5G – Security risks The introduction of 5G networks offers great opportunities for the mobile and global world. However, to successfully exploit this change, extensive preparation and planning are required. The new networks also bring changes that can be crucial for companies and their security. There is no question that 5G has a number of advantages – the first public 5G mobile networks have now been activated. In order to benefit from the new mobile communications standard, however, CISOs must first overcome new challenges in IT security and data handling.
What’s actually new about 5G?
Operating a telecom network is expensive. Costs are usually passed on to the customer. As technologies improve, performance increases – and so does the price for the end user. Unlike the hardware-based networks we have used for the past decades (IT, 1G-4G), the 5G network is software-defined: it can be reprogrammed Sweden Phone Number List according to customer requirements and thus changed quickly and frequently. Unfortunately, this data volatility has implications for network security. Correcting this presents a complex problem for identity and access management ( IAM ) and attribution, especially for ecosystems connected to the Internet of Things (IoT). This was not a problem before the emergence of the Internet of Things (IoT), as telecom providers only dealt with one type of participant: humans. Adding thousands of additional participant types means a massive increase in network complexity.
New standard – new problems
The data-driven, dynamic and thus unstable nature of 5G brings with it many fundamental changes. Many of these relate to identity and attribution, especially after the introduction of a second participant type – the machine participant, which consists of IoT devices. While security statistics were originally design to monitor only one known participant type (namely humans), IoT machine participants have hundreds of thousands of different participant types with their own network behavior, most of which are new and unknown. What is acceptable behavior for one type of machine participant could be an indication of a botnet infection in a second, similar IoT device with the same hardware. However, in the carrier network logs, both look identical, which can lead to confusion.
Since the carrier is the most important part of the data cell phone number listing supply chain of a 5G network, the inability to detect such a large number of IoT devices is a critical problem. Most carrier-dependent security applications, threat models, security vendors, and machine learning rules cannot tell the difference, which is why the behavioral models they apply do not work. They cannot therefore take security measures without affecting some IoT devices through a denial of service. However, without the ability to take effective action, affect companies will usually simply disable the “noisy” security feature. Effectively sabotaging their own network.
In addition, the security platforms
That IT security relies on today mostly use industry-accept security rules, which typically come from third parties. These rules work with industry-standard filter statistics such as blacklisting, signatures, network behavior and the like. However, these filters do not understand the intricacies of legitimate, albeit very Bulk Database individual, network traffic in software-define networks. They therefore recognize previously unknown activities as anomalies and generate false positives. These are then place on a whitelist and are thus “invisible” to the security solution. As a result, the hardware-base security solutions that perform blacklisting are burden by increas network traffic. While they are already struggling with the volatility of software-define networks. This will further reduce the effectiveness of the security architectures known to date.
