With advancements in technology, Monitoring Tools, cybercrimes have become a major threat to organizations. Numerous advanced systems are present to keep your firm’s data secure. But nowadays, criminals are leveraging the power of advanced tools and techniques to access your data and manipulate it. Along with technology, criminals can also use your workers to pose threats to your firm’s digital framework.
To mitigate such threats, you need to leverage the power of continuous security monitoring tools. These tools will not only help in keeping the different malware threats by criminals away but also analyze the behavior of your employees as well. This behavioral analysis will help avoid insider threats or phishing attacks.
However, the main point is how continuous security monitoring tools will do behavioral analysis. Don’t worry! We will guide you in this regard. Today, we will tell you about some top techniques employed in behavioral analysis by CSM tools. Let’s delve into the details of these techniques.
Table of contents
Techniques for Behavioral Analysis by CSM
The following are some techniques adopted by CSM tools for behavioral analysis.
Signature-Based Detections
Behavioral analysis techniques have become very advanced after the introduction of different tools. Still, signature-based detections are an efficient way to analyze the behavior of your employees. This technique is based on the already-known signs of phishing attacks or malicious activities by workers. If your firm hasn’t faced an insider threat yet, then it will be based on predefined signatures.
This technique involves the comparison of current behaviors against known or predefined signatures. If the tool finds similarities, security teams will be informed about it. It’s a less dynamic technique and is often used to detect well-established insider threats.
User and Entity Behavior Analytics (UEBA)
This technique forms the basis of behavioral analysis by continuous software monitoring tools. The CSM software will analyze the regular behavior of all workers in your firm and make baselines for them based on the analysis. Then the main behavioral analysis using UEBA starts. The tool keeps an eye on the behaviors of all your employees.
If the behavior of an employee deviates from the baseline, security monitoring tools will alert the security teams about it. The deviations can be of different types. For example, if one or more employees suddenly try to get access to a particular data or show irregular logins to the company’s system, the tool will get alerted.
Machine Learning Algorithms
The next technique employed by continuous security monitoring tools for behavioral analysis is leveraging the power of machine learning models and algorithms. These models and algorithms can be anomalies with a vast dataset. They can autonomously learn about the behaviors in a system or firm and adapt them.
After that, statistical models and algorithms are applied. They can easily detect the deviation from the normal pattern of behaviors. For example, if you use a clustering algorithm for grouping different behaviors, it will detect and tell you about unusual activities.
Heuristic Analysis
Your firm may have seen some phishing attacks or insider threats. If you have just started the firm or have not experienced such an attack, you can take the example of other organizations that have experienced this. It will form the baseline of Heuristic analysis. This technique will involve defining different rules, regulations, and thresholds based on the previous risks.
These rules and regulations can help in detecting malicious activities by your workers. For example, you set a rule that multiple failed login attempts or unusual data transfers should be detected by continuous security monitoring software. This technique allows the monitoring tool to detect and report red flags.
Statistical Analysis
Statistical analysis is one of the most underrated techniques used by continuous software monitoring software for behavioral analysis. The software will set columns of normal activities. Then this statistical data is compared with the activities of employees to detect deviations from norms.
For example, the frequency of certain activities such as logins to a certain system, accessing specific data, flagging instances, etc. are noticed by the software and then compared against the norms. Any deviation is reported immediately.
Bottom Line
After going through the aforementioned technology, you must have realized that continuous security monitoring software can do behavioral analysis efficiently. If you too want to keep your firm protected from phishing attacks, leverage the power of a robust CSM tool immediately.
