A-THENA improves averaged IoT intrusion detection accuracy by 3.69-6.88 percentage points over baselines on three datasets using time-aware hybrid encoding and network-specific augmentation, with near-zero false alarms and real-time deployment on Raspberry Pi Zero 2 W.
Giannoutakis, Anastasios Drosou, and Dimitrios Tzovaras
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
A-THENA improves averaged IoT intrusion detection accuracy by 3.69-6.88 percentage points over baselines on three datasets using time-aware hybrid encoding and network-specific augmentation, with near-zero false alarms and real-time deployment on Raspberry Pi Zero 2 W.