An Intrusion Detection System Using BoT-IoT
Source abstract
Abstract
The rapid growth of the Internet of Things (IoT) has led to an increased automation and interconnectivity of devices without requiring user intervention, thereby enhancing the quality of our lives. However, the security of IoT devices is a significant concern as they are vulnerable to cyber-attacks, which can cause severe damage if not detected and resolved in time. To address this challenge, this study proposes a novel approach using a combination of deep learning and three-level algorithms to detect attacks in IoT networks quickly and accurately. The Bot-IoT dataset is used to evaluate the proposed approach, and the results show significant improvements in detection performance compared to existing methods. The proposed approach can also be extended to enhance the security of other IoT applications, making it a promising contribution to the field of IoT security.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
References (31)
Show source-provided references
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