HyperIDS: a hypergraph learning and quantum-inspired transformer ensemble framework for IoT intrusion detection
Samayank Goel, Logeswari Govindaraj, Tamilarasi Kathirvel Murugan · 2026
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Description
Samayank Goel, Logeswari Govindaraj, and Tamilarasi Kathirvel Murugan build an intrusion detector in a 2026 paper. Swarms of smart devices have widened the surface hackers can attack. Standard detectors miss faint links between traffic traits and falter as settings change. Their system, HyperIDS, joins three parts: hypergraph learning, a quantum inspired feature picker, and a transformer based voting classifier.
A swarm search with a dual fitness score first picks the most telling traffic traits. Hypergraphs then map links among many traits at once, beyond simple pairs. The classifier ensemble reads those maps to flag attacks. The design aims for steady detection across mixed device networks, where attack shapes shift fast and faint signals matter.
Catalog record
| CREATOR | Samayank Goel, Logeswari Govindaraj, Tamilarasi Kathirvel Murugan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 105 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |