| Title |
Network Flow based IoT Botnet Attack Detection using Deep Learning |
| ID_Doc |
44260 |
| Authors |
Sriram, S; Vinayakumar, R; Alazab, M; Soman, KP |
| Title |
Network Flow based IoT Botnet Attack Detection using Deep Learning |
| Year |
2020 |
| Published |
|
| DOI |
|
| Abstract |
Governments around the globe are promoting smart city applications to enhance the quality of daily-life activities in urban areas. Smart cities include internet-enabled devices that are used by applications like health care, power grid, water treatment, traffic control, etc to enhance its effectiveness. The expansion in the quantity of Internet-of-things (IoT) based botnet attacks is due to the growing trend of Internet-enabled devices. To provide advanced cyber security solutions to IoT devices and smart city applications, this paper proposes a deep learning (DL) based botnet detection system that works on network traffic flows. The botnet detection framework collects the network traffic flows, converts them into connection records and uses a DL model to detect attacks emanating from the compromised IoT devices. To determine an optimal DL model, many experiments are conducted on well-known and recently released benchmark data sets. Further, the datasets are visualized to understand its characteristics. The proposed DL model outperformed the conventional machine learning (ML) models. |
| Author Keywords |
Cyber Security; Botnet; Smart Cities; Internet of Things; Big Data; Machine Learning; Deep Learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000593830400032 |
| WoS Category |
Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Engineering; Telecommunications |
| PDF |
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