The Role of Behavioral Analytics in Cybersecurity

Behavior Analytics (BA) is a widely used technique that helps you gain insights into various behavioral patterns to make data-driven decisions.

This article describes behavior analytics, particularly how it is used in cybersecurity, and the actions it involves. We’ll also provide describe popular BA tools and discuss their key benefits.  

Defining Behavior Analytics and how it’s used in cybersecurity

In terms of cybersecurity, behavior analytics analyzes large data sets using artificial intelligence (AI) and machine learning (ML) techniques. It allows you to identify unusual patterns that deviate from regular patterns or usage that indicate malicious activities that could potentially stem from cyber attackers. 

BA can reveal unusual patterns such as data exfiltration activities, potential distributed denial-of-service (DDoS) attacks and insider threat behaviors. As such, it’s a valuable security strategy that helps organizations to:

1. Detect security threats in advance.
2. Lay out better security mechanisms to improve overall security posture. 

Behavior analytics can be performed on every connected component of an organization — users, entities, applications, networks and cloud environments. The most advanced BA tools today can provide:

• Real-time insights
• Data visualization in dashboards
• Reporting for security audits
• Alerting capabilities
• Recommendations for improvements of your overall security posture 

Importantly, behavioral analytics applies to all sorts of business needs, from product development and customer service to marketing and sales. But in this article, I’ll focus on the role of behavior analytics in cybersecurity.

(For more security, check out these cybersecurity events & best security certifications to earn.)

How behavior analytics works 

Behavior analytics involves actions that transform data from various data sources into actionable insights. Now, let's see how it works by collecting raw data sources to prevent potential cyber-attacks. 

Step 1. Data collection & transformation

The first step of BA is gathering the required data related to the area of concern and transforming it into a suitable format for analysis. Data sources include:

• Network traffic logs
• Access logs
• Database user activity records

At present, data extraction and transformation can be completely automated and done in real-time. 

(Understand the difference between logs & metrics.)

Step 2. Data analysis

Once the data is transformed into the correct format, BA employs unsupervised ML algorithms to analyze that data and detect anomalies that deviate from the normal behavior. 

(Know the 4 data analytics types your business needs.)

Step 3. Alerting and remediation

When the process detects an anomaly in the data, it notifies the security teams of that behavior using an integrated alert system. It will provide all the required information to the security teams so that they can take necessary remediations.

Continuous learning

In addition to the above three steps, BA systems keep learning and improving their detection capabilities. This continuous learning process allows BA systems to adapt to emerging cyber threats.

Types & features of behavior analytics in cybersecurity

We can categorize behavior analytics into several categories based on which areas your organizations focuses on and needs to perform behavior analytics on.  Here are the most common types of BA used in organizations today. 

User and Entity Behavior Analytics (UEBA)

User and entity behavior analytics (UEBA) focuses on analyzing the behavior of users and entities like devices (routers, servers, etc.) and applications to detect unusual behaviors. Such systems monitor existing user accounts, devices, and applications, analyze their access patterns and issue alerts when there is a sign of compromise. 

Examples of malicious user and entity behaviors are:

• A user account accessed from an unusual device, browser or geographical location indicates a possible account compromise by a bad actor. In addition, multiple login attempts for an account and failed login attempts for a user who does not have any record of failed attempts before can indicate a possible bad actor attempting to access your systems.
• A user who does not have permission to access files, directories, or other resources from a privileged account is trying to access them, indicating privileged account abuse.
• A user who typically does not download large files but downloads small files daily suddenly starts to download large, unusual files. It can indicate that the user downloads malicious software or a file into the system. 
• A user suddenly starts transferring large amounts of data, which could indicate that he is exfiltrating sensitive information from the system.
• A user executes unusual commands or run scripts that they usually do not run or that do not align with their job role. For example, someone in the marketing department running a complex database query. 
• An application suddenly gets thousands more requests than usual, even if it is not the peak time for users accessing that application. This type of behavior indicates a potential DDOS attack.

Network behavior analytics (NBA)

Not that NBA! 🏀 Network behavior analytics specifically focuses on monitoring network traffic to detect unusual activity, such as unexpected traffic patterns or traffic to known malicious sites. The most common malicious network traffic behaviors include:

• Unusual usage of non-standard protocols such as HTTP, SMTP,or FTP.
• A large volume of traffic is going to or coming from a domain name or an IP address. 
• A user trying to map or scan the network topology indicates a bad actor trying to identify network vulnerabilities.
• Lateral movements across the networks.
• Downloading suspicious files like scripts and executable files from untrusted websites.
• Transferring a large amount of data to external systems or out of the network in an unusual manner.

Insider threat behavior analytics (ITBA)

ITBA is also a part of user behavior analytics, which helps organizations identify bad actors they trust. Insider threats can come from internal users who have access to the data within the organization and who have hidden motives to gain advantages by leaking sensitive data or crippling the system operations of organizations. 

Such malicious behaviors that could stem from insider threats include:

• A user is trying to install unusual software without proper permission.
• A change in the typing cadence or keystroke dynamics of users when accessing a user account can indicate a possible misuse of user credentials.

Key benefits of behaviour analytics

Following are some of the major benefits you can gain through BA.

Proactively identifying & mitigating cyber threats

The major benefit of BA is that it enables organizations to identify a wider range of cyber threats, including insider threats, advanced persistent threats, sensitive data leakages and so on.

User activity and behavior analytics can be leveraged in every part of the organization, such as networks, computers, servers, routers, endpoints and applications. It enables detecting even the most complex threats, like advanced persistent threats and zero-day exploits. Thus, organizations can address the risks in time — well before they could lead to serious cyber crimes. 

Detecting advanced persistent threats (APTs)

Behavioral analytics can be invaluable in detecting advanced persistent threats (APTs) in organizations. Today, APTs present a significant challenge to traditional security techniques due to their specialized methods of accessing systems and maintaining persistence. APTs aim to gain prolonged access to the server of an organization, making it more difficult to detection them.

However, behavioral analytics can help identify the presence of APTs by monitoring any unusual activity that deviates from typical patterns and behaviors. 

(Understand monitoring’s role in observability.)

Immediately responding to threats

Automated behavior analytics systems monitor the behaviors in real time and send alerts as and when an unusual behavior is detected. This process enables security teams to act immediately to resolve the issues and block the cyber threat from spreading further or infiltrating the system. 

Addressing compliance issues

Since behavior analytics can detect user activity, organizations can detect non-compliant user behaviors using that data. For example, unauthorized access to client data leads to a breach of privacy and security regulations. Therefore, collecting such data enables organizations to demonstrate compliance with regulatory requirements.

Avoiding huge financial loss

Nowadays, cyberattacks can lead to severe financial losses for organizations. For example, a ransomware attack can lead an organization to bankruptcy. Since behavioral analytics enables security teams to identify attacks before they occur, organizations are likely to…

• Prevent such huge financial losses.
• Keep their cybersecurity strategy as robust and intact as possible.

Behavioral analytics even enable identifying more advanced cyber attacks.

Examples of behavior analytics tools

The market for behavior analytics software has grown significantly, with many software tools that use advanced ML techniques and provide intuitive features.

Here are some top BA tools that many organizations use to improve their security—reading these descriptions will give you a clear picture of how BA contributes to the cybersecurity of an organization. 

Splunk User Behavior Analytics

Let’s start with our own solution. Splunk User Behavior Analytics (UBA) uses behavior modeling, peer-group analysis and machine learning techniques to detect potential malicious behaviors of users, devices and applications. It can detect advanced threats. Importantly, no human intervention is required for analysis. (This also ties in and aligns with SIEM platforms.)

(Learn more about Splunk User Behavior Analytics or take an interactive tour.)

Rapid7 InsightIDR

This cloud-native behavior analytic tool uses endpoint detection and response (EDR) with user behavior analytics. It uses machine learning algorithms to detect risky user behavior patterns and generate alerts for security teams to investigate.

Exabeam

This tool offers automatic anomaly detection using 800 rules and 750+ behavioral model histograms from users and devices. It also provides fully automated threat detection, investigation, and response (TDIR), reducing human intervention and accelerating the investigations and responses.  

Analyzing user behavior has many use cases

Currently, behavior analytics is used in many industries to identify trends, patterns and abnormal behaviors and take data-driven decisions. Regarding cybersecurity, BA is used to find malicious activities of users, systems, applications, networks, and many other connected components of an organization.

There are several types of behavioral analytics, depending on the area of focus. Among them, user behavior analytics is the most common and effective type for cybersecurity. Today, organizations reap many benefits from behavior analytics, such as identifying a wide range of cyber threats, detecting advanced cyber threats like APIT, accelerating security incident response and helping address compliance issues.