Neural Network-Based Distributed Denial of Service (DDoS) Attack Detection in Smart Home Networks

Ismeil Ahamed, Farhan Ahmad, Vasile Palade, Abdullahi Ahmed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 Long-Short-Term Memory (LSTM), which achieved 99.78% and 99.98% accuracy, respectively.

Original languageEnglish
Title of host publicationProceedings Volume of the 6th IET International Smart Cities Symposium
Number of pages6
ISBN (Electronic)9781839538544
Publication statusPublished - 29 May 2023
Externally publishedYes
Event6th Smart Cities Symposium - Virtual, Online, Bahrain
Duration: 6 Dec 20228 Dec 2022
Conference number: 6


Conference6th Smart Cities Symposium
Abbreviated titleSCS 2022
CityVirtual, Online

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