The Internet of Things has somehow changed the world. According to the stats, there were 26.66 billion of active IoT devices in August 2019. Also, every second, 127 new IoT devices get connected on the web. The ever-increasing number of IoT connected devices have spin the world at a fast rate. It is due to the presence of abundant data that makes the world move at a quicker speed.
One way to process the data faster and more efficiently is to detect abnormal changes, events, and shifts in the datasets. Therefore, anomaly detection technology is used that depends on Artificial Intelligence to recognize strange behavior in the pool of gathered data, which has become a fundamental objective of the industrial IoT.
To further aid our readers regarding the anomaly detection system, we’ve compiled some important information for them. Later, the article will also highlight how to train and teach the anomaly detection system to correlate with abnormal behavior. Let’s read on.
What Is Anomaly Detection?
Anomaly detection is the process of identifying items or events that do not follow any expected pattern or other things in a dataset that are usually undetectable by human intelligence. Such anomalies can be translated into problems like structural errors, defects, and frauds.
Modern business enterprises and organizations now understand the importance of interconnected operations to get the full picture of their business. Besides this, they also need to reply to the fast-moving changes in data quickly, particularly in the case of cybersecurity threats. Anomaly detection plays a crucial role in solving such interferences, along with detecting anomalies. The worries of normal behavior show a strong presence of intended or unintended induced attacks, errors, and faults.
However, there is no practical way to handle and analyze the continuously growing datasets manually. With the vibrant systems which have numerous elements in perpetual motion where the normal behavior is redefined continuously, a new active approach to recognize the abnormal behavior is required.
The abnormal data trends rarely occur on their own, but the influences of other related metrics are involved. Specific systems might show you one of these anomalies, which might leave you to search for some other affected parameters that can take hours, days, and weeks too. On the other hand, correlation also lists the related anomalies so you can quickly understand what’s the leading dimension is and which metrics are affected.
Here it is important to remember that if you don’t use anomaly detection, you won’t understand the cause of your outage until any support crew reaches the relevant site. But with anomaly detection, you can quickly discover the related anomalies and make it much easier to get back online.
Examples of Possible Anomalies
The examples of possible anomalies include:
- Fraud detection during financial transactions
- A leaking connection pipe that takes to the shutdown of the entire production line
- Several failed login attempts that show the possibility of any doubtful cyber activity
Finding Related Anomalies and Metrics
The behavioral topology learning offers a method for data scientists to understand the relationship between millions of metrics at scale. By doing so, they combine the related anomalies into stories, reduce errors, and analyze their root causes. But, when implemented correctly, this system can easily filter out all unrelated metrics from the results to achieve higher accuracy.
Correlating Concurrent Anomalies
A significant problem with anomalies is that a single problem or issue will display several abnormalities. Like for example, a DDoS attack might show an increase in both average latencies and the number of failed connection attempts. A DDoS attack is an attempt to disturb the regular traffic of a target server or network, however, you can prevent such attacks by using a VPN as it encrypts the internet traffic and hides the real IP address.
The automatic anomaly detection system doesn’t know that the two simultaneous anomalies are coming from the same error. It also means that if two metrics have the same defect, it is a coincidence. If this happens twice, it is quite suspicious and if it happens for the third time, the metrics are undoubtedly related.
There are several ways for automated detection systems to detect the abnormal-based similarities in this way. It is found that the Latent Dirichlet Allocation (LDA) algorithm works at its best. It is because it assumes that a single metric can be a part of various groups, enabling it to achieve a higher level of accuracy while describing the relationships between parameters. However, one disadvantage of LDA is that it doesn’t scale as well as other methods. It requires to examine a massive amount of historical data to function correctly.
In data science, having a constant and steady naming convention can often produce patterns of related metrics on their own. For example, the name of one e-commerce metric can get comprised of the name of a web browser, the number of abandoned shopping carts, as well as the location of the users being monitored.
Normal Behavior Similarity
By nature, normalcy is subjective. Most of the metrics look as they have been shaped from random spikes under normal conditions. To discover normality at scale, start using a machine learning detection system that recognizes the patterns out of apparent randomness. Remember, when two metrics possess the same model, they are potentially related.
However, this method often runs into problems when you consider that you can find similar patterns in almost any pair of metrics if you’re trying hard enough. But, if you want to go with a traditional measure like a Pearson correlation coefficient, your first step is to detrend the data. It is essential to do so because then any metrics that are also trending will become falsely correlated within your increased revenue. You must also remove seasonality for the same reasons mentioned above; any two parameters having the same patterns will be regarded as falsely correlated.
You can also try a pattern dictionary approach if you want to face fewer false positives. Every time the series can be divided into several archetypal patterns, like a sine way, a square wave, etc. If any two metrics are sharing the same time, they’re related.
Like the naming, the user inputs is one of the less favorite scientific methods of understanding which metrics are interrelated and which are not. The group of metrics is importantly related if an authoritative user straightforwardly claims that they are related. It can be encoded into the machine learning model. Moreover, if it makes any sense to create a composite metric like the sum of abandoned shopping carts in each country, then only the constituent metrics of that composite metrics is likely to be related.
Locality Sensitive Hashing (LSH)
LSH is an insubstantial computation that tags every metric that an organization’s run and assigns it to a particular group. After the metric is attached, you can run some extra calculations on them.
Naming the groups of metrics is highly accurate; however, it is also a tactic that doesn’t scale to millions and billions of parameters. In the meantime, the algorithm-based methods need either significant time or a lot of computing power. Now, the question is how you will make the work quick and less expensive?
The easiest way is to conquer and divide. For instance, take a billion metrics and divide them into some 100 groups of related parameters. It will leave us with ten groups of 10 million metrics. It is much smaller than one billion, which means that it would be easier to compute the similarities among them.
In conclusion, by combining the algorithms and methods helps in providing a more useful image of your platform. A single anomaly does not point to only one issue or problem. The correlated defects that can be recognized across different anomalous metrics are correct and worth attention. By using the above-discussed parameters, you shall be able to reduce a storm of anomalies into an issue that is easier to solve.