Introduction to Behavioural Analytics
Behavioral analytics uses machine learning, AI, and big data to identify daily and malicious trends. As a result, security teams may discover typical attacker methods early on, as well as the underlying causes and reasons why they work that way, and get insights for future detection and prediction. Businesses all across the world depend on cybersecurity solutions to keep their data safe.
How does behavioral analytics work?
Step 1: Data collection and transformation: The first step in BA is to collect the necessary data linked to the area of concern and convert it into a proper format for analysis. Among the data sources are:
- Logs of network traffic
- Access records
- Records of database user activities
Data extraction and transformation may now be automated and performed in real-time.
Step 2: Analyze the data: When the data has been translated into the proper format, BA uses unsupervised ML techniques to evaluate it and find differences that depart from usual behavior.
Phase 3: Alerting and preventive action: When the procedure finds an abnormality in the data, it alerts the security teams via an automated alert system. It will send all relevant information to security personnel so that they may take appropriate action.
Constant learning: In addition to the three steps outlined above, BA systems are constantly learning and strengthening their detection capabilities. This continual learning technique enables BA systems to adapt to rising cyber threats.
The Most Important Advantages of Behavior Analytics
1. Identifying and mitigating cyber dangers in advance: BA assists companies in identifying a variety of cyber dangers, such as insiders, persistent attacks, and sensitive data leakages. It identifies sophisticated attacks like advanced persistent threats and zero-day vulnerabilities by using user activity and behaviour analytics across several platforms, allowing for mitigating risk.
2. Recognizing advanced persistent threats (APTs): In businesses, behavioural analytics can be extremely useful in detecting advanced persistent threats (APTs). APTs provide a significant challenge to current security measures due to their unique methods of infiltration and persistence. APTs attempt to gain easier access to a company’s server, which makes detection more challenging.
Behavioral analytics, on the other hand, can assist in detecting the existence of APTs by tracking any odd activity that differs from conventional patterns and behaviours.
3. Responding to threats as quickly as possible: Automatic behaviour analytics systems analyze behaviours in real-time and give warnings when unusual behaviours are discovered. This procedure allows security professionals to respond quickly to address vulnerabilities and prevent the cyber threat from growing or penetrating the system.
4. Taking care of compliance concerns: Since behaviour analytics can identify user activity, companies may leverage that data to detect non-compliant user actions. For example, unauthorized access to consumer data is against security and privacy rules. So, acquiring such data aids businesses in demonstrating regulatory compliance.
5. Avoiding a massive financial loss: Cyberattacks may now cause significant financial damage for businesses. A ransomware attack, for example, can bankrupt a business. Since behavioural analytics allows security teams to detect threats before they happen, businesses are more likely to prevent:
- Avoid such massive financial losses.
- Maintain as much of their cybersecurity plan as feasible.
Behavioural analytics can even detect more sophisticated cyber threats.
Application of Behavior Analytics
1. UEBA: User and entity behaviour analytics (UEBA) is another term for cybersecurity behavioural analytics. UEBA is becoming increasingly popular as a result of significant time and money savings. It can sift through the bulk of a company’s data and generate high-quality leads for security experts to evaluate. UEBA may also reduce the number of cybersecurity experts, which may reduce the pressure on companies to compete for security expertise.
2. Zero-day attacks: The last and most common use of UEBA software is the detection of zero-day threats. Zero-day attacks are new assaults that have not been carried out before and have no detection procedures in place. Since behavioural analysis uses previous behavioural data to establish what is odd, new attacks are commonly identified because they typically use new executables and approaches that go beyond the ordinary to attack a company’s security.
Conclusion
Businesses must use behavioural analytics to improve cybersecurity by identifying hackers based on their behaviour and habits. This method involves tracking users and monitoring for potential insider threats like former employees, rogue employees, or cyber attackers. It includes searching for potential vulnerabilities in the system’s servers, devices, and connected apps.
