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SDN and Network Security: Auto...

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SDN and Network Security: Automating Defense in Software-Defined Networking

SDN and Network Security: Automating Defense in Software-Defined Networking
The Silicon Review
26 December, 2024

Networks are a highly desirable target for attackers, but they are not always protected accordingly. Software-defined networking adds complexity to this problem as it creates environments that are always online (and therefore always exposed), more complex than traditional setups, and more prone to exploitation.

To address network security, organizations must evaluate their networks and ensure that they are protected by automated, sophisticated solutions. Implementations should prioritize automation, both to decrease response times and to conserve resources. Threat detection tools are another important component, but to keep up with attackers, they should be built on AI and machine learning. As the landscape of potential attacks changes, the approach to network security should too.

The Transformation of Network Security Paradigms

Software-defined networking (SDN), designed to facilitate managing a network and increase adaptability, has changed substantially in its lifetime. Before the advent of SDN, network control occurred on hardware almost exclusively. While hardware is still involved in network infrastructure, it is controlled through APIs rather than hardware like switches and routers.

Although hardware is a highly effective way of managing a network, physical infrastructure has the problem of limited space that can create challenges. To avoid this problem, large organizations with growing networks sometimes transition to SDN. Software, often cloud-based, controls the network.

While this can reduce costs (or at least make them more predictable) and improve efficiency, there is a tradeoff. Challenges with this type of networking are often security-related. Because software used in SDN is exposed to the web, it is inherently vulnerable to exploitation. Modern network protection can be tricky because of vulnerabilities in APIs as well.

So, organizations need to invest in dynamic, intelligent security solutions. Ideally, these solutions will involve both internal and external protective measures. Useful tools include:

  • WAF. A web application firewall protects networks from attack by acting as a barrier between web traffic and network access points.
  • WAAP. Like a WAF, Web Application and API Protection services block attacks on access points, but WAAP focuses more on API security. Since SDN often relies on APIs, WAAP is not a tool to ignore.
  • IPS. Intrusion protection systems monitor for known attacks and block them once they begin. IPS is best suited for brute force attacks and works best if WAF or WAAP solutions are behind it.

As common practice for controlling and securing networks changes, organizations and their security teams need to stay current. An essential component of network security for modern companies running SDN is a suite of tools that can leverage automation and machine learning to detect and block attacks.

Effectively Defending Evolving SDN

SDN introduces vulnerabilities that traditional infrastructure does not; however, defending these vulnerabilities can be made easier by implementing tools that automate detection and response to attacks. Organizations searching for the right tools should look for a few critical functionalities:

  • Dynamic network segmentation techniques. Segmentation is important to limit the amount of access that a successful attacker is able to gain. It keeps disparate parts of the network from connecting, which means the attacker has a limited number of places that are accessible from the initial exploited access point. Dynamic segmentation enforces this limitation by directing traffic according to the user’s assigned role within the network.
  • Centralized policy management. Information silos can quickly become big problems for organizations. Limited communication, especially in security, can lead to inconsistent security practices, improper data handling, and a loss of comprehensive system knowledge. Centralized management provides unified policies that are enforced across the whole network.
  • Real-time threat response capabilities. As a rule, reacting to a potential attack more quickly means it is less likely to succeed. If it succeeds, fast reactions limit the damage done to your network and reduce the amount of data that is improperly accessed. So, any implemented tools should be able to raise alerts, block threats, and adapt to new and suspicious attack patterns.
  • Integration of artificial intelligence. Increasingly, security tools are using AI and machine learning to increase adaptability and reduce the intervention required of security teams. AI use can decrease response times while increasing the sensitivity and accuracy of detection.
  • Automated incident containment strategies. Realistically, it isn’t practical to assume that security teams will be able to respond to and contain an attack immediately. Many security professionals find themselves wading through tons of alerts and other noise, and even if they detect a problem right away, they don’t always have the time, resources, or understanding of the problem to fix it immediately. To streamline the incident response process, look for tools that can handle some of the minor incidents on their own.

Each of these goes a long way toward helping security teams improve and streamline SDN network security. Although it’s not possible for security teams to catch every attack, implementing automated solutions and threat detection can vastly reduce the number of incidents that teams need to address.

The Road Ahead: SDN and Future Network Security

As cloud storage becomes more ubiquitous and applications move to online hosting, companies are increasingly adopting SDN. Hardware-based networks are no longer practical for many organizations, especially those looking to scale up quickly. As more companies come to depend on SDN, security tools and solutions that defend networks automatically will be universally essential.

For many organizations, AI and machine learning are part of future network security. Securing networks with tools that use AI and machine learning helps security teams expand their capabilities. It allows them to focus on large, complex projects that they wouldn’t otherwise have time to build. This in turn improves strategic planning for network security and ultimately makes teams more proactive.

SDN is here to stay. To ensure their networks are secured against the vulnerabilities that come with it, organizations have to begin implementing the most robust network security solutions available. This requires a proactive approach that leverages tools like WAFs and WAAPs, automation, and AI and machine learning.

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