in scenarios where complex patterns are present or when dealing with high-dimensional data. These neural network models can discern even subtle deviations from the norm, making them a potent tool for anomaly detection.
Time-series anomaly detection models
These models are tailored for time-series data. Popular examples include ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short Term Memory) networks. These models are effective in situations where data is time-dependent, such as network performance monitoring.
Support vector machine (SVM) anomaly detection models
SVM models are capable of constructing boundaries that separate normal instances from anomalies in a high-dimensional space. This makes them particularly effective in handling complex datasets where the distinction between normal behavior and anomalies isn’t immediately apparent.
What are the different types of anomalies?
There are primarily three types of anomalies:
- Point Anomalies or Global Outliers: These are single instances of data that deviate significantly from the rest of the dataset. An example might be a sudden spike in website traffic on a particular day.
- Contextual Anomalies or Conditional Outliers: These are anomalies that are context-specific and can be identified by considering the context of the data. An example might be a lower-than-usual temperature reading in summer, which would not be considered an anomaly in winter.
- Collective Anomalies: These occur when a collection of related data instances is anomalous with respect to the entire dataset, but the individual data instances may not be anomalies. This is common in time-series data. An example might be a sudden repetitive fluctuation in the stock market, where individual stock prices may not be anomalous, but the pattern of fluctuation is.
What are the different techniques of anomaly detection?
Several techniques are used in anomaly detection, each with different applications and advantages. Here are some of the most popular techniques:
Statistical-based techniques
Statistical-based techniques use statistical rules to identify anomalies within a dataset. They generally assume that the data adheres to a typical distribution, and any data point that falls outside a defined range is considered anomalous. Generally, a range of three standard deviations from the mean is used to identify anomalies.
Density-based techniques
Density-based algorithms like Local Outlier Factor (LOF) detect anomalies based on the density of data points within a dataset. Data points residing in regions with comparatively low density are considered anomalies, as they deviate from the norm.
Clustering-based techniques
Clustering methods such as K-means clustering function on the principle of grouping similar data instances. Data points that significantly deviate from a cluster center, indicating dissimilarity, are categorized as anomalies.
Supervised & semi-supervised learning
These techniques leverage historical data with labels indicating ‘normal’ and ‘anomalous’ observations. The implemented algorithm uses these labels to develop a predictive model capable of anomaly detection.
Unsupervised learning
Unsupervised learning algorithms, which include techniques like neural networks and deep learning, independently identify and classify unusual data points. They identify patterns and correlations within the data, freeing them from a dependency on pre-labeled data.
Ensemble techniques
These techniques combine multiple anomaly detection algorithms, creating a single, comprehensive model. The advantage lies in enhanced performance resulting from the amalgamation of diverse algorithms.
Support vector machines (SVM)
SVMs aim to find a hyperplane in a multi-dimensional space that can distinctly classify data points. It is especially effective in scenarios where clear dichotomy exists between anomalies and normal data points.
Time series analysis
Time Series Analysis is a technique designed to examine time-sequence data by identifying anomalies in seasonal patterns or trends. Any sudden changes in recognizable patterns can be flagged as anomalies.
Isolation forest
The Isolation Forest algorithm separates observations by randomly choosing a feature and subsequently selecting a split value between the maximum and minimum values of the chosen feature. The rationale is that it’s easier to separate anomaly observations as they significantly differ from the majority of data.
Autoencoders
Autoencoders are artificial neural networks used to efficiently encode data. When trained to reconstruct ‘normal’ data, they struggle to recreate anomalies, leading to higher reconstruction errors. Instances resulting in these high errors are flagged as anomalies.
Why is anomaly detection important for enterprises?
Anomaly detection is crucial for organizations for everything from operational efficiency to security use cases. Below are a some key reasons why anomaly detection is important for enterprise networks:
Ensuring optimal operations
Anomaly detection plays a key role in maintaining optimal operations within an enterprise. By identifying irregularities in IT infrastructure, such as deviations in network performance, organizations can quickly address or preempt disruptions. Prompt detection and resolution minimize system downtime and sustain the quality of service, creating a seamless network experience for users.
Safeguarding security
Security is a cardinal concern for businesses, both big and small. Anomaly detection presents a crucial defense mechanism in this arena. Unusual patterns or aberrant network activity often signal potential threats or breaches. By identifying such anomalies early, enterprises can stall or prevent damaging security incidents, protecting their systems and data.
Complying with regulations
Regulatory compliance is another area where anomaly detection proves essential. Many industries, particularly the financial and healthcare sectors, operate under stringent compliance rules. Any deviation from these norms, intentional or accidental, is flagged as an anomaly. Recognizing and rectifying these anomalies aids in maintaining necessary compliance and avoiding associated penalties.
Improving customer experience
Customer satisfaction hinges on smooth, efficient operations. Anomaly detection helps ensure that system glitches, operational delays, or practical inconveniences that could negatively affect the customer experience are identified and resolved at the earliest. The enhanced user experience fosters greater customer satisfaction and loyalty.
Optimizing operations
Effective anomaly detection provides valuable insights about performing operations more efficiently. By understanding what constitutes an anomaly, enterprises can streamline their procedures, cutting away inefficiencies and redundancies. This ongoing optimization delivers cost savings and enhances overall operational efficacy.
Proactive problem resolution
Anomaly detection equips enterprises for proactive problem resolution. Predictive analytics can forecast potential anomalies based on established patterns, empowering organizations to anticipate and address issues before they escalate. The switch from a reactive approach to a proactive one enhances operational resilience and improves service quality.
How do you resolve anomalies?
Resolving anomalies largely depends on the type of anomaly and the context in which it appears. However, here are some general steps that can be taken:
1. Analyze and validate
Analysis forms the first step of anomaly resolution. It’s important to cross-verify anomalies across different data sources to rule out measurement or data collection errors. This validation ensures that the anomaly is genuine and merits further investigation.
2. Determine the cause
Once an anomaly is validated, you should delve deeper into the data and associated processes to figure out what led to the anomaly. This could involve examining related datasets, reviewing procedures, or discussing with team members involved in data collection or system operations. The goal is to pinpoint the root cause of the anomalous activity.
3. Correct the data
If the anomaly has arisen from errors or inaccuracies, correction of the data is required. This might necessitate replacing incorrect values, removing faulty data rows, or even recalibrating your data collection strategy. Correcting the distorted data ensures a ‘clean’ data set, restoring its integrity and usefulness. This should be performed on a regular basis.
4. Update the system or process
Anomalies caused by flaws in a system or a process demand that the flawed aspects be rectified. This could involve system upgrades, process alterations, or even introducing new protocols. Updating systems and processes curtails future anomalies of a similar nature. Outcomes are expected to be of high quality.
5. Build robust anomaly detection
A solid anomaly detection system, preferably leveraging machine learning and AI algorithms, can help signal anomalies early. This is especially vital when dealing with large datasets where manual monitoring is unfeasible. An automated, robust detection system provides constant vigilance and swift notifications of anomalies.
6. Learn and adjust
Every anomaly resolution process is a learning opportunity. Insights gained from dealing with anomalies should be used to tweak your predictive models or detection parameters, making your system more resilient against future anomalies. This continual learning and adjusting is a key aspect of proactive system protection.
AI anomaly detection use cases
Network monitoring
In the realm of network monitoring, AI-enabled anomaly detection plays a vital role. By learning from patterns within network data, AI can rapidly recognize and flag anomalous activity. This efficient identification and reporting streamline network management efforts, ensuring consistent, secure network performance across the enterprise.
Fraud detection
Financial fraud poses a significant risk across key sectors such as banking, insurance, and e-commerce. AI excels at rapidly processing massive volumes of transaction data and spotting anomalies indicative of fraudulent activities. These capabilities enable earlier fraud detection, minimizing financial losses and preserving customer trust.
Healthcare
The healthcare sector can hugely benefit from AI-enabled anomaly detection. Critical health risks can be identified by detecting irregular patterns in patient data. For instance, anomalies in a sequence of heartbeats may signify potential heart conditions that require immediate medical attention. This can lead to more timely diagnosis and treatment plans.
Manufacturing
In manufacturing, AI can scrutinize operations data, detecting anomalies that might indicate a potential breakdown or inefficiency in the production line. This enables proactive equipment maintenance, preventing costly operational downtime and enhancing overall manufacturing efficiency.
Cybersecurity
AI’s role in identifying potential security breaches through detection of anomalous network activity is critical in cybersecurity. Early detection enables swift defensive action, protecting vital data and IT infrastructure from malware attacks or infiltration attempts.
Education
In the education sector, especially within college campus networks, AI can help monitor vast and complex IT infrastructures. Ensuring the smooth operation of these networks is crucial for the delivery of digital education resources. AI-enabled anomaly detection can swiftly identify and resolve network vulnerabilities or points of inefficiency, ensuring a seamless experience for students, educators and staff.
Challenges of anomaly detection
At the heart of network operations, the aim is to ensure uninterrupted connectivity and security. However, the complexity of modern networks, coupled with the diversity of traffic patterns and the proliferation of devices, presents significant challenges.
Anomaly detection systems are tasked with identifying deviations from normal behavior, which could indicate potential security threats, system failures, or performance bottlenecks. The intricacies of these systems, however, can make it challenging to identify meaningful events.
Large volumes of data
One primary challenge is the sheer volume and variety of data that networks generate. Efficiently processing this data to distinguish between benign anomalies and genuine threats requires sophisticated algorithms and significant computational resources. Nile addresses this challenge through its AI networking and closed loop automation, which streamlines the detection process by focusing on known network issues. This allows Nile to offer service level guarantees for network coverage, capacity, and availability to eliminate guesswork.
Complex behavioral patterns
What constitutes normal behavior can vary significantly over time, necessitating continuous learning and adaptation by anomaly detection systems. Nile’s agile innovation model, powered by cloud-native software, enables the network to seamlessly adapt to changing patterns, ensuring that anomaly detection mechanisms remain effective over time.
False positives
False positives can lead to unnecessary alerts, wasting resources and potentially desensitizing IT teams to genuine threats. Nile’s approach minimizes false positives by leveraging a unified data architecture that enhances the precision of anomaly detection, ensuring that IT teams can focus on true issues without being overwhelmed by erroneous alerts.
Implementation and fine-tuning
Implementing effective anomaly detection must contend with the need for balance between sensitivity and specificity. Too sensitive, and the system may generate excessive false positives; too specific, and genuine anomalies may go undetected. Nile’s solution, with its built-in observability and proactive testing with virtual and physical sensors, and digital twin strikes this balance adeptly, offering organizations a robust mechanism for identifying and addressing anomalies swiftly and efficiently.
Enterprise AI anomaly detection with Nile
Nile’s Access Service leverages robust AI and machine learning anomaly detection and closed loop automation to help improve enterprise network performance, security, and scalability.
The Nile Access Service offers a seamless network experience that aligns with your strategic business requirements, eliminates network complexity, shares the responsibility for IT team’s success, reduces high up-front capital expense, and handles the challenge of managing and maintaining the enterprise network.
With Nile, you can rest assured knowing network availability, coverage, and capacity are guaranteed. This includes built-in zero trust campus security measures and offers usage-based billing for scalable, flexible consumption.
Discover how Nile can enable a highly resilient enterprise network in your environment.
