There’s not a business out there that wouldn’t want to defend itself against fraudulent activities, hacking attempts, and operations disruptions. And with cases of fraud and cyberattacks on the rise—$16 billion was stolen from 15.4 million U.S. consumers in 2017—businesses need to pay attention to solutions that can help them identify this kind of behavior.
Here’s where anomaly detection comes in. Luckily, businesses’ web server logs hold a lot more information than they realize, including vital insights into the behavior of their site visitors, and can ultimately help them uncover malicious activities.
Anomaly detection solutions that are powered by machine learning (ML) can identify different types of anomalies in any given data set, such as unusual patterns, uncommon network traffic, or unexpected behavior—and help organizations understand their root causes, in real time. So, considering all the data that goes unused (between 60% and 73%, according to Forrester) it’s certainly time businesses start putting it to use to analytical use.
By applying this ML technique, businesses can glean insights from within their web server logs, and home in on anomalies that shouldn’t be there. And anomaly detection is a market that’s set to grow with the upward trajectory of big data—the market for solutions is expected to reach $4.45 billion by 2022. Let’s take a look at exactly what an anomaly detection solution can reveal, and why every business that wants to resolve and eliminate harmful activities should be deploying one.
Using data from the web server logs, an anomaly detection solution creates a baseline for what is considered normal server traffic after performing server traffic analysis. So, deviations from this baseline are flagged as anomalies by the system, with the ML algorithm training itself on historical data to identify and detect anomalies, then applying this to real-time data.
If the number of server requests (i.e., the total number of unique IP addresses that made requests to the server) or the number of bytes transferred from the server per second are higher than the baseline, an anomaly is detected.
Fraudulent activity could be occurring if the solutions detect activities such as new sites from different IP addresses making focused efforts to reach the server—a classic example of a denial-of-dervice (DoS) attack. Not only this, but cyber espionage could also be occurring if the solution detects a known site transferring huge amounts of data. Businesses need to know about these types of anomalies sooner rather than later—before any real damage is done to their operations.
Letting such attacks go unnoticed and unaddressed can cost businesses heavily, along with putting external confidence and company morale at stake when they are eventually uncovered.
Shockingly, more than half of U.S. businesses were hacked during 2017. Hacks cost businesses thousands of dollars in investigation and replacing or restoring lost hardware, so it’s something they want to avoid at all costs. An anomaly detection solution can drastically help in reducing the likelihood of serious damage being done by potential hackers. The solution creates a baseline of the most popular pages of a website, while also keeping track of web pages that have accessibility issues in order to prevent attempted hacks or DoS attacks.
Deviations from this baseline are considered anomalies, and could suggest harmful activity. For example, if uncommon pages are suddenly being accessed more than common pages, it’s possible that the website is facing a DoS attack to slow the site down or bring it down entirely.
Predict failure probabilities
Most anomaly detection solutions also have the intrinsic advantage of being able to predict possible intrusions, failures or outages in the future. As they learn to detect what constitutes an anomaly in any given data stream, they can define patterns and over time, learn what data parameters contribute to the formation of such patterns.
From there, it becomes easy to extrapolate and predict future intrusions, failures or outages. Machine Learning algorithms are ideally suited for such predictions. These kind of predictive capabilities provide a huge benefit to online businesses and can potentially save them thousands of dollars in preventative maintenance.
Right now, there’s an abundance of solutions on the market, with many leading technology providers developing their own.
There are also a number of open source solutions that give companies with limited resources the opportunity to enhance their anomaly detection capabilities. However, it’s important to note that some of these aren’t readily-packaged and user-friendly, requiring the user to setup the entire data platform and connect the ML algorithm to it. Even some of the most popular OSS solutions require this kind of technical understanding.
However, more user-friendly commercial OSS anomaly detection solutions are available too. Commercial solutions enable anomaly detection and behavior analytics, giving users comprehensive access across all enterprise data using a scalable platform. Often, the package is free and quickly installable, and offers additional services and support at an extra cost.
These are just a few options of many, so it’s certainly worth businesses conducting research to find the right solution for them amongst the countless that are out there. Other helpful features to look out for include real-time alerts, a configurable dashboard, and integration capabilities with existing systems.
Considering all the malicious activity an ML-powered anomaly detection solution is able to identify, along with the availability of OSS solutions, there really is no reason for businesses not to deploy a solution today. Once such anomalies are detected, organizations can take pertinent actions in real-time to prevent high-risk activities and improve operations overall. An anomaly detection solution should really be essential for every business operating online.