A comprehensive survey of cybersecurity techniques based on quality of service (QoS) on the Internet of Things (IoT)

Abstract The exponential growth of the Internet of Things (IoT) has driven considerable advancements in many domains, such as healthcare, smart cities, and industrial automation. However, this connectivity introduces a broad attack surface, making IoT ecosystems vulnerable to sophisticated cyberattacks, including Distributed Denial of Service (DDoS), Man-In-The-Middle (MITM) attacks, and data breaches, which may severely degrade the Quality of Service (QoS) metrics, that may include latency, throughput, and packet loss. This paper provides a detailed survey of contemporary attack detection techniques, such as signature-based, anomaly-based and hybrid methods, focusing on their efficiency in securing IoT systems without undermining QoS. The impact of these methods on critical QoS parameters is analyzed in resource-constrained IoT environments, where computational and memory limitations deter the implementation of traditional security measures. Additionally, emerging approaches such as artificial intelligence (AI)-driven anomaly detection, federated learning, and blockchain are examined for their potential to balance robust security with optimal performance. The analysis reveals the trade-offs between detection accuracy, resource consumption, and QoS maintenance, identifying research gaps related to post-attack recovery mechanisms and real-time adaptive security models. A cross-domain comparison of attack detection techniques reveals how different IoT applications, from smart healthcare to industrial IoT, impact the performance of these solutions. The results provide direction for future research on developing scalable, resource-efficient security models that can maintain high QoS levels in complex IoT ecosystems.