Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Abstract

This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.

Keywords

• cybersecurity
• intrusion detection system (IDS)
• long short-term memory (LSTM)
• data imbalance