Anomaly detection refers to the process of recognizing certain data, behaviors or events that are not according to an established baseline or norm. In cybersecurity, this technique is used to flag suspicious activities that may signal potential threats, such as malware, intrusions, or system malfunctions.
The system works by creating a baseline, that is, a model of normal behavior, which is then used as a reference to spot unusual deviations, known as anomalies. These anomalies could range from minor system errors to signs of malicious activity. Sometimes, however, systems may produce false positives (incorrectly flagged anomalies) or false negatives (missed anomalies), which are important challenges in tuning anomaly detection systems.
There are several types of anomalies that cybersecurity systems are designed to detect. Point anomalies occur when a single data point deviates significantly from the norm (for example, an unusual login time). In contrast, contextual anomalies are deviations that are abnormal only within a specific context - like an unexpected spike in network traffic. Collective anomalies involve a series of related, unusual data points that together may indicate a larger threat.
Traditional security methods are based on known signatures, but these are useless when a new threat appears, at least until an update is done to the database. Anomaly detection has filled this huge gap in early threat detection because systems no longer have to rely on a set list of signatures. Through a thorough analysis of various data patterns and behaviors that are not normal, a breach can be identified at a very early stage. AI and machine learning are added to the systems so that they can analyze large data quantities in real-time.
Anomaly detection also prevents data breaches. By monitoring user behavior, network traffic, and system activity, these systems can pick up on subtle indicators of compromise that would otherwise be unnoticed. For example, they can flag unusual data access patterns, unexpected spikes in outbound traffic or atypical user login attempts which could be a breach in progress or about to happen.
Anomaly detection helps improve overall security posturing by giving valuable insights into the digital environment. It helps security teams understand normal operational patterns so they can distinguish between benign anomalies and real threats. This contextual awareness is key to reducing false positives and allowing security teams to concentrate on the most critical issues.
At its core, anomaly detection systems analyze historical data, establish a “normal” behavior model (a “baseline”), and continuously monitor for any deviations.
When evaluating an anomaly detection system, we use:
Machine learning models (ex. SARIMA) can help in detecting long-term trends and seasonal patterns. This is how the system can distinguish between regular fluctuations and true anomalies.
Statistical methods are the base of many anomaly detection systems. Some common ones are:
Machine learning has changed the game for anomaly detection by allowing more advanced and adaptive detection, which can be broken into:
These techniques excel at detecting both point and contextual anomalies, adapting to evolving patterns in data.
AI techniques, especially deep learning models, are the new frontier of anomaly detection and can detect complex patterns. Among these Autoencoders - a type of neural network - are good at learning to compress and reconstruct normal data. When the autoencoder is unable to accurately reconstruct the input data it typically means that the data point is an anomaly. LSTMs (Long Short-Term Memory networks) are good at detecting anomalies in sequential data like network traffic patterns where the order of events matters. For more complex patterns, especially in image and video data, CNNs (Convolutional Neural Networks) have shown great performance and are a powerful tool for anomaly detection in these domains. These advanced AI models can capture non-linear relationships in data and detect subtle and unknown types of anomalies, but they require a lot of computational resources and large datasets to train.
The choice of technique depends on many factors such as use case, data and types of anomalies. In reality, robust detection systems use multiple techniques and combine the strengths of each to create a more complete and accurate detection framework. This multi layered approach is a more robust defense against threats.
Modern IT environments produce a ton of data across many dimensions, which makes for complex datasets that are hard to analyze. High dimensionality can cause the “curse of dimensionality” where more data points doesn’t mean more information. This makes it harder for the anomaly detection algorithms to tell normal from abnormal behavior. To fix this feature selection, dimensionality reduction (like PCA) and advanced machine learning models that can handle high dimensional data are used.
False positives (when the system flags normal behavior as anomalous) and false negatives (when actual anomalies are missed) can also be a big challenge. High frequency of false positives can lead to alert fatigue, leading to real threats passing through. Finding the right balance is the only solution: tune the detection thresholds, have a layered detection system and continuously refine the anomaly detection models.
Scalability is another big challenge as organizations grow and their data grows. Anomaly detection systems must be able to process and analyze ever increasing data in real time without compromising on accuracy or speed. This means using distributed computing architectures, complex data processing and optimized algorithms for large scale data analysis.
To fix this, you need to invest in good data management practices, adaptive machine learning models, and scalable infrastructure.
Anomaly detection is moving fast with the help of cutting edge technologies like AI and ML and their integration with modern security solutions. Recent advances in AI and ML have given us more powerful and accurate detection algorithms. Deep learning models especially those using neural networks like LSTM and CNN are becoming more common. These models are great at capturing complex patterns in high dimensional data and can adapt to changing threat landscapes. Reinforcement learning is also getting popular, allowing anomaly detection systems to get better over time based on feedback and outcomes.
New technologies are adding capabilities to detection systems. For example, behavioral analytics with anomaly detection is helping to identify subtle context-based anomalies. This approach looks at not just individual data points but the broader context of user and system behavior. Anomaly detection with modern security solutions is creating a more complete and responsive security environment. Anomaly detection with Extended Detection and Response (XDR) solutions is enabling threat analysis across multiple layers of security. Also cloud native anomaly detection solutions are gaining traction, providing scalability and real time processing. Cloud based systems can analyze huge amount of data from multiple sources, giving a more complete view of threats across an organization’s entire digital footprint. Edge computing is also at play, so you can detect in real time at the source of the data, and reduce latency and response time.
Bitdefender uses anomaly detection across the GravityZone platform, layering it in multiple layers to protect against advanced threats. Anomaly Defense in GravityZone is a dedicated layer that uses custom machine learning models trained on each customer’s environment. It detects anomalies in user, process and system behavior, correlates observed behavior with MITRE ATT&CK indicators to detect threats.
Process Protection uses anomaly detection to detect malicious processes based on their behavior even if the threat is unknown. By setting a baseline of normal process activity, Process Protection can detect deviations that might be malware or ransomware and respond quickly.
Bitdefender uses custom ML models trained on each customer’s environment, adapting to changes in behavior to improve detection. HyperDetect, a tunable machine learning layer, uses anomaly detection to detect fileless attacks and exploits. It analyzes command lines, scripts, and network traffic to detect unusual patterns and behavior and stops attacks before they execute.
Endpoint Detection and Response (EDR) continuously monitors endpoint activity and correlates events to detect subtle indicators of compromise, like unusual data access patterns or atypical user behavior. EDR helps organizations detect threats that evade traditional security.
The GravityZone Extended Detection and Response (XDR) extends anomaly detection beyond endpoints by incorporating data from networks, cloud environments, identity systems and productivity apps. This approach helps detect complex multi-stage attacks that involve lateral movement or identity compromise.
App Anomaly Detection for Android, advanced threat detection for mobile environments feature uses machine learning to detect suspicious behavior on Android devices, offering a proactive protection against mobile threats.
Bitdefender has recently presented GravityZone Proactive Hardening and Attack Surface Reduction (PHASR) that correlates individualized behavior to known attack vectors. It groups similar users to proactively and continuously adjust security levels based on related identified characteristics, and flags anomalous behavior within the monitored group.
AI anomaly detection uses machine learning to automatically find unusual patterns in data that could be a security threat. Instead of relying on rules, AI learns from past data to build a model of normal behavior so you can detect new or unknown threats in real-time.
Different algorithms are used depending on the data and the type of anomalies you are looking for. Statistical methods like Z-score for simple data, machine learning algorithms like Isolation Forest for complex data, deep learning models like Autoencoders for high dimensional data. Often multiple techniques are used together to improve detection.
An example of anomaly-based detection in action is a data exfiltration attempt. A real-world example is an employee whose credentials are compromised, and an attacker is using their account to pull sensitive company data to an external server. While individual file transfers might appear normal, anomaly detection systems can identify this malicious activity by recognizing a sudden, abnormal spike in outbound data transfers from that specific workstation. The anomaly from the normal behavior baseline triggers an alert so security teams can investigate and stop the breach before it's too late. This is an example of threats that traditional security can't detect, especially when attackers use legitimate credentials or new techniques.