As IT services increasingly abstract beyond the traditional server room, computing environments have evolved to become more efficient and complex. Virtualization, mobile device technology, hosted infrastructure, ubiquitous internet, and a host of other technologies are redefining the IT landscape. Obviously, network monitoring is required to keep the network secure and anomaly-free.
Network monitoring tools whose systems are based on anomaly detection become invaluable to the security of corporate networks. Anomaly detection is essential to combat known and unknown security risks that can threaten the stability of a company, which can be financially devastating.
What is Network Anomaly?
Anomalies refer to identifiable values that significantly deviate from common patterns of behavior. Anomalies can be events, items, or observations that do not conform to what we consider to be normal. With the ability to collect various metrics such as data collection, analysis, and data virtualization, it will be easier for IT teams to manage the performance of business activities on a large scale.
Within enterprise data sets, there are data patterns and standards that illuminate common baselines of behavior. These data patterns conclusively demonstrate “standardized” business activities. Any value in the data set that appears to deviate significantly from the standard pattern is considered an anomaly. Often, these deviations indicate critical or unexpected problems.
For example, in credit card fraud detection, an anomaly might be a transaction that does not match the owner’s previous purchase behavior. Meanwhile, in network security, a traffic anomaly might point to an intrusion attempt.
Are Anomalies Always Bad?
Anomaly detection is often associated with use cases such as intrusion or fraud detection and network troubleshooting. So it’s no wonder that businesses tend to focus on detecting negative anomalies. But not all anomalies are bad. There are also positive anomalies.
For example, a video uploaded to social media suddenly goes viral, or a company’s pay-per-click ads do much better than expected. Although this data will distort the company’s report results and can be called an anomaly, the data is positive and has a good impact on the company.
3 Ways Anomaly Detection Improves Network Monitoring
Here’s how to detect anomalies that can improve network monitoring:
1. Understand Network Traffic Behavior
The network monitoring system will analyze traffic patterns at various intersections and time frames, performance baselines, and security can be established, so that potential malicious activities are monitored and managed. However, with so much data traversing the corporate environment at any given moment, detecting abnormal network behavior can be difficult.
Through filtering techniques and algorithms based on live and historical data analysis, anomaly detection systems are able to detect even the most subtle and unlikely malicious software as normal network behavior.
In addition, anomaly-based systems use machine learning capabilities to learn about new traffic as it is introduced and provide greater context on how data traverses the network. By doing so, it can improve its ability to identify security threats.
2. Putting Security Analysts at the Forefront
Anomalies are defined as actions or events that fall outside the norm. However, when the definition of what normal is does not exist, the loopholes can be easily exploited. This is often the case with signature-based detection systems that rely on predefined virus signature databases and are based on known threats.
In the event of a new security threat that is not yet known, signature-based systems will only be effective if they are able to respond to, analyze and neutralize the new threat. Since signatures work well against known attacks, they will not be able to defend the network.
Signature-based systems do not have the flexibility of anomaly-based systems, in the sense that they are unable to detect new threats. This is one of the reasons why signature-based systems are usually complemented by multiple iterations of flow-based anomaly detection systems.
3. Anomaly-Based Systems are Designed to Evolve as Networks Evolve
The main strength behind anomaly detection systems is that they allow Network Operation Centers (NOCs) to customize their security tools according to the demands of the times. With threats becoming more numerous and sophisticated, a detection system that can discover, learn, and provide prevention methodologies is an ideal tool to combat future cybersecurity threats.
Anomaly detection with automated diagnostics does this by using machine learning techniques to detect network threats. By doing so, the system can automate most of the detection aspects of security management while allowing security analysts to focus on the prevention aspects in their ongoing efforts to secure their information and technology investments.
Anomaly Detection Case Example
Cybersecurity remains the most popular anomaly detection case, but the importance of anomaly detection has expanded beyond financial and IT security. Here are some examples of its frequent cases:
1. Fraud Detection
Commonly used to prevent credit card and insurance fraud cases. Graph-based anomaly detection is used to analyze connectivity patterns and detect suspicious behavior. Online banking fraud can also be reduced with machine learning systems through the use of behavioral metrics that flag common spending patterns.
Anomaly detection plays an important role in cybersecurity strategies, so cloud computing platforms like Microzoft Azure, provide anomaly detection services in all their Azure instance types.
2. Network Security
Intrusion detection systems and network behavior anomaly detection technologies play an important role in network security. Their purpose is to detect hidden threats and vulnerabilities in the network infrastructure that will be passed on to network security professionals. Examples include ransomware detection, DDoS attack detection, link failures, suspicious devices, and others.
3. Application Performance and Testing
Anomaly detection has become a fundamental part of every software lifecycle. Application developers can use machine learning algorithms to monitor performance metrics against the norm, which enables quick detection and resolution of bugs, glitches, and other issues. This can also be applied to websites. Anomaly detection and network monitoring influence each other. Make sure to use a reliable network monitoring service so that anomalies can always be detected and dealt with as quickly as possible. Netmonk has been trusted by more than 1000 companies as a network monitoring solution. Find out the details of the service now on the Netmonk web!
