Domain-Agnostic Representation of Side-Channels

Area of Science:

• Computer Science
• Information Security
• Signal Processing

Background:

• Side channels are unintended pathways leaking target system information.
• Side-channel sensing (SCS) exploits these channels for information extraction.
• SCS is established in cybersecurity (CYB) and emerging in medical diagnostics and monitoring (MDM) and human-computer interaction (HCI).

Purpose of the Study:

• To analyze and unify existing literature on SCS across diverse domains (CYB, MDM, HCI, Misc).
• To propose a domain-agnostic SCS framework for systematic study and exploitation of side channels.
• To explore future methodologies for cross-domain transferability and new side channel discovery.

Main Methods:

• Literature analysis of SCS examples across cybersecurity, medical diagnostics, human-computer interaction, and miscellaneous domains.
• Identification of underlying advanced sensing and signal processing techniques.
• Development of a domain-agnostic SCS framework.

Main Results:

• Established SCS applications in cybersecurity, medical diagnostics, and human-computer interaction share common foundations in advanced sensing and signal processing.
• A proposed domain-agnostic SCS framework facilitates a systematic approach to studying, detecting, and exploiting side channels.
• Modeling SCS as domain-independent data structures enables computational approaches and technique transferability.

Conclusions:

• SCS is a unifying concept across diverse fields, underpinned by advanced sensing and signal processing.
• The proposed SCS framework enables formalized, systematic analysis and exploitation of side channels.
• Future research can leverage data structure modeling for cross-domain SCS applications, leakage detection, and novel side channel discovery.