Abstract:
The rapid expansion of the Internet of Things (IoT) has been met with a concurrent rise in cybersecurity threats, particularly
distributed denial of service (DDoS) attacks. These attacks pose severe risks to the interconnected networks that form the backbone of
various critical infrastructures. Traditional defense mechanisms, primarily tailored for more conventional network settings, fall short
in effectively tackling the unique complexities and dynamic nature of IoT environments. Their limitations are evident in the face of the
diverse range of IoT devices, the enormity of data generated, and the continually evolving strategies of cyber attackers. To address
these challenges, this paper introduces "IoT-Defender," an innovative solution that utilizes Convolutional Neural Networks (CNNs)
for the detection of DDoS attacks in IoT networks. This system marks a significant advancement in IoT cybersecurity, leveraging deep
learning to analyze and interpret the intricate patterns of network traffic. IoT-Defender is built on a robust CNN architecture, comprising
multiple convolutional and pooling layers that work synergistically to extract and process complex features from network traffic data.
This architecture enables the system to discern between normal operations and malicious activities with high accuracy. Utilizing the
comprehensive CICDDoS2019 dataset for training and validation, IoT-Defender demonstrates remarkable efficacy, achieving a
detection accuracy of 99.68% and outperforming traditional security models. This high accuracy underscores the system's capability
to adapt to the varied and evolving landscape of IoT networks, making it a scalable and adaptable defense mechanism. IoT-Defender
thus emerges as a critical tool in enhancing the resilience and security of IoT infrastructures against the threats of DDoS attacks.
