Key Features to Consider When Choosing an NDR Solution
Knowing what’s going on throughout the expanse of your organization’s network is
critical to ensuring the safety of users, devices, and servers. This article will cover how
AI-based Network Detection and Response (NDR) technology is cementing itself as the
future of network security – and which specific features are proving to be most worth the
price tag.
Gartner Market Guide for Network Detection and Response (NDR)
In recent Gartner® reports on Network Detection and Response (NDR), Gartner notes that OT and IT environments...
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Why Do You Need an NDR Solution?
Network security has consistently represented one of the most difficult frontiers to keep a handle on. Even relatively complex network layouts would be easy to secure with a simple firewall, if it weren’t for the fact that many services today are decentralized. With
so many devices outside of the traditional perimeter, the potential for vulnerabilities is higher than ever before. And it’s not just services that are abstracted away from your
own defenses: employees are ever-reliant on wireless networks that are more
susceptible to eavesdropping and unauthorized access. The inherent nature of wireless
communication means that securing these networks requires constant vigilance and
advanced security protocols.
Alongside a changing network landscape, there’s also the ever-evolving threat from profiteering cybercriminals to contend with. Sophisticated techniques are increasingly witnessed in the wild, while state-sponsored attacks flourish under today’s geopolitical tensions. These threats often exploit the legitimate network tools and configurations keeping actual employees connected, making them harder to detect and defend against.
Therefore, organizations need to keep an eye on the traffic flowing across their tech stacks. Enter the NDR solution: these tools continuously keep track of the network activity going on under the bonnet with AI, allowing you to detect and respond to concerning developments far quicker. Learn about what NDR is.
Alongside a changing network landscape, there’s also the ever-evolving threat from profiteering cybercriminals to contend with. Sophisticated techniques are increasingly witnessed in the wild, while state-sponsored attacks flourish under today’s geopolitical tensions. These threats often exploit the legitimate network tools and configurations keeping actual employees connected, making them harder to detect and defend against.
Therefore, organizations need to keep an eye on the traffic flowing across their tech stacks. Enter the NDR solution: these tools continuously keep track of the network activity going on under the bonnet with AI, allowing you to detect and respond to concerning developments far quicker. Learn about what NDR is.
What Are The Key Features of NDR?
Given NDR’s critical importance in keeping communications between devices secure, it’s
vital that your tool of choice has a suite of features that sheds light into even the hard-to-
reach corners of your networks. Knowing which ones are important demands a deeper
understanding of how NDR keeps your defenses up, however.
Deep Packet Inspection
Network activity takes many forms, but at the application level, packets are king. When data is sent via a network, it is broken down into more manageable parts, called packets. Like a letter, each packet contains the address it’s being sent to, as well as the actual message – or data – being transmitted. Traditional packet inspection examined just the header part of this data – which just contained information on the destination and sender. Unfortunately, even simple certificate spoofing allows attackers to work their way around this defense – meaning in the modern day, deep packet inspection is a minimum standard for safe NDR features.
Deep packet inspection relies on a central connection point and a network tap: this grants full access to packet information. Being able to see not just the packet header, but the content and protocol it comes with, grants a far deeper degree of visibility into what’s being sent over your network. Knowing what application, user, and device is transferring which packets of data offers a way to fastrack network understanding and optimization.
However, DPI comes with a few key drawbacks. Attackers are already aware of these. For instance, DPI demands a lot of processing power, as it thoroughly inspects every packet’s data segment. Ironically, therefore, DPI is actually less useful across high-bandwidth networks, as it just can’t inspect all network packets.
Organizations are turning more frequently to encryption as a means to secure their network communications and digital interactions. Unfortunately, so are attackers. DPI struggles to glean much information from encrypted network data, meaning that they would not be able to catch encrypted comms between a ransomware trojan and its C2 server.
To combat this, your NDR tool needs to have far more than DPI in its toolkit.
Deep packet inspection relies on a central connection point and a network tap: this grants full access to packet information. Being able to see not just the packet header, but the content and protocol it comes with, grants a far deeper degree of visibility into what’s being sent over your network. Knowing what application, user, and device is transferring which packets of data offers a way to fastrack network understanding and optimization.
However, DPI comes with a few key drawbacks. Attackers are already aware of these. For instance, DPI demands a lot of processing power, as it thoroughly inspects every packet’s data segment. Ironically, therefore, DPI is actually less useful across high-bandwidth networks, as it just can’t inspect all network packets.
Organizations are turning more frequently to encryption as a means to secure their network communications and digital interactions. Unfortunately, so are attackers. DPI struggles to glean much information from encrypted network data, meaning that they would not be able to catch encrypted comms between a ransomware trojan and its C2 server.
To combat this, your NDR tool needs to have far more than DPI in its toolkit.
Metadata Analysis
Metadata analysis (MA) takes a step back from DPI’s hyper-specific packet-by-packet approach, instead capturing the full array of attributes about network communications, applications, and actors – without drilling into each packet’s entire payload. This is how NDR can achieve the majority of its best NDR use cases.
For every session that traverses the network, comprehensive metadata is recorded; this metadata extends to capture a variety of critical attributes that can identify a cyberattack just in time. At its most basic level, this includes the host and server IP addresses, port numbers, and geo-location details of every connection. But metadata offers a greater wealth of info than just that: DNS and DHCP logs help map devices to IP addresses, while further details of web page access can build a clearer picture of the connections taking pace. Domain Controller logs help link the user to the systems they might have access to. The DPI issue of encryption is thwarted thanks to metadata’s presence even on encrypted web pages: from the type of encryption, cipher, hash; to the fully qualified domain names of the client and server; and the hashes of various objects like JavaScript and images, all of this network data can be funneled into modern NDRs.
Metadata analysis grants visibility into an entire network, making MA optimal for the networks unsecured by DPI. Namely, high-bandwidth networks that are more distributed. At the same time, note that MA is unaffected by encryption: this allows it to detect and prevent advanced cyberattacks that hide behind traffic encryption processes. The focus on multi-source network information is far better suited to today’s cross-functional and tightly-integrated security stacks.
For every session that traverses the network, comprehensive metadata is recorded; this metadata extends to capture a variety of critical attributes that can identify a cyberattack just in time. At its most basic level, this includes the host and server IP addresses, port numbers, and geo-location details of every connection. But metadata offers a greater wealth of info than just that: DNS and DHCP logs help map devices to IP addresses, while further details of web page access can build a clearer picture of the connections taking pace. Domain Controller logs help link the user to the systems they might have access to. The DPI issue of encryption is thwarted thanks to metadata’s presence even on encrypted web pages: from the type of encryption, cipher, hash; to the fully qualified domain names of the client and server; and the hashes of various objects like JavaScript and images, all of this network data can be funneled into modern NDRs.
Metadata analysis grants visibility into an entire network, making MA optimal for the networks unsecured by DPI. Namely, high-bandwidth networks that are more distributed. At the same time, note that MA is unaffected by encryption: this allows it to detect and prevent advanced cyberattacks that hide behind traffic encryption processes. The focus on multi-source network information is far better suited to today’s cross-functional and tightly-integrated security stacks.
Behavioral Analysis
We’ve covered what data should be collected and why – but not how it’s used to better secure your networks. In the past, network protection attempts have focused on aligning packet information with the signatures present in known malware attacks. While better than nothing, this approach leaves your networks wide open to novel attacks. Today’s attacks leave no room for error – as proven by the recent $22-million ransomware attack on Change Healthcare, with more yet to come.
Behavioral analysis is the industry’s answer to today’s increasingly novel and distributed attacks. Machine Learning algorithms allow all this metadata and packet information to be grouped into wider patterns of behavior. This is achieved in two different ways: supervised and unsupervised learning techniques. Supervised machine learning pinpoints fundamental behaviors common to various threat variants (such as the fact that newly-deployed malware will usually reach out to a C2 server), allowing for consistent detection across differing scenarios. On the other hand, unsupervised machine learning algorithms sift through enterprise data on a far vaster scale, performing billions of probability-based calculations from observed data. These algorithms do not rely on previous threat knowledge but independently categorize data and identify significant patterns.
Essentially, unsupervised algorithms allow NDR tools to establish a baseline of what is normal for your network. They then allow your SOC team to see any unusual connections that suddenly pop up: these can be indicators of a supply chain attack if they suddenly sprout from one source; or, if more data than average is being sent to an external device, it can be evidence of a malicious or compromised user. Both supervised and unsupervised learning models are important, as collectively they cover the full breadth of behavioral analysis that your networks need.
Behavioral analysis is the industry’s answer to today’s increasingly novel and distributed attacks. Machine Learning algorithms allow all this metadata and packet information to be grouped into wider patterns of behavior. This is achieved in two different ways: supervised and unsupervised learning techniques. Supervised machine learning pinpoints fundamental behaviors common to various threat variants (such as the fact that newly-deployed malware will usually reach out to a C2 server), allowing for consistent detection across differing scenarios. On the other hand, unsupervised machine learning algorithms sift through enterprise data on a far vaster scale, performing billions of probability-based calculations from observed data. These algorithms do not rely on previous threat knowledge but independently categorize data and identify significant patterns.
Essentially, unsupervised algorithms allow NDR tools to establish a baseline of what is normal for your network. They then allow your SOC team to see any unusual connections that suddenly pop up: these can be indicators of a supply chain attack if they suddenly sprout from one source; or, if more data than average is being sent to an external device, it can be evidence of a malicious or compromised user. Both supervised and unsupervised learning models are important, as collectively they cover the full breadth of behavioral analysis that your networks need.
Threat Intelligence
Integrating with threat intelligence feeds allows the NDR system to cross-reference network activity against known threats, malicious IP addresses, and indicators of compromise (IoCs). This helps the NDR solution identify and detect threats that have been observed and documented by the broader security community. When coupled up with NDR solutions, threat intelligence feeds act as rapid and accurate context providers. This goes far beyond the basic malware signatures of old, with market-leading threat intel feeds including the tactics, techniques, and procedures of the most recent attacks, as well as the impact thereof.
This contextual information helps an NDR solution better understand the nature of each detected anomaly and make more informed decisions about the appropriate response. This support for your analysts can be deepened by further integration with the MITRE ATT&CK framework, as well as some extra support from the tools already keeping the rest of your organization safe.
This contextual information helps an NDR solution better understand the nature of each detected anomaly and make more informed decisions about the appropriate response. This support for your analysts can be deepened by further integration with the MITRE ATT&CK framework, as well as some extra support from the tools already keeping the rest of your organization safe.
Security Tech Stack Integration
You wouldn’t limit a security analyst to just one platform – just as third-party intelligence feeds provide context about the threats out there, your wider tech stack can offer a bespoke view of your own landscape. When NDR integrates with Endpoint Detection and Response (EDR) and Security Information and Event Management (SIEM) tools you already have, your teams are able to perform at the same multi-faceted level as attackers.
Take the MITRE ATT&CK framework: while explicitly developed to identify Tactics, Techniques, and Procedures (TTPs), MITRE ATT&CK holds a significant bias toward endpoint tactics. Thanks to this, EDR has seen a phase of significant investment across industries. This is perfectly understandable: matching your tooling to the industry-leading frameworks is a step in the right direction. Despite this, it’s vital to keep an eye on the reality of vulnerability exploitation. When an attack campaign is winding up, many critical techniques are actually easier to detect from a network perspective. In this same timeframe of a late-stage attack, the minutes are slipping by with incredible speed – by reducing the importance of network activities, some organizations are actually impairing their security response potential at the time it’s most critical. Analyzing and correlating data from across both endpoints and network sources allows analysts the full spectrum of visibility.
Take the MITRE ATT&CK framework: while explicitly developed to identify Tactics, Techniques, and Procedures (TTPs), MITRE ATT&CK holds a significant bias toward endpoint tactics. Thanks to this, EDR has seen a phase of significant investment across industries. This is perfectly understandable: matching your tooling to the industry-leading frameworks is a step in the right direction. Despite this, it’s vital to keep an eye on the reality of vulnerability exploitation. When an attack campaign is winding up, many critical techniques are actually easier to detect from a network perspective. In this same timeframe of a late-stage attack, the minutes are slipping by with incredible speed – by reducing the importance of network activities, some organizations are actually impairing their security response potential at the time it’s most critical. Analyzing and correlating data from across both endpoints and network sources allows analysts the full spectrum of visibility.
Automate Network Detection & Response with Stellar Cyber
When NDR detects suspicious or malicious activity within the network, this data needs to be funneled to your security team with maximum clarity and efficiency. In critical security events, every second matters. Traditionally, network-based alerts would be sent to the same alert lists as everything else, leading to miles-long backlogs that eat more and more precious time from your security analysts’ limited hours. Modern NDRs recognize that endless alert streams damage organizational security in their own way: instead, they aim to collate individual issues into wider, contextual alerts.
By connecting up to your wider suite of defense tools, an automated NDR solution can take on some of the busy work that is slowing down your security teams today. Automated manual triaging is still shockingly linear – and slow. So while a multi-faceted response is able to push threat detection to new heights, the weak link still remains the fact that analysts can only process so much information at any one time. Enter, algorithms.
This increasingly holistic approach to security is the foundation of Extended Detection and Response. Instead of loading up analysts with more and more toolings, dashboards, and alerts, the new phase of cybersecurity aims to make use of the wide swathes of information already at your fingertips via automation.
Stellar Cyber’s Open XDR platform takes the capabilities of isolated NDR and couples them with EDR and automation algorithms. This way, your security is more than a skin-deep analysis of each isolated area of your tech stack: instead, an alert from one device can be compared and contrasted against the network activity associated therewith. Not only does more information aid a security analyst in comprehensively understanding the nature and potential impact of the detected threat, but a reliance on advanced analytics allows every aspect to influence the severity level of an alert.
By pre-baking an alert with its criticality, Stellar Cyber’s XDR tooling is able to quickly and easily see the potential impact and likelihood of exploitation. This automation is key to not just handling vast amounts of network data, but identifying the anomalies and concerns that genuinely need fixing. Re-evaluate your organization’s relationship with network alerts today and see how Stellar guides security teams to faster resolution times than ever before.
By connecting up to your wider suite of defense tools, an automated NDR solution can take on some of the busy work that is slowing down your security teams today. Automated manual triaging is still shockingly linear – and slow. So while a multi-faceted response is able to push threat detection to new heights, the weak link still remains the fact that analysts can only process so much information at any one time. Enter, algorithms.
This increasingly holistic approach to security is the foundation of Extended Detection and Response. Instead of loading up analysts with more and more toolings, dashboards, and alerts, the new phase of cybersecurity aims to make use of the wide swathes of information already at your fingertips via automation.
Stellar Cyber’s Open XDR platform takes the capabilities of isolated NDR and couples them with EDR and automation algorithms. This way, your security is more than a skin-deep analysis of each isolated area of your tech stack: instead, an alert from one device can be compared and contrasted against the network activity associated therewith. Not only does more information aid a security analyst in comprehensively understanding the nature and potential impact of the detected threat, but a reliance on advanced analytics allows every aspect to influence the severity level of an alert.
By pre-baking an alert with its criticality, Stellar Cyber’s XDR tooling is able to quickly and easily see the potential impact and likelihood of exploitation. This automation is key to not just handling vast amounts of network data, but identifying the anomalies and concerns that genuinely need fixing. Re-evaluate your organization’s relationship with network alerts today and see how Stellar guides security teams to faster resolution times than ever before.