How to Automate Network Analytics with XDR
What Is Network Analytics?
Network analytics is the application of big data principles and tools to the management and security of data networks. Analytics helps IT improve security, fine-tune network performance, troubleshoot subtle issues, and predict traffic trends. It can also help identify future problems and conduct detailed forensic investigations and audits.
To take advantage of network analytics, businesses need an infrastructure capable of generating network performance and utilization data. Data can include low-level information such as bit rates, collision, packet drop rates, and delays over specific physical network ports. It can also include high-level information like packets subject to a specific security policy, originating from a specific address, or destined for a specific address. Businesses also need systems to collect, store and analyze these vast amounts of network data.
Cloud-based services are making network data analytics available to enterprises and small businesses alike. As tools become easier to use, standardized and affordable, the technology becomes accessible to organizations of any size. Any organization with highly complex networks, overloaded networks, or stringent security requirements can benefit from network analysis tools.
In addition, organizations making use of the Internet of Things (IoT) and microservices can expect to exponentially increase the number and types of entities on their network, generating enormous amounts of network data. This is an even bigger driver for adoption of network analytics.
Why Is Network Analytics Important?
Network Analytics provides deep insight into IT networks and helps administrators make informed business decisions. This is particularly useful for preventing, detecting, and responding to security threats.
The complexity of IT networks has resulted in a larger attack surface. Traditional security measures are no longer sufficient to prevent these threats. Network analytics play an important role in detecting network anomalies and alerting administrators to prevent or respond to security breaches.
Network managers also rely on network analytics to optimize their infrastructure. Administrators gain a deep understanding of networks that allow them to perform complex network planning and sizing tasks.
Network analytics can support and transform many industries. It plays an important role in increasing network efficiency and reducing operational costs, while improving security and supporting capacity planning, optimization, and service assurance.
What is Extended Detection and Response (XDR)?
Extended Detection and Response (XDR) provides data visibility across networks, clouds, endpoints and applications, while applying analytics and automation to detect, analyze, remediate threats.
XDR collects and correlates data across the IT environment. This provides better visibility and context into threats, which can lead to a higher level of awareness of incidents. Security teams can assess the severity and severity of an attack, and quickly act to eliminate further impact.
XDR provides a proactive approach to:
- Maximizing the efficiency of data collected from your existing security and IT investments, and transforming it into contextual information.
- Identifying hidden threats using advanced behavioral models powered by machine learning.
- Identifying and correlating threats at multiple layers of your network or application stack.
- Minimizing alert fatigue by providing accurate alerts and reducing false positives.
- Integrating multiple signals and providing forensic capabilities to build a complete attack graph, which security professionals can use for rapid investigation.
How XDR Works
XDR works by integrating and analyzing data from multiple security tools and systems in order to detect and respond to threats more effectively. Here is a general outline of how XDR works:
- Data collection: XDR involves collecting data from a range of security tools and systems, including endpoint security, network security, security information and event management (SIEM), and cloud security. It may also include data from other sources such as log files, user activity logs, and application logs.
- Data integration: The collected data is then integrated and normalized, so that it can be analyzed and compared across different sources.
- Threat detection: Advanced analytics and machine learning algorithms are used to analyze the integrated data in order to identify and prioritize threats. This can include identifying anomalies or suspicious activity, as well as detecting known threats such as malware or phishing attacks.
- Threat response: Once a threat has been detected, XDR can take a number of actions to respond to the threat, such as blocking access to a malicious website, quarantining a infected file, or alerting security teams to take manual action.
- Continuous monitoring: XDR systems typically operate in real-time, continuously collecting and analyzing data in order to detect and respond to threats as they arise.
By integrating and analyzing data from multiple sources, XDR can provide a more comprehensive and cohesive view of an organization’s security posture, and enable security teams to respond to threats more quickly and effectively.
How to Automate Network Analytics with XDR
To catch enemies lurking in your network, you need to combine the right data with behavioral analytics and machine learning. In addition to Internet traffic, internal communication between users and servers is monitored, to detect post-compromise activities such as lateral movement and exfiltration.
Most security teams today lack visibility into some systems, especially unmanaged endpoints. Instead of stopping attacks, analysts waste time sorting through incomplete and inaccurate alerts and manually gathering investigation clues. A new approach is needed to enable efficient security operations.
With XDR, you can quickly find and block even highly sophisticated and evasive cyberthreats. When configured for network analysis, XDR uses machine learning to analyze rich network data and uncover targeted attacks, malicious insiders, and compromised endpoints with high accuracy. Viewing actionable alerts allows analysts to quickly identify and stop threats before any damage is done.
XDR solutions block all phases of an attack. XDR analyzes behavior and detects anomalies that indicate adversary tactics, detecting command and control, lateral movement, data exfiltration, and malware activity.
XDR solutions automate post-breach activity to reduce mean time to detection (MTTD), and ensure there are no active attackers on the network. XDR solutions continuously analyze user and endpoint behavior to detect anomalous activity indicative of an attack. XDR goes beyond the detection capabilities of traditional, siloed tools, by applying analytics to integrated data sets including rich network, endpoint and cloud logs, automatically discovering threats and improving the productivity of security analysts.
Conclusion
In conclusion, automating network analytics with XDR can help organizations to gain a more comprehensive and cohesive view of their network, and to respond to threats more quickly and effectively. By collecting and integrating data from multiple sources, using automated analytics tools, setting up automated alerts, and automating response actions, organizations can save time and resources and improve their security posture.
Author Bio: Gilad David Maayan
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Check Point, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.
