What is Anomaly Detection?

There are some really crucial metrics that are valuable in terms of the insights they offer.

Such metrics include user logins, application throughput, network usage and more.

Ironically however, some of these metrics are also the ones that are the most variable, having definite valleys and peaks depending on specific times of a week and because of this, it becomes invariably difficult to set up thresholds for analysis and investigation.

That’s why to add more sense and set contexts for varying metrics like these, we add anomaly detection.

For most of us, this is comparatively a new term and that’s why we’ve decided to break it down in simple language to help you understand what it is and how you could incorporate it in your system.

So, to comprehend things better, let’s start with the basics.

What is an Anomaly and Its Detection?

Anomaly is anything that is not regular – something that stands out or doesn’t belong in a cluster.

Thanks to the implementation of technologies like data science, machine learning and more, we can now measure each and every aspect of our business activity.

From operational performance, infrastructural components to KPIs (Key Performance Indicators), we could use any parameter to assess the status of our business

When you process these datasets, you are exposed to two consequences:

1. You find data patterns that convey nothing extraordinary
2. You find data patterns that are unconventional, do not conform to the other patterns or they deviate from the regular patters you’ve known so far

This deviation is called an anomaly. For instance, consider a video streaming website.

When you process the data sets generated from Monday through Friday, you see data patterns that are regular.

Meaning, regular traffic and watching trends. But when you process the data generated on the weekends, you see an upsurge in the traffic and watching trends.

You realize that more people are streaming back to back movies or are binging on their preferred programs.

The anomaly here is the deviation from the data patterns you observe during the weekdays.

Anomalies could vary from one business to another, one niche to another, one industry to another and more.

Anomaly Detection And Network Monitoring

At any given point in time, a network generates massive chunks of data. These data clusters could be insights on user activities, resource usage, packets, security aspects and more.

Network monitoring and analysis give you comprehensive information on network behaviour, any probable threats and your overall network experience.

When you process and analyze these chunks, you can uncover underlying information that can be used to optimize network performance, tighten security aspects and more.

The implementation of anomaly detection in network monitoring involves the consistent tracking and supervision of your network to detect suspicious threats.

Instead of just looking at endpoints, perimeters and firewalls for threats, anomaly detection sweeps across the entire network in pursuit of possible threats.

When sweeping, the mechanism automatically detects something that is out of the ordinary for prompt investigation.

This could be a sudden upsurge in traffic that is higher than the regular or any deviation from optimal ranges based on past data.

Types of Anomaly Detection

One of the main reasons for data generation and analysis is to empower you with information so you could make informed decisions.

Without this power, you could take irrelevant or unnecessary decisions that would still be data-driven but wrongly inferred.

That’s why it is important to understand the three types of anomalies so you could understand the origin of such anomalies and take corresponding measures.

1. Global Outliers

Global outliers are also referred to as point anomalies. They are called so because these anomalies lie far beyond the entirety of your data set.

2. Contextual Outliers

Anomalies that deviate from other data points prevailing in the same context are called contextual outliers.

Also referred to as conditional outliers, these are anomalies only when you look at a dataset contextually.

3. Collective Outliers

Consider a dataset. When you analyze them and you find that a subset of its dataset is completely different from the entire dataset, the anomaly is called a collective outlier.

Concerns You Could Fix With Anomaly Detection

Anomaly detection could uncover hidden truths about your business and network that you would have otherwise overlooked.

Some of the crucial concerns that you could fix with anomaly detection are discussed below.

Optimizing App Performance

Your app is the bridge that connects your intangible idea that resides in your mind with a tangible product your customers could use.

A poorly performing app or an app that does not get anything done is lethal to your business and your goals.

Monetary losses aside, a badly functioning app could fetch you a negative reputation in the market.

However, with consistent monitoring and the deployment of anomaly detection, you could figure out loopholes in your app’s functionalities and security aspects and fix them even before they are pointed out.

Practical Use Case

Waze is a GPS app that has over 100 million active users every month.

To keep consequences arising due to the app’s performance at bay, Waze had to ensure it detected concerns before they could affect its users.

It decided to fix this by implementing machine learning algorithms to correlate data and its app performance to share incidents its IT staff members could work upon and fix.

Optimize Product Quality

A product is constantly evolving. From the ideation phase to even after it’s rolled out in the market, a product is evolving in terms of its functionalities, security aspects, aesthetics and more.

As far as product managers are concerned, it becomes tedious for them to keep track of the changes that come with every stage of product evolution.

Relying purely (added) on other departments for their alerts and notifications is unreliable and not a good practice

That’s where anomaly detection can pay an important role by pointing out defects and concerns in products at any given point of time.

By defining goals clearly for every stage of product development cycle, the highest level of quality can be ensured.

Key Benefits of Anomaly Detection

Apart from helping you address the concerns we’ve discussed, anomaly detection offers the following advantages when implemented.

1. Monitor and track all data sources such as devices, servers, logs and networks
2. Anticipate and identify threats and zero-day attacks
3. Track and report any unusual behaviour across sources that are not under the radar
4. Uncover rogue users
5. Analyse information from hosts, agents, users and more

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