Machine learning algorithms are a powerful tool for detecting anomalies in network traffic structure. In this way they can support the early identification of potential attacks But what is anomaly detection? Anomaly detection works by identifying patterns that deviate from anticipated behavior or normal baseline data, which can be indicative of a threat or cyberattack.
These anomalies could indicate fraudulent behavior or system failures in finance, healthcare, manufacturing, and cybersecurity, for example. By being able to detect these anomalies, organizations can protect themselves from attacks, or stop threats before they even occur, while keeping their data and systems safe. Think of it like an early warning system. Once the alarm is triggered by an anomaly, security teams can get to work, responding proactively to any issue.
Bear in mind that the amount of data that organizations handle is enormous. Network usage continues to grow, which means the volume of information being generated across those networks is also rising. Firewalls and antivirus software are not enough to protect networks.
Sophisticated attackers can infiltrate networks for long periods of time and cause damage.
By using machine learning tools, organizations can flag anomalies or outliers that could be overlooked in large datasets. Unlike signature-based detection, which relies on known attack signatures, these tools rely on meta tags and labels to automatically identify deviation.To be effective though, tools capable of detecting anomalies need high-quality data and models.
All Kinds of Anomalies
There are different kinds of anomalies of course. Three anomalies that an organization should be familiar with are point anomalies, contextual anomalies, and collective anomalies.
A point anomaly is self explanatory. This occurs when an individual data point differs from the rest of the dataset. This could be a spoke in network traffic. An example of a point anomaly could be for example an unusual charge on a credit card, or use of that credit card in an unusual location. That could alert organizations to the possibility that the charge might be fraudulent.
With contextual anomalies, an individual data point is different from the rest of the dataset, but within a specific context. If a user logs into a system during non-business hours or from an IP address that doesn’t match their geographic location, this could be a conextual anomaly.
Collective anomalies occur when a group of data points differ from an anticipated pattern. An example of this could be a surge in network traffic from IP addresses that could be a coordinated attack.
How Can Machine Learning Help?
Machine learning and AI have a big role to play in anomaly detection. They are ideal for identifying patterns in large datasets in real-time, as well as for spotting anomalies. A machine learning model could analyze network traffic patterns and identify deviations, such as surges in volume, attempted intrusions, or suspicious requests, for example. This eye for pattern recognition is key.
Large datasets, such as the ones generated from an enterprise network, exhibit complex behavior that traditional systems may struggle to identify. AI-powered solutions that rely on an effective data architecture, however, excels in recognizing patterns, learning from them, and accurately identifying any deviations or anomalies. Its ability to analyze network traffic in real-time can also improve the speed with which security teams react. For instance, in a network, an algorithm might flag a jump in login attempts. The system could then turn on its automated defenses, such as blocking questionale IP addresses. This helps to address threats.
Machine learning models can adapt to identify new patterns, as they learn as the data is generated. Each time, actually, there is an attack on a network, machine learning models can make related changes by automatically incorporating new attack vectors in their detection algorithms. As such, defenses are expanded. This differs from rules-based models, where responses would have to be adjusted manually with every attack.
They also offer something called proactive anomaly management, in that they might predict where anomalies might occur in the future by observing changes in patterns over time. This is accomplished through closed-loop automation, where machine learning can correct issues as they arise. This happens thanks to a feedback loop of communication between monitoring, identifying, adjusting, and optimizing the performance of the network to enable self-optimization.
Relying on such tools, false positives can also be avoided too, as machine learning can be trained to differentiate between normal changes in network traffic and real security threats. Sometimes the volume of data transfers might genuinely spike compared to baseline behavior, but machine learning will be able to distinguish between normal spikes and suspicious activities.
Finally, machine learning tools are scalable and save users money over time. Rather than hiring big teams to do the grunt work, machine learning models can take over, freeing up time for the security team to focus on higher-level tasks.
Taken together, your cybersecurity efforts will greatly benefit from machine learning-based anomaly detection. It helps to identify unusual network traffic, which could signify a cyber-attack or data breach. Early detection allows for quick responses, minimizing damage. It also improves operational efficiency while assisting with proactive risk management and decision making.
About the Author
Oleg Bondarchuk is a Microsoft Azure Certified Solution Architect Expert with over 18 years of IT experience, including more than nine years of specialized experience in designing and supporting Microsoft Azure environments, also he holds IEEE Senior Member status . He’s proficient in Azure Open AI tools , Azure Ai Studio , Azure DevOps , Bicep, ARM and infrastructure automation using PowerShell, Bash and Python, and has deep expertise in virtual networks, configuration management systems, and implementing high-availability, business continuity, and disaster recovery strategies.
