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Anomaly Detection Market Size 2024-2028

The anomaly detection market size is forecast to increase by USD 3.71 billion at a CAGR of 13.63% between 2023 and 2028. Anomaly detection is a critical aspect of cybersecurity, particularly in sectors like healthcare where abnormal patient conditions or unusual network activity can have significant consequences. The market for anomaly detection solutions is experiencing significant growth due to several factors. Firstly, the increasing incidence of internal threats and cyber frauds has led organizations to invest in advanced tools for detecting and responding to anomalous behavior. Secondly, the infrastructural requirements for implementing these solutions are becoming more accessible, making them a viable option for businesses of all sizes. Data science and machine learning algorithms play a crucial role in anomaly detection, enabling accurate identification of anomalies and minimizing the risk of incorrect or misleading conclusions.

However, data quality is a significant challenge in this field, as poor quality data can lead to false positives or false negatives, undermining the effectiveness of the solution. Overall, the market for anomaly detection solutions is expected to grow steadily in the coming years, driven by the need for enhanced cybersecurity and the increasing availability of advanced technologies.

What will be the Anomaly Detection Market Size During the Forecast Period?

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Anomaly detection, also known as outlier detection, is a critical data analysis technique used to identify observations or events that deviate significantly from the normal behavior or expected patterns in data. These deviations, referred to as anomalies or outliers, can indicate infrastructure failures, breaking changes, manufacturing defects, equipment malfunctions, or unusual network activity. In various industries, including manufacturing, cybersecurity, healthcare, and data science, anomaly detection plays a crucial role in preventing incorrect or misleading conclusions. Artificial intelligence and machine learning algorithms, such as statistical tests (Grubbs test, Kolmogorov-Smirnov test), decision trees, isolation forest, naive Bayesian, autoencoders, local outlier factor, and k-means clustering, are commonly used for anomaly detection.

Furthermore, these techniques help identify anomalies by analyzing data points and their statistical properties using charts, visualization, and ML models. For instance, in manufacturing, anomaly detection can help identify defective products, while in cybersecurity, it can detect unusual network activity. In healthcare, it can be used to identify abnormal patient conditions. By applying anomaly detection techniques, organizations can proactively address potential issues and mitigate risks, ensuring optimal performance and security.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premise


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period. The market is witnessing a notable shift towards cloud-based solutions due to their numerous advantages over traditional on-premises systems. Cloud-based anomaly detection offers breaking changes such as quicker deployment, enhanced flexibility, and scalability, real-time data visibility, and customization capabilities. These features are provided by service providers with flexible payment models like monthly subscriptions and pay-as-you-go, making cloud-based software a cost-effective and economical choice. Anodot, Ltd, Cisco Systems Inc, IBM Corp, and SAS Institute Inc are some prominent companies offering cloud-based anomaly detection solutions in addition to on-premise alternatives. In the context of security threats, architectural optimization, marketing strategies, finance, fraud detection, manufacturing, and defects, equipment malfunctions, cloud-based anomaly detection is becoming increasingly popular due to its ability to provide real-time insights and swift response to anomalies.

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The cloud segment accounted for USD 1.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

When it comes to Anomaly Detection Market growth, North America is estimated to contribute 37% to the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast per

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

Plankton organisms are fundamental components of the earth’s ecosystem. Zooplankton feeds on phytoplankton and is predated by fish and other aquatic animals, being at the core of the aquatic food chain. On the other hand, Phytoplankton has a crucial role in climate regulation, has produced almost 50% of the total oxygen in the atmosphere and it’s responsible for fixing around a quarter of the total earth’s carbon dioxide. Importantly, plankton can be regarded as a good indicator of environmental perturbations, as it can react to even slight environmental changes with corresponding modifications in morphology and behavior. At a population level, the biodiversity and the concentration of individuals of specific species may shift dramatically due to environmental changes. Thus, in this paper, we propose an anomaly detection-based framework to recognize heavy morphological changes in phytoplankton at a population level, starting from images acquired in situ. Given that an initial annotated dataset is available, we propose to build a parallel architecture training one anomaly detection algorithm for each available class on top of deep features extracted by a pre-trained Vision Transformer, further reduced in dimensionality with PCA. We later define global anomalies, corresponding to samples rejected by all the trained detectors, proposing to empirically identify a threshold based on global anomaly count over time as an indicator that can be used by field experts and institutions to investigate potential environmental perturbations. We use two publicly available datasets (WHOI22 and WHOI40) of grayscale microscopic images of phytoplankton collected with the Imaging FlowCytobot acquisition system to test the proposed approach, obtaining high performances in detecting both in-class and out-of-class samples. Finally, we build a dataset of 15 classes acquired by the WHOI across four years, showing that the proposed approach’s ability to identify anomalies is preserved when tested on images of the same classes acquired across a timespan of years.

The dataset is a subset of the Task-2 of DCASE 2020 Challenge. The Challenge is to identify anomaly of a machine using the audio data. There are three different parts of the dataset, namely, training, validation and testing which have been combined into a single dataset.

Training- https://zenodo.org/record/3678171

Validation- https://zenodo.org/record/3727685

Testing- https://zenodo.org/record/3841772

each with 5000 labeled observations.

Overview: ReADS can analyze text reports, such as aviation reports and problem or maintenance records. ReADS uses text clustering algorithms to group loosely related reports and documents, this reduces human error and fatigue. Plus, ReADS identifies interconnected reports; automating the discovery of possible recurring anomalies. ReADS provides a visualization of the clusters and recurring anomalies. ReADS has been integrated into a secure web-based search tool to allow uses to perform their own text mining. Recurring Anomaly Identification ReADS identifies reports which mention other reports as a recurring anomaly using regular expressions to search documents and identify references of other reports by name. ReADS also detects recurring anomalies by determining the similarity between documents using a cosine distance similarity measure. Then according to the similarity measure, ReADS will run a hierarchical clustering algorithm to detect the recurring anomalies. The hierarchical tree is partitioned into clusters by setting a threshold. A low threshold implies that the reports must be very similar to be sorted into the same cluster. Here's more info. The figure below is a screenshot of the clustering results.

There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are collected from modern satellites, in-situ sensors and different climate models. Similarly, huge amount of flight operational data is downloaded for different commercial airlines. These different types of datasets need to be analyzed for finding outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations with only a subset of features available at any location. Moving these petabytes of data to a single location may waste a lot of bandwidth. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the entire data without moving all the data to a single location. The method we propose only centralizes a very small sample from the different data subsets at different locations. We analytically prove and experimentally verify that the algorithm offers high accuracy compared to complete centralization with only a fraction of the communication cost. We show that our algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of the algorithm is demonstrated on two large publicly available datasets: (1) the NASA MODIS satellite images and (2) a simulated aviation dataset generated by the ‘Commercial Modular Aero-Propulsion System Simulation’ (CMAPSS).

Description:

👉 Download the dataset here

The Road Anomaly Dataset (RAD) is meticulously designed to tackle the urgent need for real-time detection and response to dangerous and potentially life-threatening road anomalies through surveillance footage. This dataset captures a wide spectrum of hazardous road-related activities, including but not limited to vehicular accidents, car fires, violent altercations, and armed thefts. The RAD dataset serves as a vital resource for improving public safety by equipping machine learning models to detect such anomalies with precision and speed, ensuring a faster and more effective emergency response.

Motivation

With the proliferation of Closed-Circuit Television (CCTV) cameras in urban and rural areas, there is an increasing demand for intelligent surveillance systems capable of detecting abnormal activities in real-world environments. Detecting road anomalies in real-time is particularly crucial, as it can significantly reduce response times to dangerous situations, preventing injuries, fatalities, and property damage. Traditional surveillance systems, however, often fail to recognize these anomalies quickly enough, leading to delayed emergency responses. RAD aims to address this challenge by providing a comprehensive dataset that can train advanced machine learning models to identify, classify, and respond to road anomalies effectively.

Download Dataset

Dataset Description

The RAD dataset comprises an extensive collection of high-quality images and videos, systematically annotated to reflect a variety of road anomalies. The dataset focuses on four primary categories, each representing a critical risk to human safety and property:

Accidents: Includes footage of car crashes, multi-vehicle collisions, and other traffic-related incidents that pose immediate dangers.

Car Fires: Captures instances of vehicles engulfed in flames, emphasizing the importance of quick detection to prevent further casualties and property damage.

Fighting: Contains videos of physical altercations occurring on streets or roads, which could escalate into severe violence or endanger bystanders.

Armed Robberies (Snatching at Gunpoint): Features videos of robberies and thefts involving firearms, where immediate response is essential for protecting lives and reducing the impact of criminal activities.

Each category in the dataset is annotated with detailed labels to ensure the accurate identification and classification of these anomalies. The annotations include temporal and spatial metadata, allowing for precise localization of the anomalies within the footage. This dataset is primarily aimed at training and testing machine learning models, particularly those using Convolutional Neural Networks (CNNs) and other deep learning architectures for high-accuracy anomaly detection.

Additional Categories and Data Enrichment

To further enhance the dataset's utility, additional categories of road anomalies are being considered, including:

Illegal Parking: Captures instances of vehicles parked in unauthorized or hazardous locations, potentially obstructing traffic or emergency services.

Obstructions on Roadways: Identifies obstacles such as fallen trees, debris, or stalled vehicles, which can disrupt traffic flow and pose risks to motorists.

Pedestrian Accidents: Includes footage of collisions involving pedestrians, which are often more severe due to the vulnerability of those involved.

Moreover, the RAD dataset has been enriched with contextually relevant metadata, such as environmental conditions (e.g., weather, lighting) and road types (e.g., urban streets, highways), further refining the data for more nuanced training applications.

Applications

The RAD dataset is ideal for developers and researchers working in public safety, smart cities, and autonomous vehicle technologies. Its primary applications include:

Surveillance Systems: To improve the accuracy and responsiveness of Al-powered surveillance systems by detecting hazardous road activities.

Traffic Management: To help authorities monitor and manage traffic flow more efficiently, identifying potential threats in real-time.

Autonomous Vehicles: For the training of autonomous vehicle systems, equipping them to recognize and respond to road anomalies during navigation.

This dataset is sourced from Kaggle.

Here are a few use cases for this project:

1. Road Safety Improvement: Government road maintenance departments or highway authorities can use this model to proactively identify and fix road anomalies, thus dramatically improving road safety and comfort for all road users.

2. Autonomous Vehicles: This model could be integrated into the systems of self-driving cars. It would allow these vehicles to accurately detect road anomalies in real-time and navigate around them appropriately, ensuring a safer and smoother journey.

3. Ride-Share Companies: Companies like Uber or Lyft could use this model to gather data on the condition of roads used by their drivers, and then prioritize routes with fewer road anomalies for the comfort and safety of their passengers.

4. Dynamic Navigation and Mapping Apps: Real-time road anomalies detection could be used to update navigation apps like Google Maps or Waze. This would provide real-time alerts about road conditions to users and suggest alternative routes to avoid problematic areas.

5. Infrastructure Maintenance: Urban planners and city maintenance departments could use this model as a tool to monitor urban infrastructure. It would assist in identifying areas requiring maintenance promptly, thus efficiently planning their repair and maintenance schedules.

Anomaly Detection Solution Market Outlook

The global anomaly detection solution market size was valued at USD 3.2 billion in 2023 and is projected to reach USD 9.7 billion by 2032, growing at a CAGR of 13.1% during the forecast period. This market growth is primarily fueled by the rising demand for advanced cybersecurity measures, increasing adoption of IoT devices, and the growing need for effective fraud detection systems.

One of the primary growth factors for the anomaly detection solution market is the escalating number of cyber threats and attacks. Companies across various sectors are increasingly realizing the importance of early detection and response to mitigate the impact of these threats. Anomaly detection solutions, leveraging machine learning and artificial intelligence, offer a proactive approach to identifying unusual patterns that could indicate potential security breaches. This has significantly driven the adoption of these solutions across industries such as BFSI and healthcare, which are particularly sensitive to data breaches and cyber threats.

The Internet of Things (IoT) ecosystem is expanding rapidly, with a growing number of connected devices being integrated into business operations. These devices generate vast amounts of data, which, while valuable, also create new vectors for potential anomalies and security breaches. Anomaly detection solutions are essential in monitoring and analyzing this data in real-time to identify deviations from normal behavior, thereby ensuring the security and integrity of IoT networks. This rising integration of IoT devices is a crucial driver for the market.

Another significant factor contributing to the growth of the anomaly detection solution market is the increasing need for effective fraud detection systems. In sectors like finance and retail, where financial transactions are pivotal, the ability to detect fraudulent activities early can save companies millions of dollars and protect their reputation. Advanced anomaly detection systems, powered by AI and machine learning, can analyze transactional data to identify potentially fraudulent activities, providing a robust defense mechanism against financial crime.

Regionally, North America holds a substantial share of the anomaly detection solution market, driven by the early adoption of advanced technologies and the presence of major market players. Europe is also a significant market, with stringent regulations around data privacy and security driving the demand for sophisticated anomaly detection solutions. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digitalization, an increasing number of connected devices, and growing awareness about cybersecurity threats.

Component Analysis

The component segment of the anomaly detection solution market can be categorized into software, hardware, and services. The software component is the most critical and rapidly evolving segment, as it encompasses the algorithms and applications used to detect anomalies in data. Advanced software solutions, powered by AI and machine learning, are becoming increasingly sophisticated and capable of handling large volumes of data in real-time. This capability is particularly crucial for industries that require immediate detection and response to potential threats or deviations.

Hardware components, although significant, play a more supportive role in the anomaly detection ecosystem. These include servers, storage devices, and networking equipment that facilitate the functioning of anomaly detection software. The demand for robust hardware infrastructure is on the rise, especially in large enterprises that need to process and analyze vast amounts of data generated from various sources. The hardware segment is expected to grow steadily as companies continue to invest in upgrading their IT infrastructure to support advanced anomaly detection capabilities.

Services related to anomaly detection solutions include consulting, implementation, and maintenance services. These services are crucial for the effective deployment and functioning of anomaly detection systems. Consulting services help organizations understand their specific needs and choose the right solutions, while implementation services ensure that these solutions are seamlessly integrated into existing systems. Maintenance services are essential for keeping the anomaly detection systems updated and efficient. The services segment is expected to witness significant growth as more organizations seek expert assistance to optim

This dataset contains synthetic HTTP log data designed for cybersecurity analysis, particularly for anomaly detection tasks.

Dataset Features Timestamp: Simulated time for each log entry. IP_Address: Randomized IP addresses to simulate network traffic. Request_Type: Common HTTP methods (GET, POST, PUT, DELETE). Status_Code: HTTP response status codes (e.g., 200, 404, 403, 500). Anomaly_Flag: Binary flag indicating anomalies (1 = anomaly, 0 = normal). User_Agent: Simulated user agents for device and browser identification. Session_ID: Random session IDs to simulate user activity. Location: Geographic locations of requests. Applications This dataset can be used for:

Anomaly Detection: Identify suspicious network activity or attacks. Machine Learning: Train models for classification tasks (e.g., detect anomalies). Cybersecurity Analysis: Analyze HTTP traffic patterns and identify threats. Example Challenge Build a machine learning model to predict the Anomaly_Flag based on the features provided.

Anomaly detection solutions offer various advanced features to enhance their effectiveness, including:

Real-time Monitoring: Continuous analysis of activity and data to detect anomalies immediately. Automated Threat Detection: ML and AI algorithms automatically identify suspicious patterns and alert security teams. Historical Analysis: Analysis of historical data to establish baselines and improve anomaly detection accuracy. Actionable Insights: Provides detailed reports and recommendations to guide response and mitigation strategies.

Report Coverage & Deliverables Market Segmentations:

Type: Network Behavior Anomaly Detection, User Behavior Anomaly Detection Application: Banking, Financial Services, and Insurance (BFSI), Retail, Manufacturing, IT and Telecom, Others

Type

Network Behavior Anomaly Detection: Monitors network traffic patterns to detect anomalies indicating malicious or suspicious activity. User Behavior Anomaly Detection: Analyzes user activity and behavior patterns to identify suspicious deviations that may indicate compromise or insider threats.

Application

Banking, Financial Services, and Insurance (BFSI): Critical for detecting fraud, money laundering, and other financial crimes. Retail: Identifying abnormal purchase patterns, suspicious returns, and insider threats. Manufacturing: Monitoring industrial control systems and operational technology (OT) networks for anomalies. IT and Telecom: Detecting cyber attacks, data breaches, and malware on IT infrastructure and telecommunications networks.

Anomaly Detection Solution Regional Insights Regional Trends:

North America: Early adopter of anomaly detection solutions due to stringent regulations and high awareness of cyber threats. Europe: Strong market growth driven by GDPR and other compliance requirements. Asia-Pacific: Rapidly expanding market with increasing investment in digital infrastructure and cybersecurity. Middle East and Africa: Emerging market with growing demand for anomaly detection solutions to protect critical infrastructure and financial institutions.

Anomaly Detection Solution Trends

Increased Adoption of Cloud-Based Solutions: Cloud-based anomaly detection solutions offer flexibility, scalability, and reduced infrastructure costs. Integration with SIEM and SOAR: Anomaly detection solutions integrate with SIEM and security orchestration, automation, and response (SOAR) platforms to enhance threat response. Focus on Predictive Analytics: ML and AI are used to predict future anomalies, enabling proactive threat prevention. Advanced Threat Intelligence Sharing: Collaboration between businesses and security vendors to share threat intelligence and improve detection capabilities.

Driving Forces: What's Propelling the Anomaly Detection Solution?

Rising frequency and sophistication of cyber attacks Stricter data privacy and security regulations Increasing adoption of cloud computing Growing awareness of insider threats Technological advancements in ML and AI

Challenges and Restraints in Anomaly Detection Solution

False Positives: Anomaly detection solutions can generate false positive alerts, leading to unnecessary investigations and resource drain. Data Volume and Complexity: Increasing amounts of data from various sources make anomaly detection more challenging. Lack of Skilled Professionals: Finding qualified professionals with expertise in anomaly detection and cybersecurity can be difficult. Cost Considerations: Implementing and maintaining anomaly detection solutions can involve significant costs.

Emerging Trends in Anomaly Detection Solution

Behavioral Biometrics: Using ML to analyze user behavior patterns for anomaly detection. Context-Aware Anomaly Detection: Considering context and environmental factors to improve detection accuracy. Explainable AI: Providing explanations for anomaly detection results to improve trust and understanding. Automate Response: Using ML and AI to automate threat response based on detected anomalies.

Growth Catalysts in Anomaly Detection Solution Industry

Government Funding and Incentives: Governments are investing in cybersecurity research and development, including anomaly detection technologies. Strategic Partnerships: Partnerships between technology vendors and security service providers accelerate adoption. Increased Cyber Threat Awareness: Organizations are becoming more aware of the importance of anomaly detection to protect their assets.

Leading Players in the Anomaly Detection Solution

Cisco Systems, Inc. Dell Technologies, Inc. Hewlett Packard Enterprise Company Guardian Analytics Anodot, Ltd. Happiest Minds Gurucul Niara, Inc. Flowmon Networks Wipro Limited SAS Institute Inc. Symantec Corporation Trustwave Holdings, Inc. International Business Machines Corporation Logrhythm, Inc. Splunk, Inc. Trend Micro, Inc. Greycortex S.R.O. Securonix, Inc.

Significant Developments in Anomaly Detection Solution Sector

Partnerships between leading vendors to integrate anomaly detection solutions with wider security platforms. Investment in research and development of advanced ML and AI algorithms. Acquisition of smaller companies by established vendors to expand their anomaly detection capabilities.

Anomaly detection is a process of identifying items, events or observations, which do not conform to an expected pattern in a dataset or time series. Current and future missions and our research communities challenge us to rapidly identify features and anomalies in complex and voluminous observations to further science and improve decision support. Given this data intensive reality, we propose to develop an anomaly detection system, called OceanXtremes, powered by an intelligent, elastic Cloud-based analytic service backend that enables execution of domain-specific, multi-scale anomaly and feature detection algorithms across the entire archive of ocean science datasets. A parallel analytics engine will be developed as the key computational and data-mining core of OceanXtreams' backend processing. This analytic engine will demonstrate three new technology ideas to provide rapid turn around on climatology computation and anomaly detection: 1. An adaption of the Hadoop/MapReduce framework for parallel data mining of science datasets, typically large 3 or 4 dimensional arrays packaged in NetCDF and HDF. 2. An algorithm profiling service to efficiently and cost-effectively scale up hybrid Cloud computing resources based on the needs of scheduled jobs (CPU, memory, network, and bursting from a private Cloud computing cluster to public cloud provider like Amazon Cloud services). 3. An extension to industry-standard search solutions (OpenSearch and Faceted search) to provide support for shared discovery and exploration of ocean phenomena and anomalies, along with unexpected correlations between key measured variables. We will use a hybrid Cloud compute cluster (private Eucalyptus on-premise at JPL with bursting to Amazon Web Services) as the operational backend. The key idea is that the parallel data-mining operations will be run 'near' the ocean data archives (a local 'network' hop) so that we can efficiently access the thousands of (say, daily) files making up a three decade time-series, and then cache key variables and pre-computed climatologies in a high-performance parallel database. OceanXtremes will be equipped with both web portal and web service interfaces for users and applications/systems to register and retrieve oceanographic anomalies data. By leveraging technology such as Datacasting (Bingham, et.al, 2007), users can also subscribe to anomaly or 'event' types of their interest and have newly computed anomaly metrics and other information delivered to them by metadata feeds packaged in standard Rich Site Summary (RSS) format. Upon receiving new feed entries, users can examine the metrics and download relevant variables, by simply clicking on a link, to begin further analyzing the event. The OceanXtremes web portal will allow users to define their own anomaly or feature types where continuous backend processing will be scheduled to populate the new user-defined anomaly type by executing the chosen data mining algorithm (i.e. differences from climatology or gradients above a specified threshold). Metadata on the identified anomalies will be cataloged including temporal and geospatial profiles, key physical metrics, related observational artifacts and other relevant metadata to facilitate discovery, extraction, and visualization. Products created by the anomaly detection algorithm will be made explorable and subsettable using Webification (Huang, et.al, 2014) and OPeNDAP (http://opendap.org) technologies. Using this platform scientists can efficiently search for anomalies or ocean phenomena, compute data metrics for events or over time-series of ocean variables, and efficiently find and access all of the data relevant to their study (and then download only that data).

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:

4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.

3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.

10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.

5 million timestamps. Sensors readings are at 1Hz sampling frequency.

1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.

4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).

200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.

Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.

Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.

Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.

Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).

Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.

Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.

Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.

No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

Change Log

Version 2

Metadata: we include a metadata.csv with information about:

Anomaly categories

Root cause channel (signal in which the anomaly is first visible)

Affected channel (signal in which the anomaly might propagate) through coupled system dynamics

Removal of anomaly overlaps: version 1 contained anomalies which overlapped with each other resulting in only 190 distinct anomalous segments. Now, there are no more anomaly overlaps.

Two data files: CSV and parquet for convenience.

[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

About Solenix

Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.

Random Walks-based Anomaly Detection (RWAD) is commonly used to identify anomalous patterns in various applications. An intriguing characteristic of RWAD is that the input graph can either be pre-existing or constructed from raw features.

Anomaly Detection Market Outlook

The global anomaly detection market size was valued at approximately $3.2 billion in 2023 and is projected to reach around $9.5 billion by 2032, growing at an impressive CAGR of 12.5% during the forecast period. The substantial growth factor driving this market is the increasing adoption of advanced technologies such as AI and machine learning in various industries to enhance security measures and operational efficiencies.

One of the primary growth factors for the anomaly detection market is the exponential rise in cyber threats and attacks. As businesses and organizations continue to digitize their operations, the amount of data being generated and transmitted over networks has increased manifold. This surge in data traffic presents a lucrative target for cybercriminals, necessitating the implementation of robust anomaly detection systems to identify and mitigate potential threats swiftly. Incorporating machine learning and AI into these systems has further heightened their ability to detect anomalies proactively, thus providing an added layer of security.

Another significant factor contributing to the market’s growth is the increasing complexity of IT infrastructure across various industries. As companies expand their digital footprint, the complexity of their IT environments grows, making it challenging to monitor and manage systems effectively. Anomaly detection solutions offer a way to automatically identify unusual patterns and deviations in system behavior, enabling IT teams to address potential issues before they escalate into critical problems. This proactive approach to system health monitoring is crucial in maintaining operational continuity and minimizing downtime.

Moreover, the growing emphasis on regulatory compliance and data protection acts as a catalyst for market expansion. Governments and regulatory bodies worldwide are enacting stringent data protection laws, requiring organizations to implement comprehensive security measures to safeguard sensitive information. Anomaly detection systems play a pivotal role in ensuring compliance with these regulations by continuously monitoring data activities and alerting administrators to any suspicious behavior. This not only helps organizations avoid hefty fines but also fosters trust among stakeholders and customers.

Regionally, North America holds a dominant position in the anomaly detection market, driven by the early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the rapid digitization of industries, increasing investments in cybersecurity infrastructure, and supportive government initiatives aimed at enhancing digital security. Emerging economies in this region, such as India and China, are at the forefront of this technological adoption, creating a fertile ground for the expansion of the anomaly detection market.

Component Analysis

The anomaly detection market can be segmented by components into software, hardware, and services. The software segment holds the largest share of the market, driven by the continuous advancements in machine learning algorithms and AI technologies. Anomaly detection software solutions are designed to analyze vast amounts of data in real-time, identifying deviations from normal patterns that could indicate security breaches or system malfunctions. These solutions are highly adaptable and can be integrated into existing IT infrastructures with relative ease, offering organizations a scalable approach to security and system monitoring.

The hardware segment, though smaller in comparison to software, plays a critical role in the deployment of anomaly detection systems. Specialized hardware devices, such as network appliances and sensors, are used to collect data and monitor network traffic for anomalies. These devices are essential in environments where high-speed data processing is required, such as financial institutions and large enterprises. The demand for robust and reliable hardware solutions is expected to grow as organizations seek to enhance their detection capabilities and improve overall system performance.

The services segment encompasses a wide range of offerings, including consulting, integration, training, and maintenance services. With the increasing complexity of cyber threats, organizations often require expert guidance to implement and manage anomaly detection systems effectively. Consulting services help businesses

Detecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios. This method is particularly valuable in contexts where labeled data are scarce or labels for the anomaly class are not available, allowing for preliminary insights and detection that can inform further data labeling and more focused supervised learning efforts. We employed fourteen different anomaly detection algorithms and evaluated their performance using Area Under the Receiver Operating Characteristics (AUCROC) and Area Under the Precision-Recall Curve (AUCPR) metrics. Our experiments demonstrated that One Class Support Vector Machine (OCSVM) and Empirical-Cumulative-distribution-based Outlier Detection (ECOD) effectively identified anomalies across different birth weight categories. The OCSVM attained an AUCROC of 0.72 and an AUCPR of 0.0253 for extreme LBW detection, while the ECOD model showed competitive performance with an AUCPR of 0.045 for very low LBW cases. Additionally, a novel feature perturbation technique was introduced to enhance the interpretability of the anomaly detection models by providing insights into the relative importance of various prenatal features. The proposed interpretation methodology is validated by the clinician experts and reveals promise for early intervention strategies and improved neonatal care.

Anomaly Detection Service Market Outlook

The anomaly detection service market size is poised for substantial growth, with its valuation estimated at USD 4.5 billion in 2023 and projected to reach USD 12.8 billion by 2032, reflecting a robust CAGR of 12.4% during the forecast period. The exponential growth trajectory of this market is underpinned by several critical factors, including the increasing reliance on data-driven decision-making across industries, the rising sophistication of cyber threats, and the need for real-time monitoring and analysis. The growing integration of advanced technologies such as artificial intelligence and machine learning in anomaly detection solutions is further catalyzing market expansion by enhancing accuracy and reducing false positives.

One of the primary growth drivers of the anomaly detection service market is the escalating volume of data generated across diverse sectors. With the proliferation of IoT devices, mobile applications, and digital platforms, industries are inundated with massive datasets that require real-time analysis to derive actionable insights. Anomaly detection services provide the capability to sift through vast amounts of data to identify irregular patterns and potential threats, enabling organizations to act swiftly and mitigate risks. Additionally, the increasing focus on enhanced customer experiences and operational efficiency is propelling businesses to invest in robust anomaly detection solutions that ensure seamless operations and prevent disruptions.

The mounting frequency and complexity of cyberattacks have significantly contributed to the demand for advanced anomaly detection services. As cybercriminals employ more sophisticated methods to breach security systems, traditional security measures are often inadequate. Anomaly detection services, leveraging machine learning and artificial intelligence, can detect unusual patterns and deviations from normal behavior, thus providing an additional layer of security against cyber threats. Furthermore, regulatory requirements mandating data protection and privacy have compelled organizations to adopt anomaly detection solutions to comply with standards and safeguard sensitive information, driving further market growth.

Technological advancements and innovations in the field of artificial intelligence and big data analytics are playing a pivotal role in shaping the anomaly detection service market. These technologies enable the development of more refined and accurate detection models that can process and analyze data in real time. The integration of AI and ML algorithms not only increases the precision of anomaly detection but also helps in predicting future anomalies, thereby allowing organizations to take pre-emptive measures. The ability to customize and scale solutions according to specific organizational needs is another factor that is attracting enterprises towards investing in anomaly detection services.

The regional outlook for the anomaly detection service market is characterized by significant variations in growth rates and adoption patterns across different geographies. North America remains a dominant region due to the early adoption of cutting-edge technologies, a strong emphasis on cybersecurity, and substantial investments in IT infrastructure. Europe is also witnessing steady growth, driven by stringent regulatory norms and the increasing focus on safeguarding digital assets. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR over the forecast period, fueled by rapid digital transformation, expanding IT and telecommunications sectors, and increasing awareness about the importance of cybersecurity in emerging economies.

Component Analysis

In the anomaly detection service market, the component segmentation into software and services encapsulates a dynamic aspect of market growth. The software segment is witnessing a significant surge in demand as organizations increasingly seek sophisticated tools capable of real-time anomaly detection. These software solutions, often powered by AI and ML algorithms, facilitate the seamless integration of data from various sources, enhancing overall system efficiency. The burgeoning need for customizable and scalable solutions that can be tailored to specific industry requirements positions the software segment as a pivotal growth driver in the anomaly detection landscape.

On the other hand, the services segment is equally pivotal,

The anomaly detection market is expected to grow at a CAGR of over 6% during the forecast period.The market is driven by the increasing need for organizations to protect themselves against cyberattacks and data breaches.Machine learning and AI are playing a key role in the development of new anomaly detection solutions.Cloud computing is making anomaly detection solutions more accessible and affordable for organizations of all sizes. Recent developments include: HPE launched an HPE Swarm Learning solution in April 2022 to improve AI model training while reducing biases and maintaining data safety. According to the report, this product is a next-generation AI approach that can identify worldwide issues like better patient health with improved anomaly detection, predictive maintenance, and fraud detection., June 2023: Wipro unveils new Microsoft Cloud-based banking financial services; the partnership shall blend Microsoft Cloud capability with Wipro FullStride Cloud, leveraging Wipro and Capco's rich domain expertise in financial services. The aim of this is to develop fresh solutions for helping clients within the financial sector increase their own business and take care of greater client demands., June 2023: Cisco has delivered on its promise of AI-driven Cisco Security Cloud to make cybersecurity simple and enable people to do their best work anywhere without minding the increasingly complex threat environment. Investing in state-of-the-art artificial intelligence (AI) and machine learning (ML) innovations allows Cisco to empower security teams by simplifying operations and increasing efficacy., Jun-2023: Amazon Web Services Inc., a leader in cloud computing, announced an expanded partnership with Lacework Inc., a cloud security provider. This collaboration will make it possible for Lacework customers to strengthen their security alerts and offer them enhanced anomaly detection tied back to the findings of Amazon GuardDuty., May 2023: Amazon Web Services partnered with Elastic, a free distributed search engine for all kinds of data. AWS wants clients using Elastic Cloud on AWS to have a seamless user experience, and that's why they're working together on this project. Additionally, it will aid in global adoption across client organizations during the global cloud adoption journey, thereby enhancing digital transformation efforts., May 2022: IBM Corp. signed a deal with Amazon Web Services to rapidly extend AWS and its suite of offerings, including data and AI, security, sustainability, and automation, to IBM's customers. This agreement brings IBM software such as Data and AI, Security, Sustainability, and Automation within easy reach of the company's clients.. Key drivers for this market are: Growing frequency of cyberattacks, such as fraud and data breaches, is driving the demand for anomaly detection solutions to identify abnormal behavior and mitigate risks in real-time. Potential restraints include: Anomaly detection systems can sometimes flag normal variations as anomalies, leading to high false positive rates. This can result in unnecessary alerts, increased workload, and reduced effectiveness of the system. Notable trends are: The integration of AI and machine learning into anomaly detection systems is enhancing their ability to detect complex patterns, predict potential risks, and improve accuracy over time.

We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. While the algorithms we present are general and domain-independent, we focus on a specific problem that is critical to determining the system-wide health of a fleet of aircraft. The approach taken uses unsupervised clustering of sequences using the normalized length of the longest common subsequence (nLCS) as a similarity measure, followed by detailed outlier analysis to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from the cluster centre. We present new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence is deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence when compared to more normal sequences. In the final section of the paper we demonstrate the effectiveness of sequenceMiner for anomaly detection on a real set of discrete sequence data from a fleet of commercial airliners. We show that sequenceMiner discovers actionable and operationally significant safety events. We also compare our innovations with standard HiddenMarkov Models, and show that our methods are superior.