Anomaly detection via machine learning is an especially buzzing topic nowadays because of an exponential increase of data generated across industries. The growing amount of data makes it challenging or even impossible to process it timely, error-free, and react accordingly using only traditional mathematical approaches. Moreover, huge amounts of unstructured data of all sorts and formats (image, video, audio, etc.) are gathered and stored uselessly, because there is a lack of motivation or human resources to process it.
This is where machine learning comes in as probably the only way to address data processing issues that are beyond people and mathematical algorithms capabilities. For modern companies, it allows, for instance, thoroughly tracking the use of protective gear in manufactures, identifying unfastened drivers and passengers on the road, detecting violence and criminal behavior on social media, and more. So let’s dive a bit deeper into the topic and see how exactly ML boosts anomaly detection efficiency.
On the most basic level, anomaly detection is the process of identifying data items that suspiciously stand out from among the bunch — rare occurrences, unexpected behaviors, conflicting assets, and other out-of-line elements. Technically, these are called dataset outliers. And their gist is that they may indicate corrupted data parts, undermined secret data, hardware malfunctions, the fraudulent activity of different sorts, and more.
Machine learning-powered anomaly detection is the next level of the traditional anomaly detection routine where ML powers are used to accelerate and smooth out processes. In the long run, the technology allows detecting data anomalies faster, more efficiently, and precisely. As such, properly identified data anomalies can point you towards major system-hindering and business-undermining risks. Depending on the field/niche of application, the technical results of proper, efficient ML-based anomaly detection may include:
An anomaly detection system is quite difficult and cumbersome to design manually. Data generation, storing, and processing are dynamic processes that require a continuous approach as a whole, then there’s also the need to predict potential issues and find ways around them only to re-implement algorithms later on. This is where anomaly detection with machine learning delivers a great solution.
Thorough ML-powered anomaly detection is paramount in industries where the structure, comprehensiveness, and safety of data are the defining points of workflow and operations (which includes practically every existing service provider today). The most vivid anomaly detection via machine learning example is the prevention of suspicious activity and frauds, as well as other instances, including:
To date, Python has remained the most widely used programming language in terms of ML engineering, providing proper ML and math-based libraries. Then, there’s also R, which is best used for data analytics and statistics; Java, C++, and other languages can also be used for similar purposes, but in quite rare instances of anomaly detection machine learning algorithms.
Consequently, it is impossible to manually process huge data structures before they lose their relevant value. And no matter how qualified a specialist you are, humans simply cannot match the preciseness and efficiency of dedicated data processing algorithms.
Specialized ML models can be used to create complex anomaly detection systems that work autonomously without downtime, adapt to data shifts and dynamic instances, and simplify huge dataset handling overall:
In the long, these and other ML-based anomaly detection traits bring the following business benefits:
This brings us to the question — how exactly is machine learning used in anomaly detection routines? There are several techniques. Let’s take a look.
On the global scale, machine learning techniques for anomaly detection are subdivided into two major categories — supervised and unsupervised. These are essentially different. Supervised approaches work based on the preliminary machine education and a specifically composed dataset. In turn, unsupervised techniques are more autonomous, using other, more complex math functions. In more technical terms, supervised anomaly detection models are trained based on the labeled data, while the unsupervised approach uses non-anomalous data as training material, excluding any labeled data. In practice, supervised algorithms demonstrate a higher degree of accuracy and are preferable. However, some tasks can be addressed without utilizing unsupervised algorithms.
In terms of machine learning anomaly detection routines, implicit and explicit ML models can be implemented. These models help set off the categorization of dataset patterns you need to analyze so that all underlying efforts are neat and structured. Thus, we can point out several major ML-based anomaly detection techniques.
Regardless of which algorithms are used to detect anomalies, “correct” data preprocessing is essential for high performance. For this purpose, it uses a data smoothing algorithm, Local phasing method, and data clusterization using Hierarchical Density-Based Spatial Clustering (HDBSCAN) or Affinity propagation (AP). The last one is perfect in hyperparameter selection and automatic partitioning into clusters without specifying their number in advance.
This approach is based on structuring data into standardized forests made out of decision-making trees. This structure is then thoroughly monitored to identify any points that stand out from the crowd over time. The displacement of attention principle is used here where the system is taught to spot out-of-the-ordinary occurrences. The sample data you use as an input for RCF models is partitioned and used to learn further so spot more occurrences.
The Elliptic Envelope algorithm is used to detect outliers (anomalous data instances) in a Gaussian distributed dataset. The crucial thing is, whichever data anomaly detection model is preferred, it must be employed by knowledgeable specialists that have a lot of experience in the field. Contact NIX United if you are looking for such specialists to do high-performance work in the most reasonable terms.
This is a very promising approach because, as opposed to other techniques where detection classifications are built based on previous out-of-line occurrences, neural networks can be more autonomous and self-learn to recognize unseen patterns that weren’t predefined. This helps define new, important anomalies right in the process of anomaly detection.
The most commonly used are Convolutional neural networks (CNN), Autoencoder, and Recurrent neural networks (RNN) such as Long short-term memory (LSTM) and Gated recurrent units (GRU).
In terms of this technique, analyzed data variables gain probabilistic relationships encoded by the Bayesian network. In combination with statistical data analysis, this provides a flexible way to detect anomalies where further anomalous events can be predicted based both on previous instances and on the general knowledge gained by the network.
The fuzzy logic approach is about employing approximate reasoning over precise reasoning, which means that out-of-line data occurrences are treated as fuzzy variables. As opposed to traditional predicate logic, this gives more space for the elaboration of the most fitting solution to a specific situation. This approach is considered quite efficient, especially when it comes to the timely detection of probes and port scans. Yet, it is also pretty resource-consuming, which is its major drawback.
Genetic anomaly detection algorithms are inspired by biological principles of search heuristics. Evolutionary traits such as information inheritance, crossover, selection, mutation, and others are employed here. With this in hand, such algorithms can shape the autonomous system that selects the most proper detection parameters based on many data traffic specifics. For this, classification rules are employed based on particular anomalies, which are then applied to bulk data.
On a side note, we’d like to say that genetic algorithms can be overly time-consuming and complex. This is exactly why at NIX United we rarely employ these algorithms in projects.
NIX United is a seasoned software engineering services provider with a firm focus on advanced approaches and flexibility of collaboration. We can help you kick off the project of any scale and complexity, and achieve the top-of-the-line results that fall in line with your requirements, outrun competitors, and promote the latest, most efficient market practices.
We have had a great experience implementing our data anomaly detection machine learning expertise in the course of the original automotive cybersecurity solution. SafeRide is a provider of AI- and ML-powered cyber anomaly detection and threat prevention solutions focused on granting real-time digital security for vehicles.
vSentry is SafeRide’s application that conveniently manages interconnected microcontrollers and devices via specialized software that works without the dependence on a host computer of some sort. For this application, we had to reinforce the security of processes carried out by connected software pieces, provide a way to efficiently gather in-vehicle data for further anomaly detections, maintain CAN (controller area network) errors, and support handling of intense incoming data streams by the microservice architecture.
The resulting optimizations enabled machine learning in behavior anomaly detection algorithms to detect and prevent cyberattacks in a more cost-effective way.
All-around efficient data anomaly detection is paramount to the stable, safe, and reliable performance of companies that depend on data as a major asset. Especially if you are operating in a field where data frauds are frequent and corrupted data can cost too much. Basically, any digital solution that can be hacked or intruded in any way requires the input of machine learning to detect anomalies and save lots of costs that can be spawned easily by malware attacks.
Not to mention a market reputation, which is pretty much invaluable and, sometimes, impossible to restore. This is why only seasoned professionals should handle anomaly detection implementation. Contact NIX United to get the assistance of experienced specialists with a wide portfolio and a firm grip on the latest tech trends and practices.
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