What Is Network Behavior Anomaly Detection? Definition, Importance, and Best Practices for 2022

Network behavior anomaly detection is defined as the process of monitoring enterprise networks to detect abnormal behavior. Once an anomaly is spotted, network behavior anomaly detection either initiates an automated response or notifies security teams. This article covers the definition, importance, and best practices of network behavior anomaly detection.

What Is Network Behavior Anomaly Detection?

Network behavior anomaly detection is the process of monitoring enterprise networks to detect abnormal behavior. Once an anomaly is spotted, network behavior anomaly detection either initiates an automated response or notifies security teams.

The post-pandemic corporate environment is rife with unpredictable cybersecurity threats. New types of malware built to silently compromise enterprise systems, crippling DoS attacks, and advanced persistent threats capable of bypassing traditional security solutions have completely changed how we look at IT security in 2022. 

Gone are the days when a strong network perimeter and robust signature-based security solutions could protect an enterprise from bad actors. Today, more proactive measures to counter cyber threats, such as in-depth awareness of network behavior, are necessary to ensure a secure IT environment.

Of course, many companies continue to rely on legacy IT security systems that consist of endpoint security and perimeter protection. However, such cybersecurity infrastructure often fails to account for the network between the perimeter and the endpoint. Since the pandemic hit, threat actors have become more advanced than ever. Today more than ever, threat actors possess the tools to bypass traditional security solutions and sneak into enterprise networks.

Network behavior anomaly detection is built to counter such threats. As the name suggests, it relies on network behavior analysis to operate. Network behavior anomaly detection uses artificial intelligence (AI) and machine learning (ML) to detect hidden threats in those parts of network infrastructure that other security tools cannot reach and then notifies network teams. Continuous monitoring is a crucial feature of network behavior anomaly detection, augmenting anti-threat applications such as antivirus and spyware detection solutions with an extra layer of security.

Network behavior anomaly detection works by detecting unusual network behavior, for instance, heavy traffic flow during otherwise ‘quiet’ hours. However, while, by itself, this solution is highly efficient at patching the gaps left by more traditional cybersecurity tools, it is the most useful when combined with them. 

Security teams leverage network behavior anomaly detection alongside network firewalls, network performance monitoring software, and other measures. While these other tools protect the network from known threats, network behavior anomaly detection brings to light suspicious activities that might end up compromising network operations through hidden infections, data theft, or other malicious activities.

Combining signature and anomaly detection capabilities allows network behavior anomaly detection to investigate unusual network activity. The network characteristics tracked by network behavior anomaly detection programs at scale include packets, bandwidth, bytes, traffic volume, and protocol used. Any suspicious event is logged in a report and consists of the originating and destination IP addresses, relevant ports, protocols, timestamps, and more. 

These critical metrics and many more are tracked by the network behavior anomaly detection tool in real-time. An alarm is raised if a strange trend or outlier that might hint at the existence of a threat is detected. Depending on the chosen configuration, network behavior anomaly detection programs can also monitor the behavior of individual network users.

Network behavior anomaly detection solutions work by ‘sweeping’ the complete enterprise network when looking for threat actors. This is a marked difference from the perimeter, firewall, and endpoint security systems. 

These solutions only detect threats communicated through the specific part of the network where they are set up. Conversely, network behavior anomaly detection accounts for three significant network properties—traffic flow patterns, passive traffic analysis, and network performance data—from across the network to detect several different types of threats, such as:

• Inappropriate network behavior, such as unauthorized applications or a known program’s unusual use of ports. On detecting such activity, the network behavior anomaly detection solution and associated protection systems can identify and disable the associated network processes automatically and notify the concerned security personnel.
• Data exfiltration, like a suspiciously high volume of data being transferred. In case such an activity is detected, network behavior anomaly detection and related security solutions can automatically monitor the outbound transfer of data and report it to security teams in real-time. Some systems would even be able to identify the destination of these data transfers further and determine whether it is a legitimate communication or a cybersecurity event.
• Hidden threats, such as advanced malware. Network behavior anomaly detection would work with other security solutions to deploy the appropriate security countermeasures. It notifies concerned stakeholders to detect a threat that may have dodged perimeter security and entered the enterprise network.

Regardless of the configuration of the network or the tool, the first step taken by a network behavior anomaly detection solution is to establish a baseline for the average user and network behavior. This baseline is established over a prolonged period; the longer the time, the more accurate and useful the collected behavior data. Once the solution captures and defines the ‘normal’ parameters, it flags outliers in real-time.

Acknowledging the dynamic cyber threat landscape of 2022, software vendors have begun to include network behavior anomaly detection tools in their network security solution suites.

See More: What Is Content Filtering? Definition, Types, and Best Practices

Importance of Network Behavior Anomaly Detection

Network behavior anomaly detection is an essential part of modern-day cybersecurity systems. It provides a detailed overview of network activity and enables security teams to detect and respond to advanced network threats.

Listed below are five key points highlighting the importance of network behavior anomaly detection across roles, departments, and industry verticals.

1. Bolsters enterprise security for the CISO

Network behavior anomaly detection uses network traffic statistics gathered from routers, switches, and network probes in formats such as jFlow, NetFlow, NetStream, and IPFIX to highlight the activities of bad actors. 

When combined with complementary cybersecurity solutions, network behavior anomaly detection detects botnets, targeted attacks, unknown malware, data leaks, insider threats, and other vulnerabilities. Apart from this, the solution can bolster network operations by automatically detecting outliers and operational issues.

2. Enables dev team to improve application performance

The pandemic has pushed almost every type of business to establish a digital presence. With competition in the online space reaching new heights, application performance is often the most significant deciding factor in revenue generation. Internal applications can determine workforce productivity, while customer-facing applications need to be fast and secure to attract and retain clients.

Traditionally, dev teams adopted a ‘reactive approach’ to monitoring application performance and responding to security issues only after they have taken place. As a result, an enterprise can suffer the consequences of a targeted attack—sometimes even before the presence of the problem was detected—before remedial action is taken.

Network behavior anomaly detection seamlessly correlates relevant network metrics with existing behavioral data to provide developers with actionable reports if application performance is compromised. This tool uses ML algorithms to process data collected over the long term to identify potential issues with application performance before users can be affected.

3. Helps CRM optimize the user experience

Dynamically responding to the needs of users is vital in 2022. If an organization cannot keep up with user demands, it will likely lose revenue to competitors. As such, product teams need to constantly strive to ensure new features are rolled out as soon as possible. 

However, not every application version can undergo rigorous testing before being released. Bugs and security flaws may slip through occasionally, leading to negative user experience at best and loss of sensitive data to threat actors at worst.

Network behavior anomaly detection monitors the performance of various products on a network from the moment they are rolled out. This data adds value to reports on every A/B test, purchase funnel modification or other process change. 

Behavioral anomalies—whether indicative of major threats or minor bugs—can be addressed more efficiently with the help of network behavior anomaly detection. This solution can help ensure a timely response to DDoS attacks, unforeseen changes, and other lapses due to faulty version releases.

4. Streamlines processes for the security team

With security flaws being exploited faster than ever before, any online business needs to remain alert and ensure smooth operations. Dealing with a compromised security posture in real-time is vital if a major breach is prevented. Network behavior anomaly detection can help mitigate everything from API errors and server downtime to load-time glitches and more through instantaneous reporting. Comprehensive network coverage and minimization of response time across business networks are the core tenets of network behavior anomaly detection.

5. Has applications across industries

Most, if not all, industries across the globe have established an online presence after the 2020 pandemic onset and subsequent lockdowns. Network behavior anomaly detection solutions are playing a key role in improving the security posture of organizations across numerous industry verticals, including:

• Ecommerce: This is where network behavior anomaly detection can assist in identifying issues such as network spikes, downtime, and even price glitches.
• Telecommunications: One of the largest producers of time series data, telecommunication companies must mitigate anomalies in their complex networks or risk downtime. Network behavior anomaly detection can help monitor parameters such as call quality, latency, and jitter to address performance issues proactively in real-time.
• Adtech: In the adtech industry, billions of transactions are processed every day, and real-time settlements occur within milliseconds. It can leverage network behavior anomaly detection for continuous transaction monitoring and maintaining the status quo.
• Gaming enterprises: Gaming organizations can use network behavior anomaly detection to monitor operating systems, user segments, stats, devices in use, and more to quickly fix glitches and errors that might lead to a negative user experience.
• Finance companies: This is an industry where security is of utmost importance. It can use network behavior anomaly detection as a part of its security measures to process transactions securely and counter advanced attacks before they cause widespread damage.

See More: What Is Zero Trust Security? Definition, Model, Framework and Vendors

Network Behavior Anomaly Detection Best Practices for 2022

Are you thinking of adopting a network behavior anomaly detection solution for your organization? The best practices listed below can help you make the most of it in 2022.

1. Automate your analytics

Network behavior anomaly detection solutions use AI algorithms to analyze millions of network actions in just a few seconds. While this alone can significantly decrease the probability of threats making it through your network, automating all related analytics solutions can help boost your security team’s efficiency. If you have a relevant use case, adopt end-to-end network analytic solutions for your enterprise to ensure maximum efficacy.

2. Set a manual baseline

While network behavior anomaly detection solutions can set a baseline for network behavior, security teams might occasionally interfere with the alert settings. This can lead to them not being notified of certain abnormal user activities, such as the sudden downloading of massive volumes of data or the network being accessed from a new location.

While it is understandable if your security team wants to block certain alerts because the data is noisy, being utterly unaware of abnormal incidents might lead to data leaks or other cybersecurity incidents. 

Therefore, if keeping alerts activated all the time is not an option, consider creating a manual baseline that can enable your security team to check the logs and spot uncharacteristic activities. This would help spot outliers such as a download being initiated in an untimely manner or a user accessing the network after work hours.

3. Account for anomalies in login patterns

Remote work is still trending in 2022 and may last until 2023. Configure your network behavior anomaly detection solution to notify yourself of outliers in login patterns, even if this is an inconvenience due to the dynamic nature of remote work. Less tech-savvy employees might access the corporate network over a public Wi-Fi connection, leading to their credentials being compromised. Additionally, credentials may also fall into the wrong hands due to phishing and related malicious activities.

While the login patterns of some employees may lead to constant ‘annoying’ alerts, security teams must use network behavior anomaly detection to account for anomalies in login patterns. This is because other cybersecurity solutions might not be able to prevent cybercriminals from misusing leaked credentials.

4. Ensure global implementation

Network behavior anomaly detection detects covert threats located anywhere on the network, as long as it has access to the entire network. If your enterprise network has a large, complex structure, make sure the network behavior anomaly detection solution is configured to reach even the most remote corners of it. Set up other security solutions, such as firewalls and encryption tools, in a way that prevents them from blocking network behavior anomaly detection from reaching its full potential.

5. Set alert threshold wisely

We discussed above how disabling alerts might lead to missed threats until it’s too late. Building on that, it is essential to set an alert threshold that filters out (most of) the noise without any important alerts being skipped. Threats nowadays are no longer as overt as they used to be—criminals can penetrate a corporate system and make away with sensitive information. The only hint you would have is the out-of-place behavior detected by network behavior anomaly detection. Whenever possible, give preference to examining alerts, no matter how noisy they may be, instead of ignoring them.

6. Beware of intentional insider threats

No security solution, not even network behavior anomaly detection, might be able to account for every intentional (and sometimes unintentional) action of insiders. A sufficiently crafty (or unaware) internal stakeholder with enough access privileges might be able to get away with anything. 

This is because network behavior anomaly detection relies on detecting anomalies based on historical data, and insiders might act on malicious intentions while performing their daily activities. Make sure measures such as awareness training and strong security policies are implemented to prevent malicious insiders from compromising the integrity of your organization.

7. Watch out for false positives

Networks are more dynamic in a remote work environment, blurring the distinction between normal network operations and abnormal activities. Many network behavior anomaly detection solutions rely, to a certain extent, on probability scores to define anomalies, which could lead to false positives. 

For instance, an unexpected system update would also transfer a large amount of data to a particular endpoint, which is an innocuous activity but might be marked as an outlier by network behavior anomaly detection. 

Frame your policies to acknowledge the possibility of false positives and give your security team enough authority to make a call if they detect an uncertain situation. This is a great alternative to forcing them to investigate every alert, which consumes resources and might divert them from the actual threats.

8. Use metadata to monitor encrypted traffic

Data encryption is widely prevalent in today’s corporate landscape. While this is great from a security perspective, it can also transmit threats through encrypted communications. To prevent this, set encryption to take place at the application level. 

This would enable network behavior anomaly detection to perform the statistical analysis of destination ports, IP addresses, and other related metrics for incoming and outgoing encrypted communications. This way, encryption would not completely block anomaly detection, even though it might lead to fewer anomalies being detected.

9. Implement findings in decision-making

The primary purpose of network behavior anomaly detection is to bolster enterprise cybersecurity. However, that does not mean that it cannot be used to add value to other organizational processes as well. The insights provided by network behavior anomaly detection solutions can be useful in other business areas too, including policymaking, business development, and application management. 

Combine the findings of the network behavior anomaly detection tool with the insights generated by other analytic solutions to gain a comprehensive overview of your organization’s cybersecurity posture, application viability, and other helpful information.

10. Finally, don’t rely on network behavior anomaly detection alone

Network behavior anomaly detection is a great solution for detecting and preventing cyber criminals from exploiting your network. However, it works best when combined with other security solutions. 

• Use a firewall to prevent known threats from entering your network
• Use intrusion detection systems (IDS) to spot more common malicious activities
• Use intrusion prevention systems (IPS) to prevent cybercriminals from executing known attacks
• Use network access control (NAC) software to restrict network access to only those endpoints that comply with existing security policies 

Other security tools that further boost the overall security posture include web filters, proxy servers, anti-DDoS solutions, load balancers, and spam filters.

See More: What Is URL Filtering? Definition, Process, and Best Practices

Takeaway

Network behavior anomaly detection solutions allow security teams to protect enterprise systems and databases from advanced, dynamic threats that traditional solutions might not even be able to detect. Detecting network anomalies proactively helps mitigate the undesirable consequences of hidden threats.

Cutting-edge network behavior anomaly detection solutions leverage powerful AI measures to observe network traffic constantly. This analysis of network communications allows security teams to spot anomalies and suspicious behavior and act before they pose a major threat to the integrity of the organizational network. With network behavior anomaly detection, unknown security threats that are undetectable by other security technologies can be dealt with swiftly and efficiently.

How would network behavior anomaly detection help your enterprise? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear your views!

MORE ON NETWORKING

• Top 10 Network Management and Monitoring Tools in 2022
• What Is Network Behavior Analysis? Definition, Importance, and Best Practices
• Top 10 Best Practices for Network Monitoring in 2022
• What Is Network Traffic Analysis? Definition, Importance, Implementation, and Best Practices
• Top 10 Enterprise Networking Hardware Companies in 2022