## 1 Introduction
### Existing IoT Security Solutions and Their Limitations  
As highlighted by one of the reviewers, there is a pressing need to examine the landscape of existing solutions for mitigating cyber threats in the IoT and evaluate their shortcomings. Therefore, before delving into the details of our proposed approach, we dedicate this section to providing a comprehensive overview of the current state of IoT security solutions and the challenges that they face. This discussion sets the stage for our research by emphasizing the critical need for advancements in IoT security and the unique contributions of our work.  
We explore common approaches adopted by the cybersecurity community to address IoT threats, including intrusion detection systems, encryption methods, access control mechanisms, and more. We critically analyze these solutions, shedding light on their strengths and weaknesses, and identifying areas where they may fall short in protecting IoT devices and networks.  
Our research is driven by the recognition that while existing solutions have made significant strides in enhancing IoT security, they are not without limitations. Vulnerabilities, scalability issues, and adaptability to evolving threats are among the challenges faced by these solutions. These identified shortcomings serve as a backdrop against which we propose our innovative IDS framework, which not only addresses these limitations but also brings novel capabilities to IoT security.  
In the subsequent sections, we present the details of our LSTM-based IDS model, the application of feature selection, and the integration of SMOTE to tackle data imbalance. We highlight how our approach overcomes the limitations discussed in this section, ultimately contributing to the enhancement of IoT security.  
The outcomes of this research are poised to offer substantial contributions to the field of IoT security. Our work leverages the following key elements:  
1. LSTM-Based Deep Learning: By incorporating long short-term memory (LSTM) networks into our intrusion detection system (IDS), we introduce a novel approach to IoT security. LSTM's ability to model sequential data enables our system to capture and recognize evolving cyber threats, thus enhancing the adaptability and responsiveness of our IDS.
2. SMOTE for Data Imbalance: Addressing the inherent data imbalance in IoT security datasets is a critical challenge. Through the synthetic minority over-sampling technique (SMOTE), our research takes a proactive stance, enabling our IDS to learn from underrepresented threat instances. This approach strengthens the overall robustness of our system.
3. Random Forest Classifier-RFE for Feature Selection: Feature selection plays a pivotal role in optimizing the performance of an IDS. Our use of the random forest with recursive feature elimination (RFE) ensures that our system operates with a streamlined and relevant set of features, reducing computational overhead while preserving detection accuracy.  
Collectively, our AI-driven IDS offers a proactive, adaptive, and efficient defense mechanism against cyber threats in the IoT landscape. This research lays the foundation for reinforcing the resilience and security of modern IoT networks. By fostering trust and confidence, it encourages the widespread adoption of IoT technologies across diverse industries, addressing critical security concerns in the ever-expanding IoT ecosystem.