The overall vision of ENViSEC project is to enhance Smart Environments cybersecurity by introducing intelligent multi-agent data handling, cyber threats sharing, situational awareness and data streams aggregation from Edge devices. Our ambition is to offer a resilient response to cyber-attacks as well as to ensure human-oriented warning and early detection of adversarial actions. Our new method enables multi-level data collection and off-chip Machine Learning model training to reduce the overhead and latency of the Internet of Things (IoT) components. It will contribute towards hardening cybersecurity in a cross-sector context and building an efficient infrastructure in a resource-constrained environment.

Technology

The main goal of this project is to develop a solution to tackle the following cybersecurity-related challenges in Smart Environments which are also intrinsically found in IoT infrastructure: (i) hard to implement end-point protection (Antivirus, IDS, IPS, etc) in IoT resource-constrained environment, especially energy-efficient, (ii) impossible to have synchronous real-time communication, (iii) limited and far from universal design of ML applicability to the devices diversity. This webpage will include following work-in-progress results

• Open source code
• Cross-platform middleware
• Documentation and tutorials
• Performance testbeds an evaluation

Application Areas

Cybersecurity becomes one of the important factors in distributed networks of Smart Environment. Conventional signature-based intrusion and malware detection models are too cumbersome and too resource-/data-consuming for on-site model retraining. Therefore, intelligent protection against attacks in the IoT ecosystem should be created and suited also for use in insecure environments out in remote areas. We envision the creation of uniform benchmark data and testbeds that can be used for cyber attacks analysis on multiple levels.

Operational Stage

Understanding and awareness of threats and malware attacks against intelligent IoT ecosystem.

Tactic Stage

Facilitation of testbed and bench-marking setup implementation to prepare IoT ecosystem for ML-based detecting network attacks.

Strategic Stage

A new intelligent solution that uses similarity-based cyber attacks detection resulting in a small overhead, improved efficiency and faster processing speed.

This‌ ‌project‌ ‌has‌ ‌received‌ ‌funding‌ ‌from‌ ‌the‌ ‌European‌ ‌Union’s‌ ‌Horizon‌ ‌2020‌‌ research‌ ‌and‌ ‌innovation‌ ‌programme‌ ‌within‌ ‌the‌ ‌framework‌ ‌of‌ ‌the‌ ‌NGI-POINTER‌‌ Project‌‌ funded‌‌ under‌‌ grant‌‌ agreement‌‌ No‌ 871528‌. It is executed at the Department of Technology, Kristiania University College.

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