Cyber network analytics is a necessary part of protecting your network, IT assets, applications, and team members from a daily barrage of cyber threats and attacks.
Small businesses, enterprise organizations, and government agencies are all at risk and should prioritize cybersecurity.
Large organizations have traditionally used complex technologies (such as Security Information and Event Management Systems) to collect and aggregate cyber feeds and events — all while integrating with specific technologies focused on identity management, endpoint protection, virus and malware protection, and network security.
Core to understanding the day-to-day patterns and health of an organization’s network is the practice of Network Analytics, which we’ll break down in this article.
What is Cyber Network Analytics?
Network Analytics is the continuous process of monitoring network traffic, along with network users, devices, and applications interacting with the network, to comprehend network patterns and health.
This continuous process is essential to comprehending normal network behaviors and deeply understanding your network's daily, weekly, monthly, and yearly patterns.
Network Analytics provides comprehensive context, including identifying the users connected to your network, their interactions with devices, their network access locations, the types of data they share, and more.
All this information is vital to gain an understanding of the normal traffic patterns of a specific network. You need this understanding of normal traffic patterns if you want to identify network anomalies or potential threats actively operating on the network.
Much easier said than done, especially since network boundaries have morphed in size and shape over the last few years.
COVID-19 pushed many organizations to allow remote and hybrid work options. This change in work options resulted in:
- Larger network structures
- Larger surface area or digital footprint
- Far more access points to the network.
These larger networks have increased the attack surface, leaving organizations far more vulnerable to cyber attacks than they were pre-COVID.
Why does Cyber Network Analytics matter?
Network Analytics is pivotal to understanding the core posture of an existing network.
With the complexities of network configurations, various applications, and mixed work locations of employees, contractors, and guests, the task of tracking and understanding normal network behavior is extremely complex.
No matter the size of an organization, Network Analytics is a 24x7 process that is a core function of IT or Cyber Operations teams.
With so much at stake in monitoring and protecting the network, organizations typically depend on a combination of human and advanced software for monitoring.
Shortage of trained professionals
Finding skilled professionals who are willing to work various shifts to monitor the network is challenging.
Additionally, while employees are monitoring the network, they are also expected to participate in additional cyber activities if there are no known active risks.
Due to the skills required, variations in shift schedules, and complex work requirements, attracting and retaining skilled resources to work in a Cyber Operations team is challenging.
As a result, the majority of organizations utilize Network Analytics software to monitor network traffic and provide alerts and reports for humans to review and focus their efforts.
The downside of this type of software: if not tuned correctly, it also produces a massive amount of false alarms.
These false alerts force teams to quickly switch from previous tasks and evaluate the alert. When an alert is incorrectly triggered, this takes valuable time away from the already busy cyber team.
Alert Fatigue
Speaking of alerts, another issue facing cyber professionals is “alert fatigue”.
Cyber operations centers are heavily dependent on technologies that produce a plethora of reports, logs, and alerts — all that need to be reviewed by the team.
Those reports, logs, and alerts can bombard even the largest of teams. As a result, many alerts get muted and are never reviewed by cyber analysts.
While some of those alerts may seem mundane, not all threats are labeled correctly by network monitoring technologies. Muting alerts can lead to cyber threats and vulnerabilities going undetected for weeks or months.
During the 2020 SolarWinds Hack, attackers had for over 14 months access to the networks, systems, and data of thousands of SolarWinds customers, including the federal government.
As part of the analysis of the SolarWinds attack, the attackers were able to avoid detection by mimicking legitimate network traffic and circumvent threat detection procedures used by trained cyber teams.
How Do Cyber Network Analytics Help?
There are two main ways cyber network analytics can support your organization:
Evolution of Enterprise Network
As organizations continue to evolve their hybrid or remote work policies, they need to improve the ways they protect their network, devices, and employees.
Pushing the network outside of a single building into multiple office locations, employees’ homes, coffee shops, co-working spaces, and any other location with a Wi-Fi signal, poses significant threats to the network.
Add to that employees, contractors, and partners company’s ability to bring their own devices onto the network to access enterprise applications.
With all these changes, the technologies and processes required to monitor networks quickly become outdated.
Many organizations have evolved from using Virtual Private Networks (VPNs) to allow their external employees and partners to connect to enterprise assets to using Software Defined Perimeters (SDP).
Software Defined Perimeters allow organizations to fully control what device types, locations, and times users and their devices can access the network.
Key to the operation of an SDP is the client installed on devices accessing the network. The client enables organizations to maintain a secure environment and begin the evolution to zero-trust access patterns.
By utilizing the software client on the end-point, organizations can create user profiles and enforce specific hardware, geography, and date/time requirements that the user device and user must comply with before access is authorized.
If a user or device doesn't meet any of the policy requirements established and enforced by the SDP, users or devices are prevented from gaining access to the network.
Traffic patterns
Another major benefit of using the newer network security technologies is the richness of data it can provide, such as:
- Change in network volume to a new location
- Frequent changes in geographical location
- Unusual volumes for a user or location
- High volume over unusual port or protocol
This additional data allows organizations to monitor devices and users actively accessing the network, further enabling a detailed understanding of network traffic patterns that is critical to understanding the health of the network.
Feeding network analytic capabilities like this make it easier to pinpoint traffic to specific users and devices, as well as exact user, device, and geographic locations. All of these new data attributes are available to be included in new Network Analytics capabilities that continuously monitor the network for anomalies.
The Cost of Cyber Network Analytics
Numerous organizations are developing capabilities to extend the capabilities of Network Analytics to keep pace with ever-evolving cyber threats.
Businesses and government agencies must continue to implement new techniques and technologies to deliver advanced analytics on existing networks.
There is a cost to acquire, deploy, and operate network analytics capabilities, which is a major issue for a lot of organizations.
Some have already invested millions of dollars into existing technologies, processes, and staff and in many cases struggle to add new and innovative technologies. These struggles are primarily due to existing cyber budgets and limited access to highly skilled resources capable of delivering AI/ML cyber capabilities.
A Better Way Forward
With the high cost of cyber solutions, organizations need faster and easier ways to monitor their network.
Solutions that can automatically remediate simple threats and vulnerabilities and provide timely and relevant alerts requiring human interaction are the future.
Cyber Security and Network Analytics need to continue to evolve away from rules, patterns, and simple analytics and further embrace AI/ML solutions that are constantly monitoring and learning authorized network traffic.
With the continued advancements of open-source technologies that provide frameworks to accelerate the development and operations of AI/ML Deep Learning models and Large Language Models, organizations can take advantage of new capabilities to monitor and learn network patterns that can quickly and securely learn network patterns and users.
With the recent advancements in Large Language Models (LLMs), models can be trained to learn and answer questions about the network.
Combining these can provide advanced capabilities to support the existing cyber teams and tools to continuously protect organizations' networks, applications, and users.
An added bonus: these emerging technologies can enable teams with fewer resources the ability to review and process cyber threats and potential vulnerabilities!
Upgrade Your Network Analytics
To learn more about Ulap and how to evolve Network Analytics, contact Ulap at contact@ulap.co
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