Repository for IEEE CCNC'21 paper titled "Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network"
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@INPROCEEDINGS{Sing2101:Edge,
AUTHOR="Praneet Singh and Jishnu {Jaykumar P.} and Akhil Pankaj and Reshmi Mitra",
TITLE="{Edge-Detect:} Edge-centric Network Intrusion Detection using Deep Neural
Network",
BOOKTITLE="2021 IEEE 18th Annual Consumer Communications \& Networking Conference
(CCNC) (CCNC 2021)",
ADDRESS=virtual,
DAYS=9,
MONTH=jan,
YEAR=2021,
KEYWORDS="Edge computing; Deep Learning; DDoS; Recurrent Neural Networks;
Cyber-attack",
ABSTRACT="Edge nodes are crucial for detection against multitudes of cyber attacks on
Internet-of-Things endpoints and is
set to become part of a multi-billion industry. However, with
resource constraints in this novel network infrastructure tier,
it is difficult to deploy existing Network Intrusion Detection
System with Deep Learning models (DLM). We address this
issue by developing a novel light, fast and accurate 'Edge-Detect'
model, which detects Distributed Denial of Service attack on
edge nodes using DLM techniques. Our model can work within
resource restrictions i.e. low power, memory and processing
capabilities, to produce accurate results at a meaningful pace. It
is built by creating layers of Long Short-Term Memory or Gated
Recurrent Unit based cells, which are known for their excellent
representation of sequential data. We designed a practical data
science pipeline with Recurring Neural Network to learn from
the network packet behavior in order to identify whether it
is normal or attack-oriented. The model evaluation is from
deployment on actual edge node represented by Raspberry Pi
using current cybersecurity dataset (UNSW2015). Our results
demonstrate that in comparison to conventional DLM techniques,
our model maintains a high testing accuracy of ∼99\% even
with lower resource utilization in terms of cpu and memory. In
addition, it is nearly 3 times smaller in size than the state-of-art
model and yet requires a much lower testing time."
}
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