# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
###### Abstract  
Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users' privacy, and maintaining critical systems' reliability and safety. As the IoT landscape evolves, effective intrusion detection mechanisms become increasingly essential to mitigate the ever-growing spectrum of cyber threats. Practical security approaches, notably deep learning-based intrusion detection, have been introduced to tackle these issues. This study utilizes deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), while introducing an ensemble deep learning architectural framework that integrates a voting policy within the model's structure, thereby facilitating the computation and learning of hierarchical patterns. In our analysis, we compared the performance of ensemble deep learning classifiers with traditional deep learning techniques. The standout models were CNN-LSTM and CNN-GRU, achieving impressive accuracies of 99.7% and 99.6%, along with exceptional F1-scores of 0.998 and 0.997, respectively. intrusion detection system; Internet of Things; cybersecurity; cyber-physical systems; cyberattacks; cybercrime; intrusion detection +
Footnote †: journal: Applied sciences  
Furthermore, the IoT ecosystem's heterogeneous nature, characterized by various manufacturers, protocols, and software stacks, complicates establishing a unified security approach. Many IoT devices, designed primarily for functionality and cost-effectiveness, often lack regular software updates, exposing them to known security threats for prolonged periods [6; 7]. Data privacy is another pressing concern. As these devices continually collect vast amounts of data, sensitive information can be accessed or misused, threatening individual privacy and corporate confidentiality [8]. Addressing these security challenges is paramount to ensure that the IoT realizes its full potential without compromising user trust and safety [9]. As the adoption of IoT continues to surge, there is an increasing emphasis on developing sophisticated security measures, including advanced intrusion detection systems and adaptive threat response mechanisms. Only by prioritizing and innovating in the realm of security can the promise of a truly connected, innovative, and safe world be achieved [10; 11]. Moreover, the physical nature of IoT devices adds another layer of vulnerability. Unlike purely digital platforms, these devices can be physically tampered with, leading to breaches not just in data but also in their operational integrity. Consider smart infrastructure in cities or hospital health devices; tampering could have real-world, life-threatening consequences [12]. This interconnectedness, while being the strength of the IoT, also becomes its Achilles' heel. A breach in one device can potentially cascade, affecting a network of interconnected systems, emphasizing the need for holistic security frameworks. Collaboration across industries, manufacturers, and regulatory bodies is essential to develop and enforce standards that ensure the resilience and safety of the IoT ecosystem.