Millions of cybersecurity events occur every day, and as time and technology continue to progress, the means of these cyber attacks become more sophisticated and immensely more difficult to detect.
Statistically speaking, if you are a business owner who stores and manages sensitive data, it is not a matter of if but when someone will attempt to access your networks and compromise your data. In fact, it’s highly likely that you have already been the target of a malicious attack, whether successful or unsuccessful, and you may not even be aware of it.
In today’s article, we will be discussing network detection and response: what it is, how it works, and some of the methods it employs. We will also take a close look at its potential for automation through artificial intelligence and machine learning.
Network Detection and Response
In the past, most networks relied on security measures like antivirus software and firewalls for protection from outside threats. However, today’s security landscape is much more complex and nuanced, and these legacy softwares simply can’t provide a level of security that rivals the sophistication of cyber threats that we see today.
Firewalls monitor and control traffic that is coming into a network or device, but this can present a serious problem for organizations. The problem is that if an attacker is able to gain access to a device or network undetected, they can then propagate malicious software (or malware) throughout the system unnoticed. If this is allowed to happen, the attacker can cause significant damage to your network and steal data from you, and you will likely remain unaware of this until after the damage is done.
What Is Network Detection and Response?
Network detection and response systems must work in conjunction with many other programs and applications within the security infrastructure to form a well-rounded network detection and response system.
Network detection and response (NDR for short) is a comprehensive approach to cybersecurity that continuously monitors, records, and diagnoses all traffic coming into and moving throughout a network.
So how does network detection and response work? A network detection and response system:
Collects and Analyzes Traffic Data
The network detection and response system collects, monitors, and analyzes network traffic to and from every device connected to the network such as computers, routers, switches, and servers, as well as all of the external traffic coming into the network.
Creates a Baseline for Expected Network Activity
Upon capturing and storing the network traffic data, the network detection and response system then thoroughly analyzes the data using a specific set of algorithms based on behaviors of users and devices. This information is then used to create a baseline of typical or expected network traffic, which can then be used as a benchmark for future activity in order to detect inconsistencies and anomalies.
Creates and Extracts Metadata to Provide Additional Context
As the network detection and response system continues to monitor your network, it will constantly extract new information, such as IP addresses, download sizes, protocols, and user session details. This metadata is then used to improve the baseline for monitoring activities by adding contextual information to what is considered normal activity.
Monitors for New and Known Threats
Network detection and response systems also continuously monitor data coming into and moving around the network to detect known malware signatures.
Provides Real-Time Responses for Potential Threats
When a potential threat is identified, the network detection and response system will immediately perform predetermined automated responses (if used with SOAR) and notify cybersecurity personnel so they can take the appropriate action in order to mitigate risks. Depending on the severity of the threat, the security response may involve quarantining the affected devices from the rest of the network or simply performing an investigation into the forensic details of the security event.
What Kinds of Network Detection and Response Systems are Used Today?
There are many different tools that are used in network detection and response systems, and each has its own specific purposes and varying degrees of capabilities.
However, for the purposes of this article, we will be discussing SOAR and XDR.
SOAR
SOAR stands for Security Orchestration, Automation, and Response. SOAR is essentially the governing platform for the rest of the integrated software that brings the entire network detection and response system together. To use an analogy, you can think of SOAR as the brain of the network detection and response system, and all of the integrated pieces of software are the organs that make the entire system work.
SOAR allows you to set protocols for security events, notify security personnel concerning detected threats, and automate security responses whenever possible.
SOAR also interprets data and displays it in a visual format so security personnel can monitor activities within the system, while also updating dashboards with key security metrics.
Let’s take a look at some of the security integrations that go into making the SOAR system possible.
- SIEM – SIEM stands for security information and event management. SIEM is not always used in conjunction with SOAR, but it can be used to enhance SOAR’s data consolidation and workflow capabilities.
SIEM can be integrated with SOAR for the purpose of recording and analyzing security logs and storing them in a centralized location to provide real-time visibility into that data set.
- TIP – TIP stands for threat intelligence platform, and it is responsible for gathering intelligence regarding security risk profiles from a number of different sources. These sources can include government agencies, research institutions, trusted partners, commercial entities, and even internally-generated business intelligence.
TIPs may also incorporate automation and orchestration to automate the processing, analysis, and communication of threat intelligence data.
- EDR – EDR is another security capability that can also be integrated within the broader SOAR system. EDR stands for endpoint detection and response, and it is designed to protect “endpoints” or devices within a network.
EDRs offer deep visibility of endpoint activities, such as file changes, network connections, process execution, and system events.
By establishing workflows and specific protocols, EDRs can perform real-time automated responses to security threats. These can include quarantining devices within the network, terminating suspicious processes, and removing malware.
To put it in simple terms, the hierarchy of cybersecurity goes like this:
- There is an EDR at the individual endpoint level to monitor and record data on that device.
- The SIEM collects logs from all the other connected devices within the environment.
- At the network level, the NDR records data from network traffic and shared data sources.
- And the governing platform of all of these data sources, including data sourced from a TIP, is SOAR.
- The ultimate end stage is XDR (or extended detection and response) which combines all of the above into a single offering.
All of these elements within this security hierarchy work together to improve each other’s functionality and overall effectiveness. Each one builds on the data provided by the others to enrich and contextualize the data, allowing it to provide better visibility into potential security threats.
Now that we have gone over some of the elements of XDR, as well as the hierarchy of data enrichment, now we’re going to get into machine learning.
Artificial Intelligence and Machine Learning
Artificial intelligence (AI) and machine learning (ML) make all of these processes possible by employing algorithms, models and logic to analyze data, learn from it, and automatically generate alerts and even recommend appropriate responses.
Here are a few of the applications for artificial intelligence and machine learning in network detection and response:
Detecting Threats
The primary function of network detection and response is to identify threats and respond to those threats, and there are several ways that AI and ML can achieve the former of these two functions.
AI and ML can also be used in a network detection and response system. The list of uses will surely continue to grow as these technologies become more sophisticated. However, it is important to note that this automation cannot replace the work of a human.
Threat detection is highly nuanced and complex, so human interventions will always be necessary to make judgments about the nature of suspicious network activity and how it should be handled. Nevertheless, technologies like artificial intelligence and machine learning make the network detection and response system much more sophisticated and efficient than would be possible for humans alone.
Protect Your Data With NetWitness
NetWitness offers a full stack of cybersecurity integrations and platforms that will keep your data, devices and users under a watchful eye looking for malicious actors that may make their way into your network.
NetWitness’s security solutions are also equipped to protect businesses and organizations that operate within the cloud and across various platforms. If you are concerned about your digital assets and would like to make sure they are protected at all times, contact NetWitness today to request a free demo!