Anomaly detection for more security in the IT infrastructure

The number of cyber attacks is continuously increasing. This makes it all the more important for companies to strengthen their cyber resilience. Anomaly detection can prove to be a crucial element for more security.

Anomaly Detection in IT infrastructures

Smallest indications are harbingers of danger

Often, cyber attacks begin completely unnoticed. A good example of this is ransomware. Before the data is encrypted and a ransom note pops up, the cybercriminals have often been busy in the background, spying on systems and siphoning off data so that they can optimally prepare the double or triple extortion and gather massive amounts of leverage. In doing so, they act as inconspicuously as possible so as not to draw the attention of their victims too early.

However, they cannot make their presence and actions completely invisible. Those who know how to interpret the smallest clues can detect the criminal activity in the shadows of the IT network and react before anything worse can happen - in the case of a ransomware attack, this would be data theft and data encryption.

Sophisticated defenses help organizations protect themselves from such cyber attacks and strengthen their cyber resilience. A critical component of this is anomaly detection. But what is anomaly detection exactly? How does anomaly detection work? And why is it so important for IT security?

What is anomaly detection?

Anomaly detection (also: anomaly detection or anomaly detection) is inextricably linked to the monitoring of a network. By continuously monitoring the network, appropriate tools have an accurate picture of how the network and each component within it normally behaves. The smallest deviations from this norm or unusual behavior of individual components are thus noticed directly and immediately set security processes and test mechanisms in motion.

The smallest indications can serve as harbingers of potential dangers, which can be prevented by reacting in good time. Possible anomalies that indicate cyber attacks and network problems can be, for example: new network participants, new network connections, changed command structures, unknown data packets or new protocol types.

The German Federal Office for Information Security (BSI) names anomaly detection in the network as an important means of protecting it. The reason is that it not only detects technical errors and incorrect configurations, but is also suitable for detecting unknown forms of attack - unknown malware falls through the cracks of many antivirus and firewall solutions.

How does anomaly detection work?

Anomaly detection tools are designed to observe and analyze the entire network communication and network infrastructure. This results in a comprehensive network mapping that documents exactly which network nodes exist, the connections and relationships between these network nodes, which data packets are usually transmitted, and the frequency of communication on the network.

how does Anomaly Detection work?

The result of this is a detailed inventory of the complete IT infrastructure. In addition, standard patterns can be defined from this. This is particularly important. After all, if there is no definition of the normal state, no atypical behavior can be identified. Logical or statistical approaches can be used to determine which behavior is normal and which is not. Setting thresholds is also important as a basis for anomaly detection.

Often, corresponding tools need some time to create such a network mapping and to reliably detect anomalies. The more knowledge there is about the normal state, the better the anomaly detection works. It registers anomalies in real time and reports them so that a check can be carried out immediately and an appropriate reaction initiated.

Network: Anomaly detection averts dangers

This real-time reporting and the insights and knowledge gained from it can therefore be used to detect unusual behavior - in other words, anomalies. Here's an example. An attacker has managed to infiltrate a yet unknown malware. In the network, it causes one network component to make contact with all other network components, which is unusual behavior for that particular component. Anomaly detection makes this immediately noticeable. A message is issued, which in turn triggers a check. It is possible to prevent the attacker from taking control of individual components or the entire network in good time.

This procedure can also be applied to other cyber threats. For example, anomaly detection can also set off alarm bells if a DoS attack causes a mass of requests to overload individual components of the data network and lead to a failure. Or if unusual data streams to external destinations suddenly appear, which can occur in the event of data theft by cybercriminals. In such cases, it is important to be quick and put a stop to the attackers as soon as possible. Anomaly detection makes it possible.

Not only external attackers endanger the network

As you can see: Anomaly detection can significantly improve the level of protection against hard-to-detect attacks and malware. After all, while firewalls, intrusion detection systems (IDS) and intrusion prevention systems (IPS) only look for already known threats and activities, anomaly detection can also uncover unknown threats.

But it's not just cyber threats that can be noticed and prevented in time by anomaly detection. It can be just as dangerous for enterprise business operations when network problems, capacity bottlenecks or equipment failures become apparent. Here, too, anomaly detection knows how to interpret minor changes in the network's communication streams and alert accordingly. The result: holistic fault prevention and comprehensive digital transparency. As a result, anomaly detection also pays dividends in important tasks such as business continuity and IT compliance.

Conclusion on anomaly detection

After all, the complexity of networks and the volume of data streams are expected to grow in the near future. Tools for anomaly detection are ready for such requirements. And: by means of intelligent algorithms and machine learning, they are getting better and better.

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