How to use Behavioral Analytics in Cyber Security?

The phrase "behavioral analytics" has recently gained popularity in the cybersecurity industry. What is it then? Traditionally, behavioral analytics have been used by organizations to focus on the trends, patterns, and behaviors of their customers. Humans are often creatures of habit, and our Internet usage is no exception.

 

Businesses can tailor marketing campaigns, enhance the customer experience, and even change product offers to fit a consumer's specific likes by generating and using analytics that analyzes an individual's habits and tendencies. However, studying client habits is not the only use of behavioral analytics. Using them to improve cybersecurity can be the difference between preventing a serious attack and a huge breach.

 

Organizations frequently limit cybersecurity, which is a major error. Only their staff used to be subject to behavioral analytics. Since behavior is frequently connected to human activity, it is simple for businesses to concentrate just on user behaviors to spot insider threats or stolen credentials by creating a baseline of user behavior and monitoring it for abnormalities. The cybersecurity sector, however, offers Behavioral Analytics with a tonne more opportunity.

 

What is Behavioral Analytics?

 

Behavioral analytics examines variances in typical, daily activities to spot malevolent behavior using machine learning, artificial intelligence, big data, and analytics.

 

Malicious attacks all perform differently from daily behavior within a system or network, which unites them all. Through signatures that are closely connected to specific kinds of well-known attacks, businesses are frequently able to recognize harmful behavior. However, as attackers advance in sophistication, they consistently create new strategies, methods, and procedures that enable them to not only infiltrate susceptible settings but also to move laterally unnoticed.

 

Behavior analysis is useful in this situation. Security experts may now employ behavioral-based tools, algorithms, and machine learning to identify what the typical behavior of regular users is - and is not - with the aid of vast amounts of unfiltered endpoint data. Events, trends, and patterns that deviate from the status quo can be found via behavioral analysis, both in the present and in the past.

 

Security teams may obtain visibility and uncover unusual behavioral techniques of attackers early on, before they completely execute their plan of attack, by focusing on these abnormalities. Behavioral analysis can also assist in discovering underlying causes and offer perceptions for future detection and forecasting of such attacks.

 

Organizations all over the world have resorted to a variety of cybersecurity solutions, services, and software to protect their data from dangers in the ever-evolving 21st-century battle against data loss, data breaches, and data theft.

 

History of Behavioral Analytics:

 

Analytics based on behavior, more precisely User and Entity Behavior Analytics (UEBA), is one such method. UEBA uses algorithms and machine learning to track the unusual activity on a network, including the routers, servers, and endpoints that make up the network as well as the people that use it. 

 

The market size of UEBA is expected to increase from $890.7 million in 2019 to $1.1 billion in 2025, according to a 2022 Market Data Forecast research. UEBA is also expanding in ways that independent market data don't fully reflect as it increasingly becomes a component of key cybersecurity solutions like SIEM and EDR.

 

Despite the term conjuring up thoughts of psychological or sociological investigations, behavioral analytics' roots aren't in academia; rather, they may be found in the business and statistics sectors. In essence, behavioral analytics is a branch of business analytics, which is the iterative analysis of historical company performance to provide knowledge for decision-making.

 

Such an inquiry can be carried out in a variety of ways. The entire point is to take in data and use it to make better-informed decisions, whether that means examining the performance of your direct competitors, using predictive analytics to predict what the future may hold for your industry, or reviewing employee performance and basing optimization decisions on that information.

 

Machine learning and big data analytics are especially used in behavioral analytics to collect user behavioral data and discover trends, anomalies, and patterns based on this data. Any person who directly interacts with your organization and its data on a regular enough level to produce patterns is referred to here as a "user," whether they are one of your workers, clients, or just anybody else.

 

On media or e-commerce platforms, behavioral analytics are frequently used. There are many ways that businesses use your past activity on their platforms to generate insights and predictions to keep you spending money with them. Examples include the Netflix algorithm, which suggests shows for you to watch next based on what you've already watched, and meal delivery services like GrubHub or DoorDash, which offer you discounts at specific restaurants based on your ordering history.

 

What Makes Behavioral Analytics Effective in Cybersecurity?

 

Of course, data collection and analysis may be useful in cybersecurity. In the fight against data breaches, leaks, and loss, information is one of the most effective weapons that consumers and businesses have at their disposal. The objectives of many firms' cybersecurity programs are also ideally suited to behavioral analytics. Similar to real-world criminals, cybercriminals also prefer to enter a space by taking the easiest route possible. 

 

In the field of cybersecurity, the human factor, especially user accounts with sufficient access rights or credentials for the cybercriminal to carry out their scheme, has repeatedly been demonstrated to be the path of least resistance.

 

According to a 2020 research by the Ponemon Institute and supported by IBM Security, stolen/compromised employee credentials or cloud misconfigurations were to blame for 40% of what the report refers to as "malicious occurrences." 

 

The most expensive infection vector for businesses was compromised employee account login information. The survey found that harmful activities cost businesses $3.86 million on average per breach, but that cost increased to $4.77 million when stolen credentials were included.

 

Companies have a greater chance of preventing a data breach before it occurs and may save the company millions of dollars by watching user activity as well as anomalies in other components of a network, such as servers or routers. Top UEBA businesses like Cynet, IBM, Splunk, or Microsoft frequently use this as part of their sales pitches, but like any cybersecurity solution, the technology isn't perfect.

 

How Can Your Enterprise Deploy Behavioral Analytics?

 

You must first consider each use case specifically for your businesses. This may involve taking into account factors such as your company's size, industry, user base, applications, IT infrastructure, and your plans for scaling and growth over the next five years. Of course, this ought to be the first step in choosing any cybersecurity solution, but businesses frequently skip it.

 

Instead, they choose efficiency above excellence, choosing the approach that best addresses their current issue. As a result, your IT infrastructure will surely get overrun by solutions, which might seriously hamper integration.

 

Your company has to start thinking about cybersecurity on a long-term basis, and you need a long-term solution to go with it. Simple as that. You may choose a solid SIEM solution after making sure that these factors are taken into account. User and entity behavior analytics (UEBA), the most recent iteration of this technology, are typically provided by SIEM solutions. Additionally, SIEM systems include threat intelligence feeds to speed up the discovery of contemporary and sophisticated threats by your analytics.

 

Despite its seeming complexity, SIEM serves as a tool for log management and analysis while also providing a layer of behavioral analysis. This type of threat detection is becoming more and more important as cybersecurity professionals come to realize that digital perimeters cannot completely thwart all attackers. Keep hackers from using your users' accounts against you. With your cutting-edge cybersecurity and analytical tools, keep a tight check on them.

 

Applications of Behavioral Analytics in Cybersecurity

 

The majority of interactions that individuals make with computers or software result in the collection of behavioral data. Uploading information to a website or choosing a product on a website are two examples of interactions. These events are often saved with date and time stamps in databases that are accessible locally on a device or, more frequently, on corporate servers.

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Applications of Behavioral Analytics in Cybersecurity

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1. Detecting potential cyber threats:

 

In the past, rule-driven frameworks were the only ones employed in cybersecurity to identify possible online threats. This can happen, for instance, if a lot of data is downloaded in the middle of the night. This might result in a rule violation and notify the security staff. 

 

Although sophisticated hackers may avoid activating many of the rules that are put up in these systems and it can be challenging to identify workers operating maliciously, this rule-based approach is still a crucial component of a tiered analytics security strategy today (otherwise known as insider threats).

 

Utilizing advanced machine learning algorithms to scan user and entity data throughout a business and spot unusual behavior that might be a sign of a security breach, behavioral analytics provides a people-centric defense.

 

2. UEBA:

 

User and entity behavior analytics (UEBA) is another name for behavioral analytics in cybersecurity. Due to the huge time and money savings, UEBA has become more and more popular. It can sort through the majority of an organization's data to produce high-quality leads that security analysts can assess. UEBA may also lessen the number of security analysts, which may lessen the pressure on businesses to engage in the fierce competition for security expertise.

 

3. Detecting insider threats:

 

Identifying insider threats is one of the behavioral analytics' most important uses in the security industry. Attacks carried out by workers of a firm who are either seeking financial benefit or seeking retaliation against the business are known as insider threats. 

 

No hacking is necessary to steal critical information from the business because workers already have access to it for work-related purposes. As a result, security rules frequently do not activate. However, the security staff may be made aware of any strange conduct by employees using behavioral analytics.

 

4. Identifying APTs:

 

Detecting advanced persistent threats (APTs) is another popular use of behavioral analytics in security. APTs arise when a hacker acquires prolonged access to a company's system. These attacks are extremely difficult to detect using traditional approaches since APTs are intentionally intended to avoid activating common rules to maintain their access for as long as possible. Behavioral analytics, on the other hand, may detect APTs since their algorithms track behavior that is out of the usual and that APTs would display.

 

5. Zero-day attacks:

 

The last and most prevalent application of UEBA software is the detection of zero-day attacks. Zero-day attacks are novel attacks that have never been utilized before and have no rules set to detect them. Since behavioral analysis employs past behavioral data to determine what is abnormal, these new attacks are frequently recognized because they typically employ new executables and techniques that are out of the ordinary to break a company's security.

 

Conclusion

 

Behavioral analytics are important for every firm that wants to improve its cybersecurity. It detects hackers based on their behaviors and inclinations. They may disguise themselves to get access to your system. Their true actions, however, will reveal them.

 

The method of behavioral analytics necessitates tracking the users of your cybersecurity system. It often searches for potential insider threats. These frequently involve disgruntled workers, rogue personnel, or cyber attackers with access to your system. Applications, servers, and devices linked to the system are all examined.