| Abstract | Due to various limitations, such as limited power supply, the
lack of storage capability and processing power, Internet of
Things-based smart home networks have become vulnerable to
various cyber-security attacks including Distributed Denial of Service (DDoS) attacks. These attacks are a malicious
attempt to exhaust and overwhelm the target system
resources, which has significant impact on the operation of
smart home net- works. This paper proposes a novel, efficient
and lightweight DDoS attack detection scheme in smart home
networks, which employs artificial neural networks (ANN) to
classify smart home networks traffic into DDoS attacks or
normal traffic. The proposed solution is evaluated on four
datasets, namely, IoT-23, DS2OS, NUSW-NB15GT and CICDDOS2019. Experiments were conducted on two types of
ANN models, i.e., Multilayered Perceptron (MLP) and LongShort-Term Memory (LSTM), which achieved 99.78% and
99.98% accuracy, respectively. |
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