What Is Internet Background Noise: Types, Sources, and How to Minimizer Its Impact

Every day, your cybersecurity teams face a relentless stream of data, some threatening, much of it irrelevant. 

This constant flood of unsolicited traffic, known as internet background noise, is a constant challenge that shapes how your teams defend your networks. Generated by automated scans, misconfigured devices, and outdated systems, internet background noise accounts for much of the clutter that overwhelms our security systems.

The impact is really hard to ignore. As of late 2023, studies estimate that up to 90% of security alerts are false positives, which are often triggered by benign background noise. For teams already stretched thin, this noise leads to alert fatigue, slower response times, and a growing risk of missing real threats.

In this article, we’ll explore what internet background noise is, why it matters, and how you can effectively manage it. 

What is internet background noise?

Internet background noise is the constant, unsolicited flow of data across the internet. It’s not directed at anyone specifically but exists as a byproduct of how networks operate. 

Internet background noise can be generated from various sources. Automated systems, like software updates or web crawlers, generate legitimate background traffic. Misconfigured devices send data unintentionally, and outdated systems still online may broadcast unnecessary signals. 

For cybersecurity teams, the volume of this random traffic is the main problem. It creates a crowded baseline of activity, making it difficult to spot actual threats. Security systems have to process every bit of data they receive, including background noise. 

The more noise there is, the more likely it is for systems to miss real threats or trigger false alarms. Each false positive costs time and attention, stretching teams thin and delaying responses to real issues.

Types of internet background noise

Internet background noise can be grouped into three main categories, harmless, malicious, and ambiguous. Each type requires a different approach to identify, analyze, and respond effectively.

Harmless noise

Harmless noise is routine traffic that doesn’t pose any risk to network security. It’s an unavoidable part of internet activity but typically serves a legitimate purpose.

Some examples are search engine bots indexing web pages, network diagnostics like ping requests, software checking for updates, and DNS queries that translate domain names into IP addresses. 

Malicious noise

Malicious noise is intentionally harmful. It includes bots scanning for vulnerabilities, malware scanners searching for exploitable systems, and attack probes attempting to bypass security. 

Ambiguous noise

Ambiguous noise lies in between. It includes activities like automated vulnerability scanners and unknown probes, which could be either benign or malicious, depending on their intent. These are the hardest to classify and demand closer scrutiny to determine their nature.

Sources of background noise

As we mentioned above, internet background noise comes from many places, and each source adds to the challenge of managing cybersecurity. 

When you break it down, internet background noise is a challenge that affects the day-to-day work of anyone tasked with protecting networks. If you know where this noise comes from, it becomes easier to develop strategies to filter it out and focus on what truly needs attention.

Automated scanners

Some of the traffic comes from automated tools like Shodan and Censys. These scanners are designed to map the internet, probing networks, and devices to understand their vulnerabilities. 

While useful for research and legitimate security purposes, they generate a lot of activity that security teams need to sort through, even when it poses no actual risk.

Malicious bots

On the more dangerous side are bots deployed by attackers. These programs run tirelessly, scanning for weak points like open ports or unpatched software. 

They’re not targeting specific systems but casting a wide net to find anything exploitable. This kind of traffic blends into the background noise, making it harder to detect actual attacks.

Misconfigured devices

Many devices, like routers or IoT gadgets, are simply misconfigured. They send data out unintentionally, often without their owners realizing it. 

This kind of traffic isn’t harmful on its own, but it clogs up networks and makes legitimate threats harder to spot.

Legacy systems

Older systems that are still online can also contribute to the problem. These systems weren’t built for today’s network standards and often produce outdated or unnecessary communications. They create a steady stream of irrelevant traffic, further complicating efforts to maintain a clear picture of network activity.

Cybersecurity research

Even well-intentioned efforts like honeypots, which are tools used to attract and study malicious traffic, add to the noise. These systems generate data for research and analysis, but they also increase the volume of background traffic, which security teams still need to account for.

Common protocols and ports affected by background noise

Certain protocols and ports are frequent targets of internet background noise. These are essential for communication across networks, but their widespread use also makes them vulnerable to both benign scanning and malicious exploitation.

Here are some common targets:

• HTTP (Port 80): Used for web browsing.
• HTTPS (Port 443): The secure version of HTTP.
• SSH (Port 22): A critical protocol for secure remote access.
• RDP (Port 3389): Enables remote access to Windows systems.
• FTP (Ports 20 and 21): Used for file transfers but vulnerable to outdated systems.
• SMTP (Port 25): The basis of email communication.
• DNS (Port 53): Vital for resolving domain names.
• NTP (Port 123): For time synchronization.
• SNMP (Ports 161 and 162): Used for managing network devices.
• LDAP (Ports 389 and 636): Supports directory services.

Why is internet background noise important in cybersecurity?

Internet background noise complicates cybersecurity in ways that directly impact how threats are identified and handled, making it harder to spot genuine attacks. 

Automated scanners, misconfigured devices, and bots flood networks with traffic that looks like normal activity. In this clutter, malicious actions often blend in and, hence, slip past detection. 

Security systems, like Security Information and Event Management (SIEM) tools, are meant to analyze and flag unusual activity. But these systems have limits. When they are inundated with too much background noise, they become less efficient, and threat detection and response times slow down. 

Internet background noise also has a significant impact on network bandwidth and resources. All this traffic uses bandwidth and network resources that could otherwise support legitimate activity. Slow systems and delayed operations hurt productivity and can create additional vulnerabilities.

Last but not least, the huge volume of security alerts generated by internet background noise overwhelms cybersecurity teams, making it harder to focus on what truly matters.

Over time, it leads to alert fatigue, where important warnings are missed simply because they get lost in the flood. For human analysts, this constant pressure can even lead to burnout.

How to measure, analyze, and filter out internet background noise

Several tools and methods help make sense of this constant flow of data. Below, we take a look at the most common.

Using honeypots vs. crowd-powered detection tools

Honeypots are deliberately vulnerable systems set up to attract malicious traffic. They’re useful for studying attack methods and gathering insights into threats, but their scope is limited. 

Honeypots only capture traffic directed at them, meaning they provide a narrow view of the broader noise on the internet.

Crowd-powered detection tools, on the other hand, take a collaborative approach. They collect data from many users and systems and offer a much wider view of internet activity. These tools offer a collective perspective, make it easier to identify patterns, and distinguish between harmless and harmful traffic. They’re especially effective for spotting emerging threats in real time, which gives them an edge over honeypots in many scenarios.

CrowdSec holds a major advantage when it comes to crowdsourced data and the diversity of the intelligence collected. The CrowdSec data come from real users, real servers, in real production environments across a massive range of industries. 

If you’re curious about how crowdsourcing works in cyber threat intelligence and want to dive into how we curate and aggregate data, check out our free course on the CrowdSec Academy!

Threat intelligence platforms

Threat intelligence platforms go a step further by providing context. These systems analyze traffic patterns and correlate them with known threats, such as botnets and suspicious behaviors. They help teams focus on what matters by filtering out irrelevant noise and highlighting real risks. 

Tools like the CrowdSec CTI are particularly effective because they not only identify anomalies but also provide you with extensive context on a malicious IP and its activity, helping you act quickly and confidently.

Anomaly detection tools

Anomaly detection tools use machine learning to spot patterns that don’t fit normal activity. They analyze historical traffic to understand what “normal” looks like and flag anything unusual, like unexpected spikes or access attempts. 

So, they’re excellent for identifying subtle or hidden threats that might otherwise be overlooked in the sea of background noise.

IP blocklists

Advanced and curated IP blocklists can filter out noise-generating traffic, such as botnets or automated mass scans, which don’t pose a real threat. 

By removing this noise, security teams can focus on more critical alerts and improve response times. These filters reduce the overwhelming volume of non-threatening traffic that often masks real risks. The CrowdSec High Background Noise Blocklist has been designed to do just that! It contains IPs considered internet background noise, identified as malicious or potential threats. Blocking these IPs can further reduce your alert volume and save infrastructure resources.

Regular monitoring and updating

Background noise isn’t static, and it changes over time. So, regularly monitor your network traffic for changes and adjust your detection rules to match these shifts. 

Benefits of understanding and managing internet background noise

When you can effectively separate noise from threats, you get several benefits that enhance your security posture and operational performance.

Without the distraction of irrelevant traffic, it’s easier to pinpoint suspicious activity and act quickly. This reduces the risk of critical incidents being overlooked in a sea of false alarms. 

False positives are one of the most frustrating consequences of unmanaged background noise. They drain your team’s time and energy, leading to alert fatigue and potentially missed warnings. Filtering out irrelevant alerts allows your team to focus on genuine threats. This not only improves response times but also boosts morale.

Most importantly, reducing unnecessary traffic eases the load on your network infrastructure, cutting costs related to bandwidth and processing power. With fewer irrelevant alerts, your team can shift their focus from repetitive tasks to more strategic efforts.

Moving forward

Internet background noise is an inevitable side effect of having internet-facing systems, but the tools to manage it are becoming smarter. AI-driven solutions are helping security teams filter out irrelevant traffic more effectively, while threat intelligence tools are improving how we identify and prioritize genuine threats.

As the field evolves, we are likely to see the adoption of clear standards and best practices that guide how you address background noise. These changes will help streamline efforts and ensure resources are directed toward protecting against genuine threats.

References and further reading

• Deep learning for network intrusion: A hierarchical approach to reduce false alarms
• What Is Network Security: Types, Best Practices, and Emerging Trends
• Honeypots Vs. Crowdsourced Threat Intelligence Explained
• What Are Mass Exploitation Attacks and How to Defend Against Them
• Security Information and Event Management (SIEM) Reviews and Ratings
• Detecting Suspicious IP Behavior and Impossible Travel
• What is Cyber Threat Intelligence: Lifecycle, Types, and Benefits