Network Digital Twin Solutions

A Network Digital Twin refers to a computer simulation model of the communication network, along with its operating environment and the application traffic that it carries. The digital twin can be used to study the behavior of its physical counterpart under a diverse set of operating conditions, including cyber attacks, in a low-cost and zero-risk environment. However, to do so effectively, the digital twin must have sufficient fidelity to accurately reflect the network dynamics that can cause networks to behave unpredictably. The network dynamics are typically created by the interplay among the communication protocol, device configurations, network topology, application traffic, the physical environment, and any cyber threats.

 

In addition, live hardware and software applications can be seamlessly interfaced with, or integrated into, a network digital twin that executes in real-time. These real-time network digital twins can then be used to improve management, performance, and cyber resilience of networks in all domains, from commercial enterprise IoT to military networked systems operating from seabed to space.

Network Digital Twin for Cyber Analysis Bundle

SCALABLE’s network digital twin cyber solutions are used by our customers to assess the resiliency of networked communication environments to diverse cyber threats and to enable system lifecycle management and operator training. Our cyber security tools allow the customer to explore and analyze live early-stage device designs, application code response, and overall communications effectiveness. This is done in a highly realistic synthetic network at real-time speed to provide a complete cyber security defense solution. In addition, we offer you the ability to evaluate your network design, assess your information security, and prepare a cyber threat assessment on its resiliency.

 

When creating an accurate network digital twin of the physical network, several components are required, including network model libraries. Similar to how a blend of hardware and software helps construct live networks, a variety of network protocols, equipment, radio waveforms, and other elements combine to create network digital twins. Network model libraries include communications systems model libraries, which feature various definitions of network protocols and communications equipment waveforms. SCALABLE developed a cyber analysis bundle with specific network model libraries to support customers looking to model and analyze the cyber resiliency of their networks.

 

Cyber Security Tools enables users to:

 

• Optimize the network, systems, and applications performance and the physical structure to mitigate risks

• Identify issues, bottlenecks, and failure modes before they impact users

• Create best practices to increase systems availability and lower operational risks

• Quantify potential business or mission impacts due to cyber attacks and the resulting network disruptions

• Replicate specific environments in a lab and quickly evaluate “what if” scenarios to determine what happens to physical systems if they are subject to cyber attacks

Network Digital Tiwns for Analysis Bundles to support Enterprise, Autonomous Technology, and Critical Infrastructure

SCALABLE provides unique network digital twin solutions to commercial enterprises, government and defense agencies, research organizations, and educational institutions around the world. The software enables customers to analyze and predict the network performance of communication technologies prior to deployment. Network digital twins offer organizations a solution to predict how the networked enterprise, or at least the critical sub-systems, will behave when under cyber attack. To assure that cyber resilience is not a one-time effort, IT managers and network planners must continuously assess their current network’s vulnerabilities to known and emerging threats and proactively update their strategies.

 

Safely Relying on Digital Twin Technology

Cyber-Physical Systems

SCALABLE is leveraging its experience in network and cyber emulation technologies to model cyber-physical systems (CPS) systems, people, and processes to evaluate their cyber resiliency.  SNT’s technologies “mimic” the functions of a real CPS network so that the model appears, interacts, and behaves like the real CPS network. The emulator provides an exact, high quality, reproduction of external behavior so that the emulated system is indistinguishable from the real system and can serve as a lab based system integration, analysis, and test tool. It includes the capabilities to collect, report, and visualize a comprehensive set of statistical data that can be used to derive appropriate Measures of Performance (MOPs) from the emulated network under various operating conditions, including when exposed to cyber-physical threats.

 

Autonomous Vehicles

One way our network digital twin modeling and simulation tools are used throughout the world is to make vehicles safer. They provide a powerful capability for simulation of networks that incorporate a diverse variety of environmental, protocol, mobility, and network traffic configurations for urban environments, vehicle mobility, fading, shadowing, path loss and interference, 802.11p, LTE, and 5G. Digital twin technology lowers development costs by drastically reducing time to test wireless network performance, reliability, and security under varying environmental conditions, including urban environments with thousands of moving vehicles.

 

LTE/5G

Creating network digital twins of 5G mobile networks will enable effective proficiency training, cyber situational awareness, network analysis, and mission rehearsal exercise support for cyber-physical activities. They will also accurately represent and be interoperable with the new frequencies available with 5G (e.g.,mmWave and cmWave) Access Points (AP), smart buildings and homes, mobile-to-mobile, telematics, and sensor networks. As industry leaders in network digital twin technology, SCALABLE developed an accurate 5G model library to help evaluate the performance and behavior of 5G cellular networks. Our modeling and analysis tools give our customers the competitive edge to understand operational and business challenges across the 5G lifecycle.

 

Enterprise Systems

Enterprise networks are increasing in complexity, deploying new and legacy systems and applications and, increasingly, cloud-based solutions. To ensure that the network continues to provide services even when under cyber attack, it is critical to understand how it behaves under different types of cyber attacks and to pro-actively prepare counter-measures. Network simulation using a network digital twin offers a zero-risk, low cost alternative for studying network behavior under a comprehensive set of cyber attacks.

Network Digital Twins for Wargaming and Operational Analysis Bundles to Support All Defense Domains

Modern warfare requires integration across all domains: land, sea, air, space, and cyber. This creates an urgent need for wargames to incorporate real-world cyber, communications, and networking effects to support development of effective operating concepts, capabilities, and plans. The complexity of a multi-domain, combined cyber and kinetic battlefield requires incorporation of high fidelity, physics-based network digital twins into future wargame environments to adequately account for potential impacts resulting from degraded network operations and/or cyber vulnerabilities on overall mission outcomes.

 

Our network digital twins provide design and analysis tools and cyber training systems to develop, test and deploy large sophisticated wired and wireless networks and communications equipment. It can be used to enhance overall wargame fidelity in a low-cost and zero-risk environment, improving the knowledge and insights gained from wargame execution.

 

Network digital twins offer a realistic network platform for war games to model all aspects of the mission. The wargaming platform then handles the platform mobility and the kinetic missions, while the network digital twin handles the underlying communication network and cyber effects.

 

Benefits of Network Digital Twins for Cyber Resilience Testing

Large-scale wargaming

It is difficult to deploy live large-scale wargame scenarios or missions due to time and resource constraints. Using a network digital twin, it is possible to recreate the whole or a segment of the scenario, making large scale wargames easier to conduct. This facilitates at-scale testing of new tactics and strategies for future conflicts.

 

Cyber threats

Network digital twins can make it easier to scale the scope of cyber resilience tests by allowing many cyberattacks, for example, vulnerability exploitation, virus/worm propagation, or Distributed Denial of Service (DDoS), to be executed on models without the risk of damaging or compromising the live system.

 

Extending a test

Often, post-test analysis and After-Action Review (AAR) identify further tests that could lead to a more thorough understanding of the system’s operation and potential means to mitigate threats. For example, the results of a test may highlight the need to investigate what might happen if a compromised system were removed from the network and traffic allowed to travel through an alternate route and the impact of this change on the flow of mission sensitive data. This test can be performed swiftly and with minimum effort when using a network digital twin.

 

Effective test planning

When using a network digital twin, multiple configurations or profiles can be examined to set a priority for tests or scenarios to run and help select the most effective scenarios.

Network digital twin analysis can also integrate easily into machine learning (ML) and artificial intelligence (AI) to help bring the data and the context of the data together, allowing the scenario designer to test alternatives and possibly find issues in the scenario before they arise. This can be done using faster than real time execution of the wargaming platform in conjunction with the network digital twin and feeding the results into a machine learning algorithm and allowing the algorithm to recommend modifications to the parameters being examined.