Adapting NIST Cybersecurity Framework (CSF) 2.0 for Lightweight Security in Assistive Devices.
Description
Assistive devices, including smart glasses, wearable navigation aids, and voice-controlled assistants, enhance accessibility for visually impaired individuals but remain highly vulnerable to cybersecurity threats due to limited hardware capabilities. Risks such as unauthorized access, spyware, keylogging, and firmware exploits necessitate lightweight security solutions that balance protection with low-resource constraints. This research adapts NIST Cybersecurity Framework (CSF) 2.0 to assess and mitigate risks in assistive technologies. The study integrates ASCON, Elliptic Curve Cryptography (ECC), and ChaCha20 encryption to secure communication with minimal computational overhead. Additionally, voicebased multi-factor authentication (MFA), behavioral access control, and secure boot mechanisms are implemented to enhance device security. To validate the approach, cybersecurity datasets including IoT attack data, intrusion detection, and malware detection will be analyzed, with penetration testing and risk scoring models used for evaluation. By aligning NIST CSF 2.0 with lightweight security techniques, this research ensures assistive devices remain low-power, privacy-preserving, and cyber-resilient, supporting broader adoption in healthcare, mobility assistance, and smart environments