NIKSUN® - Network Threat Detection

What is network threat detection?

Network threat detection, or network detection monitoring (NDM), refers to technologies and practices you can use to identify and mitigate malicious activities in your network infrastructure as they develop – before they can cause harm.

How does it work?

Network threat detection or NDM works by continuously monitoring your network traffic, analyzing data patterns, and correlating this data with policies and live sources, such as threat intelligence (TI), including blacklisted IP feeds, to detect and alarm on anomalies and other markers of malicious activity.

Who uses it and why?

Every organization and business should have access to threat intelligence to identify and respond to security risks and vulnerabilities in real time, often in the form of a Threat Intelligence Platform (TIP). When companies apply the tools and practices of network threat detection to their cybersecurity efforts, they see benefits, including:

Early detection – Anomaly-based detection and other tools can help you identify malicious activities as they emerge in the threat landscape, giving you the opportunity to defend against them proactively
Better insights – Uninterrupted monitoring provides visibility into what's happening in your network at any moment and over time so you can detect a wide range of patterns and events – from security breaches to outdated operating systems, poor performance, and downtime
Compliance support – Continuous monitoring also helps you maintain your regulatory compliance posture and track your status against vulnerabilities such as CVEs, as is required by recent legislation, including NIST 800-171 and CMMC 2.0

Key components

Key components of network threat detection and intelligence include:

Intrusion Detection Systems (IDS) – Monitoring network traffic for suspicious activities using signature-based detection, which identifies known threats by matching traffic patterns to predefined signatures
Content-based alerting – Discovering malicious files and other content traversing the network by matching against known-bad malwares and ransomwares
Anomaly-based detection – Identifying deviations from normal and/or expected behavior, which may signal a potential threat
AI/ML heuristics – Using machine learning and artificial intelligence to enhance the accuracy, speed, and success of your threat detection tools and practices by training models against large data sets for better predictive models of possible malicious activities
Blacklisting – Identifying and blocking specific IP addresses, domains, or other entities known to be malicious or harmful

Let's get some context

The threat detection market expanded significantly because of the surge in cyberattacks and increased connectivity in the 1990s and early 2000s. As demand drove innovation, signature-based technologies, which rely on pattern matching and known threats, were augmented by anomaly-based approaches, which look for deviations in expected behavior. This approach became required as the cybersecurity industry quickly realized that focusing detection only on known threat vectors would inevitably fail as it would fall short in detecting major intrusions such as zero-day cyberattacks. These techniques became increasingly important in the move to Zero Trust Architectures (ZTAs), where organizations implement further verification standards for employees to access data and applications. More recently, machine learning (ML) and artificial intelligence (AI) have enhanced network threat detection capabilities by supporting their operation at extreme scale and with more accurate predictive modeling.

So, what's the problem?

While we've evolved to the point where there's decent technology in each of these individual sectors for gathering meaningful threat intelligence, and while the sectors themselves keep growing more effective, these approaches remain disconnected or siloed. Most organizations manage and operate them as discrete technologies. That means an organization is likely to be running multiple threat intelligence tools, and each may be managed by a team responsible for understanding and working with that toolset on a daily basis, tuning it for effective outcomes, and investigating the alerts and alarms it produces in yet more tools that add details and context to the potential threats. Meanwhile, the various tools organizations leverage all collect, format, and aggregate data in different ways, furthering their lack of interoperability and keeping them in discrete siloes.

The resulting problem is two-fold:

The cost and time investment for such an approach is much, much higher than it needs to be as organizations must not only invest in several tools for each type of threat intelligence, but also in systems integration to attempt to tie their results together
Gaps in threat intelligence exist because each siloed component and its team is specialized and therefore able to capture and deliver only a portion of the overall picture; in other words, major blind-spots get left “between the tools” which are inevitably where hackers and other nefarious actors focus their activities

More challenges

In addition to the fundamental problem of siloing, there are other challenges specific to different tools:

Signature-based detection can be challenging to do at scale for network traffic, where upwards of trillions of packets traversing an organization's infrastructure need to be matched against known patterns as they move in real time. How can a tool perform this operation at a fast enough rate to tell you about the problem as it happens so you can take immediate action and not fall victim to the attack vector?
In addition to having the challenge of scalability, blacklisting is only as effective as your list is accurate. An out-of-date or incorrect list can point you to people and IPs that aren't really threat actors and fail to include ones that are. How can you make sure you're not wasting resources and/or putting your company at risk because of a bad list?
Anomaly detection looks at behavioral patterns to find activities or events outside of the norm and tells you about them. The challenge with anomaly-based detection is the creation of false positives. How can you make sure you're spending resources investigating actual bad actors (for example, a true brute force attack) and not just an employee mis-remembering their own passwords, for instance?
AI/ML is only as good as the data you're feeding it. Garbage in, garbage out, as the saying goes. How can you make sure your AI/ML capabilities are learning from adequate amounts of accurate and complete data for effective predictive modeling and threat detection?

NIKSUN has the solution

Let's look at how NIKSUN can solve all these challenges for network threat detection.

Problem: Siloed threat detection components are costly, time intensive, and return incomplete intelligence

Solution: NIKSUN solves this problem by putting all tools, operations, analytics, and alarming in one unified platform that supports robust hybrid deployments. NIKSUN's unique platform encompasses all network threat detection or NDM approaches, including IDS, content-based alerting, anomaly and behavioral alerting, and AI/ML heuristics, making for the most comprehensive attack intelligence on the market today. Importantly, this cutting-edge threat detection runs across your entire infrastructure – from cloud to virtual to on-prem and hybrid, and from the network to your endpoints and applications. It also runs across all your data – packets, flows, logs, device metrics via SNMP, and more. In addition, once threats are identified, the NIKSUN platform immediately gives you the “who, what, where, when, and how” for the incident, no matter whether it is a cybersecurity issue, compliance problem, network to application performance incident, or otherwise.

Plus, NIKSUN's affordable platform serves all team members via role-based access, so there's only one UI to learn and one toolset to manage – no need for multiple teams dedicated to different monitoring tools. Instead, everyone from data analysts to C-suite executives are on the same page and get comprehensive intelligence that's easy to drill, filter, and alarm on.

Problem: Signature-based network threat detection relies on pattern matching at scale.

Solution: For decades, NIKSUN been in the business of performing signature-based detection at extreme scale and in real time for customers. Indeed, NIKSUN is the chosen provider of Full Packet Capture for the U.S. Department of Defense (DoD) in the Defense Information System Agency (DISA)'s network protection program. From its inception, NIKSUN has been committed to providing network threat detection at this level of scalability and the NIKSUN platform is able to run over a hundred thousand threat intelligence rules across multi-Tbps of network traffic without missing even a single packet.

Problem: Blacklisting must be current to be beneficial.

Solution: NIKSUN's in-house teams work constantly to keep NIKSUN threat lists current, with experts updating threat intelligence policies to make sure we uncover new threat actors and keep our lists up-to-date, relevant, and accurate. NIKSUN's platform also allows third-party feeds to be run against its own database.

Problem: Anomaly detection can create false positives, diminishing your resources for true threats.

Solution: NIKSUN solves this problem by not only creating consistently robust heuristic-based alarms that alert you to actual incidents across all domains (NPM, APM, Security, etc.), but also by ingesting a lot of data. The more data you have, the better anomaly detection you can do because having large data sources makes it easier to correctly differentiate the exception from the norm. With limited data, it's difficult to achieve that level of nuance. NIKSUN's zero-loss data capture and warehousing technology works at any scale to ensure all the data is ingested and accounted for from every source type (packets, logs, flows, SNMP, and more), in one unified, cross-correlated, high scale database.

Problem: AI/ML can do more harm than good if the data it's learning from isn't accurate and comprehensive.

Solution: Because NIKSUN has all the data – packets, logs, flows, SNMP, and more – at extremely high scale, we can train our AI/ML technology to be accurate and useful in its assistive role. It can help you write better rules and alarms and help find data you care about in investigations, for example.

More about NIKSUN's Platform

Delivers comprehensive intelligence, from executive-level insights to SOC root-cause analysis in one-to-zero clicks with drilldown options, and no hopping between tools
Provides the opportunity to leverage its platform at extremely minimal cost, with licensing available for the end-to-end platform, or for specific product-sets, including Security Information and Event Management – SIEM (LogWave), Network Detection and Response – NDR (NetDetectorLive), SecOps Analytics (FlowAggregator), Central Management (NetOmni), and more
Includes robust training/assistance and rapid turn-arounds on requested enhancements
Leverages a security-hardened platform with certifications that include Common Criteria, DISA STIG compliance, and placement on the U.S. Department of Defense's Approved Products List (APL)

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More about NIKSUN

NIKSUN is the recognized world leader in empowering organizations to Know the Unknown^®. Since 1997, we have been committed to delivering the most innovative solutions for securing and optimizing the networks of over a thousand customers including Fortune 500 companies, government agencies, and service providers.

Our industry leading suite of scalable, forensics-based cyber security and network performance monitoring products provide customers with in-depth and actionable insight into security threats, performance issues, and compliance risks. NIKSUN's patented real-time analysis and recording technology is the industry's most comprehensive solution for securing and maintaining dynamic network infrastructure.