qhjqhj00 ton-iot-tpu-eval
Exploring Edge TPU for Network Intrusion Detection in IoT — Hosseininoorbin et al. (2021) (arXiv:2103.16295, 2021)
What this evaluates
Evaluates deep learning-based network intrusion detection on IoT traffic, comparing hardware accelerators (Edge TPU vs ARM CPU) for classification accuracy, inference speed, and energy efficiency.
Datasets
- ToN-IoT — total 22339021; splits: train (-1), val (-1), test (-1)
Metrics
classification accuracy(primary) — range: [0, 1]- Standard top-1 accuracy: fraction of correctly classified traffic flows out of total samples. Exact formula not specified in provided text.
inference speed— range: other- Latency per inference or throughput (samples/sec). Measured on Edge TPU and ARM Cortex-A53.
energy consumption— range: other- Total energy used per inference or per dataset run, measured in Joules.
Input / output format
Input: 44 numerical/categorical features extracted from IoT network traffic flows (flow identifiers like IPs/ports removed).
Output: Multi-class label indicating benign traffic or one of nine attack types (backdoor, DoS, DDoS, injection, MitM, password, ransomware, scanning, XSS).
Scoring recipe
def compute_accuracy(predictions, labels):
correct = sum(1 for p, l in zip(predictions, labels) if p == l)
return correct / len(labels)
# Latency and energy measured via hardware profiling tools per inference.
Common pitfalls
- Dataset is highly imbalanced (96.44% attack vs 3.56% benign), requiring stratified splits or weighted loss.
- Flow identifiers are explicitly removed to prevent bias, so models cannot rely on IP/port patterns.
- Edge TPU performance heavily depends on model size due to on-chip memory limits; small models may run slower on TPU than CPU.
Evidence (verbatim from paper)
A heterogeneous IoT dataset has been utilised to evaluate the proposed TPU-based NIDS. The ToN-IoT dataset... It consists of nine attack scenarios including backdoor, DoS, Distributed DoS (DDoS), injection, Man In The Middle, password, ransomware, scanning and Cross-Site Scripting. The dataset is made up of mainly attack samples; 21,542,641 (96.44%) and a low amount of benign samples; 796,380 (3.56%).
Citation
@misc{hosseininoorbin2021edge,
title={Exploring Edge TPU for Network Intrusion Detection in IoT},
author={Hosseininoorbin et al. (2021)},
year={2021},
note={arXiv:2103.16295}
}
- arXiv: 2103.16295