## 2 Literature Review  
The increasing prevalence of internet of things (IoT) networks in various domains has facilitated new opportunities for innovative services and applications. However, this rapid growth in IoT adoption has also exposed critical cybersecurity challenges, necessitating effective intrusion detection mechanisms. In this section, we present a comprehensive review of existing literature on AI-based intrusion detection systems (IDSs) tailored for IoT environments. Specifically, we focus on the utilization of long short-term memory (LSTM) models, the synthetic minority over-sampling technique (SMOTE) for addressing data imbalance, and random forest-based feature selection.  
We sincerely appreciate the reviewer's valuable comment regarding the significance and advancement of our proposed methodology compared to the published literature. We recognize the importance of effectively conveying the unique contributions of our research. To address this concern, we would like to highlight the following points:  
Significance of the Proposed Methodology:  
Our methodology holds significant importance in the realm of IoT intrusion detection for several key reasons:Imbalanced Data Mitigation: We acknowledge the challenges posed by imbalanced datasets in intrusion detection, a problem widely recognized in the literature. Our incorporation of the synthetic minority over-sampling technique (SMOTE) addresses this issue effectively by rebalancing the dataset, resulting in enhanced detection capabilities.  
Elimination of Extensive Feature Engineering: Traditional intrusion detection approaches often require extensive manual feature engineering. Our method streamlines this process by employing the random forest classifier with recursive feature elimination (RFE), ensuring that only the most relevant attributes contribute to intrusion detection. This not only improves accuracy but also reduces the computational overhead associated with feature selection.  
Advancements Over Published Literature:  
Our proposed methodology advances the state of the art in IoT intrusion detection through the following:  
Adaptive Learning: Our approach incorporates an attention mechanism and the bidirectional long short-term memory (Bi-LSTM) network, allowing the model to adaptively learn sequential patterns in data traffic. This stands in contrast to some existing methods that rely on fixed or handcrafted features.  
End-to-End Model: DLNID is an end-to-end model, eliminating the need for manual feature extraction. This innovation streamlines the intrusion detection process and enhances model efficiency.  
Quantitative Results: Our research presents empirical evidence through a comprehensive evaluation using widely adopted public datasets, including CICIDS2017, NSL-KDD, and UNSW-NB15. The superior accuracy achieved by our LSTM-based IDS, coupled with SMOTE and RFE, demonstrates its advancements over existing methods.  
Real-World Relevance:  
Our methodology holds practical significance by addressing the evolving landscape of cybersecurity threats in IoT networks. It offers a proactive and adaptive defense mechanism that is particularly relevant in today's context, where network attacks are increasing in number and sophistication.