| Title |
Deep Learning Based IoT Re-authentication for Botnet Detection and Prevention |
| ID_Doc |
41266 |
| Authors |
Salim, MM; Park, JH |
| Title |
Deep Learning Based IoT Re-authentication for Botnet Detection and Prevention |
| Year |
2020 |
| Published |
|
| DOI |
10.1007/978-981-32-9244-4_33 |
| Abstract |
IoT devices face a grave security threat from botnet attacks. These devices are known for their poor default authentication system due to being set on weak factory set passwords. Critical systems such as Healthcare and transportation can be jeopardized if hijacked. Using a bot, an attacker can use it to relinquish control from Smart city network administrators and users. In this paper, we present a Software-defined Deep learning based IoT Defense (SDID) mechanism which monitors and compares device historical traffic flow with current patterns to determine if a device is under an attack. Furthermore, to prevent false detection under flash-crowd events, the mechanism compares data with adjacent nodes to determine if the traffic flow is anomalous or not. |
| Author Keywords |
Botnet; Cybersecurity; Internet of Things; Smart city; Deep Learning; Software defined networking |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000611815900033 |
| WoS Category |
Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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