This post is the fifth entry in our series on AI application patterns across multiple industries. In this post, we will discuss how AI and machine learning are helping analyze massive amounts of data to find meaningful patterns and anomalies.
There is truth in the belief that the Industrial Internet of Things (IIoT) will someday transform the world. Until now, the proliferation of data is what has made the universe spin faster. However, big data piling up in often unmanageable data sets rendered the Creme de la creme into a challenge, forcing companies and organizations to make a quicker, real-time decision.
An approach for processing data more quickly and effectively is to identify unexpected events, updates or changes in datasets. Anomaly detection, a technology that depends on artificial intelligence (AI) to recognize irregular activity within the captured data set, has, therefore, become one of the Industrial IoT’s key objectives.
Anomaly detection involves detecting objects or occurrences that do not adhere to a pattern predicted or certain objects in a database that are typically impossible to detect by a human expert. Generally, these anomalies or irregularities may be converted into problems such as fraud, errors, and design defects. The following are examples of some potential anomalies that organizations may come across:
These are just a few of the hundreds of potential anomalies that make anomaly detection critical for organizations today. Let’s dig a little deeper into why detecting anomalies is critical for businesses today.
More and more organizations today are starting to realize the value of integrated processes to get a complete picture of their business. In addition, they have to respond quickly to constantly evolving data changes, particularly in the context of cyber-security risks. Anomaly detection may be the best way to tackle these breaches, as disruptions in normal activity, when detecting abnormalities, signify the existence of expected or unforeseen triggered interventions, errors, defects and so on.
Sadly, there is no efficient way to physically manage and evaluate ever-increasing datasets. With the adaptive environments having multiple components in incessant motion in which the “natural” behavior is continuously reframed, a new measured approach is required to recognize anomalous or unusual behavior.
In times where human resources are not adequate to manage the dynamic world of containers, microservices, and cloud computing, AI comes in and provides assistance in several ways to help with anomaly detection. These ways or the anomaly detection tasks for AI/machine learning are discussed later in this article.
Detection of anomalies involves a technique that detects unusual occurrences or phenomena that can raise suspicions by being objectively distinct from the rest of the phenomena. This “anomalous” activity usually converts into a problem such as cyber-attack, server of failure, credit-card fraud, etc. An anomaly can essentially be divided into the following three groups or categories.
A tuple is considered to be a point anomaly if it is far from the other data in the same database. In simple words, point anomaly is where just one instance of data is anomalous as a result of being extremely far from the rest. An example of point anomaly is credit card fraud detection based on ‘money that is spent’.
An occurrence is a contextual anomaly if it is a phenomenon attributable to the observation background. The anomaly is specific to context. In time-series data, this sort of anomaly is normal. Real-world example: It is common to spend a hundred dollars per day on food during the holiday period, but otherwise it may be unusual.
A series of occurrences of data help locate an anomaly. This is referred to as a collective anomaly. Real-world example of this would be a person suddenly attempting to import data from a remote computer to a local host, a phenomenon which would be flagged as a possible cyber-attack.
Identification of anomalies is comparable to — but not quite the same as — removal of noise and identification of novelties. For instance, novelty detection is associated with detecting a non-observed trend in new findings that are not part of the training data — like, for example, an immediate fascination in a new YouTube channel around the holiday period.
On the other hand, Noise reduction (NR) is the method of inoculating data from the emergence of unintended inferences; in other words, the elimination of noise from a signal otherwise important. It was important to make this distinction because a lot of people use anomaly detection, novelty detection, and noise removal interchangeably, as if they are the same thing, which is not the case.
In data mining, the discovery of objects or occurrences that do not adhere to an anticipated trend or to other items found in a database is referred to anomaly detection. Such anomalous objects usually can be converted into issues of some kind, such as frauds, errors, and structural flaws. The use of machine learning for identification of anomalies can help to increase the detection speed.
Anomalies are those actions that can harm the information networks. Detection of these intruders has gained widespread interest in recent years. Anomaly detection may be a key to solving breaches, because when identifying abnormalities, common behavioral disruptions suggest an existence of deliberate or unintentional errors, flaws, triggered attacks etc.
The implementation of machine learning algorithms can provide an easy but efficient approach to identifying and categorizing these anomalies for businesses. Machine learning (ML) algorithms are capable of learning from data and of predicting based on that data. ML for identification of anomalies uses techniques that provide a viable approach to detecting and classifying anomalies based on an initial broad feature set. The following are two machine learning techniques for the effective identification of anomalies.
To generate a predictive model to identify potential data points, this approach includes a classified database comprising normal as well as anomalous samples. K-Nearest Neighbors Classifier, Help Vector Machine Learning, Neural Networks are the most widely used algorithms for this.
K-nearest neighbor (k-NN) is among the most common nonparametric methods used for the identification of anomalies in supervised learning. It measures the estimated intervals on the input variables between various points and then attributes the unmarked point to its K-nearest neighbors class. The Bayesian network is another common model, capable of encoding stochastic relationships between interest variables. This method is commonly used in conjunction with statistical methods for the identification of anomalies.
This approach involves no training data and considers two things about the data instead, i.e. just a tiny fraction of data is anomalous and every deviation is objectively different from the usual observations. The data is then grouped using a correlation measure based on the above premises, and the data points which are far away from the group are deemed anomalies.
Premised on these two principles, data groups of identical occurrences that frequently occur are believed to be regular traffic, and those data groups that are uncommon are deemed hostile. The most popular unsupervised algorithms are single-class support vector machine, self-organizing maps (SOM), adaptive resonance theory (ART), K-means, meta-algorithm (EM) expectation-maximization, C-means. The Self-organizing Map (SOM) is a commonly used technique. The main goal of this technique is to lower the visualization aspect of the results.
There are several things, when a person drives, that are easily noticed and then overlooked. Those issues that may be a threat are what attract attention. Special attention needs to be paid to an approaching car driving into the same lane, a light turning yellow, a pedestrian who could go out on the road, and so on. They’re exceptions, the out of ordinary things. The same holds in the business computing world. A sudden rise in revenue, for example, is fantastic, but the business needs to trace the anomalous rise back to its source to ascertain and reproduce the cause.
The rise of social media, mobile devices, cloud computing, and e-commerce has caused an abundance of data accessible to businesses as well as an increase in business threats. Cyber-attacks are one of the most noticeable threats. There’s a massive amount of transactions hitting the firewall and servers of an organization each day.
Although there may be a threat only to some transactions, those threats may risk millions of dollars in revenue. The large transaction volume makes it difficult for an individual to monitor and act promptly on marked anomalies. However, anomalous details must be identified fast to take effective steps for both risks and rewards accurately and fast. Here is where it can be useful to leverage AI and its kin, machine learning (ML). The following are the ways AI and ML can help organizations in detecting anomalies.
Anomaly detection algorithms driven by AI can examine the datasets automatically, seamlessly adjust the typical behavior variables and detect pattern breaches.
AI applications will analyze the behavior of the data in real time. The instant the machine doesn’t understand a pattern, it sends out a signal.
AI-driven algorithms are the foundation of self-learning systems which can learn from patterns of data and provide forecasts or answers as needed.
AI improves anomaly detection accuracy to prevent disruptive warnings and false positives / negatives caused by fixed limits.
Detection of anomalies is all about identifying interest trends (deviations, idiosyncrasies, outliers etc.) that diverge from anticipated activity within dataset(s). Like in most data science ventures, an algorithm or operational model is not the ultimate objective or anomaly detection output. It’s all about the perspective outliers give, instead. For a company, that is, saving money from avoiding loss of revenue due to fraudulent transactions, by avoiding damage to assets, etc.
Anomaly detection systems include end-to-end disparity-free tracking to scrutinize data and locate the slightest anomalies that human beings would not find.
The seventh and final anomaly detection task for AI/machine learning on our list is fraud detection. There are three reasons why emphasizing AI for risk reduction makes sense. Firstly, it is expensive to fail to identify fraud and laundering of money. In the year 2019, authorities in the US and UK issued fines amounting to $8.14 billion for breaches of money laundering.
Secondly, effective risk management systems bring customers at ease and boost the company’s credibility. For instance, customers are more inclined to use companies with a track record of stopping cyber-attacks.
Thirdly, by using AI to automate cyber -security practices, fraud detection, and certain enforcement, companies can realize substantial savings in time and money. This needs considerable efforts from humans today, and existing anomaly detection systems based on rules leave much to yearn for.
Machine learning techniques are now attracting significant attention from researchers in the field of anomaly detection to overcome the shortcomings of information-based techniques. Detection of anomalies will help to identify fraud efficiently and uncover suspicious behavior in large and complicated Big Data sets.
It can be valuable in areas that are vulnerable to subversive activities such as marketing, medicine, natural sciences, and banking security. Additionally, a learning organization may use the computer to enhance search and grow the efficacy of its digital enterprise efforts. In the next article in this series, we will look at how AI and machine learning can help find the optimal solution through iterations of trial and error.
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