Collective Immunity: Russian Researchers Develop Technology to Shield Smart Devices From Complex Cyberattacks

Collective Digital Immunity

The project’s core innovation is a “collective digital immunity” concept. In a distributed IoT architecture, devices detect threats locally and exchange incident information with one another, reaching up to 95% accuracy in recognizing complex multivector attacks – a level comparable to traditional security systems. The differentiator is speed: incident data spreads across the network in about five seconds, prompting nodes to activate defenses almost immediately, in a process that resembles biological immune response.

Strengthening protection of critical infrastructure is increasingly treated as a baseline element of national security. The project reinforces Russia’s technological sovereignty in IoT by reducing dependence on foreign software in high-stakes digital segments.

The team also positions the system as exportable. The approach can be adapted to international IoT security markets, especially for sectors with strict data protection requirements, including smart city deployments, healthcare and transportation.

Expanding Scientific Collaboration

The development is relevant to securing Russian IoT networks at multiple levels and aligns with the broader trend toward decentralized cybersecurity, often described as edge security. By distributing detection and response, the technology can reduce load on central communication nodes and minimize the risk of large-scale data leaks. The project’s support from the Russian Science Foundation signals high institutional confidence in its scientific value.

The project also has international potential. Built on machine learning algorithms and distributed protocols, it could be adapted for foreign markets and, over time, serve as a basis for global research projects in energy and telecommunications, potentially leading to patents and expanded scientific collaboration.

We developed a kind of collective digital immunity for the Internet of Things. Instead of pulling all data into a single center, we train the devices themselves to distinguish normal behavior from anomalies directly at the edge. That allows us to block even the most sophisticated attacks

Fariza Tebueva

Doctor of Physical and Mathematical Sciences and Professor in the Department of Computational Mathematics and Cybernetics at North Caucasus Federal University

Meanwhile, the technology remains experimental. Commercial rollout and integration into production cycles will require additional rounds of testing and formal standardization before deployment at scale.

Growing Interest in IoT Security in Russia

IoT threats have been rising in recent years, driven by the rapid increase in connected devices, which expands the attack surface available to adversaries. That dynamic is pushing demand for protective measures, threat detection technologies and clearer security governance. Key risks include data confidentiality breaches, network and physical attacks, and software vulnerabilities. Common mitigation steps include network segmentation, data encryption, regular firmware updates, authentication and access control.

International research also points to a shift toward decentralized information security approaches that resemble the collective immunity model. The idea is to distribute data and oversight mechanisms across many nodes. Gartner research cited by the authors suggests that by 2026 more than 35% of enterprise solutions will incorporate decentralization elements for mission-critical systems. Fully decentralized systems, however, can be slower and harder to manage, which is driving interest in hybrid architectures.

Interest in IoT security is growing in Russia as well. Analysts say IoT has moved from concept to tangible products and services, with rising demand from both corporate buyers and individual users. Major growth areas include industrial digitalization, energy, real estate and utilities, with strong potential in agriculture and healthcare.

Contributing to Cybersecurity Standards

The NCFU team addresses a core challenge in modern cybersecurity – defending IoT networks against complex, coordinated attacks. The prototype follows global trends toward decentralized protection, using collective learning and rapid node-to-node exchange of data. The system can be viewed as a promising base for scaling and eventual commercial use, primarily because of its high threat identification accuracy.

In the near term, the project is expected to advance through large-scale testing and pilot programs in laboratory and production settings. Over the next two to four years, the authors forecast integration of the developed approaches into state information security standards and the start of active collaboration with industrial partners in energy and telecommunications. Longer term, the project could reach international markets, become part of export-oriented IoT protection offerings and contribute to global cybersecurity standards.