Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices (2306.14263v2)

Abstract: The field of NLP is currently undergoing a revolutionary transformation driven by the power of pre-trained LLMs based on groundbreaking Transformer architectures. As the frequency and diversity of cybersecurity attacks continue to rise, the importance of incident detection has significantly increased. IoT devices are expanding rapidly, resulting in a growing need for efficient techniques to autonomously identify network-based attacks in IoT networks with both high precision and minimal computational requirements. This paper presents SecurityBERT, a novel architecture that leverages the Bidirectional Encoder Representations from Transformers (BERT) model for cyber threat detection in IoT networks. During the training of SecurityBERT, we incorporated a novel privacy-preserving encoding technique called Privacy-Preserving Fixed-Length Encoding (PPFLE). We effectively represented network traffic data in a structured format by combining PPFLE with the Byte-level Byte-Pair Encoder (BBPE) Tokenizer. Our research demonstrates that SecurityBERT outperforms traditional Machine Learning (ML) and Deep Learning (DL) methods, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), in cyber threat detection. Employing the Edge-IIoTset cybersecurity dataset, our experimental analysis shows that SecurityBERT achieved an impressive 98.2% overall accuracy in identifying fourteen distinct attack types, surpassing previous records set by hybrid solutions such as GAN-Transformer-based architectures and CNN-LSTM models. With an inference time of less than 0.15 seconds on an average CPU and a compact model size of just 16.7MB, SecurityBERT is ideally suited for real-life traffic analysis and a suitable choice for deployment on resource-constrained IoT devices.

Revolutionizing Cyber Threat Detection with LLMs

Introduction

The perpetual evolution of cyber threats necessitates innovative approaches in threat detection and incident response systems. The introduction of pre-trained LLMs, including the implementation of the BERT architecture, has marked a significant step forward in the field of cybersecurity. This paper delineates the development and evaluation of SecurityLLM, a pre-trained LLM specifically devised for cyber threat detection and incident response.

The SecurityLLM Model

SecurityLLM amalgamates two pivotal generative components: SecurityBERT, designed for cyber threat detection, and FalconLLM, aimed at incident response and recovery. This combination promises to leverage the synergy between detection and response mechanisms to enhance the overall security posture.

SecurityBERT Model

The cornerstone of the SecurityLLM model, SecurityBERT, leverages the transformer architecture for the detection of cyber threats. By processing cybersecurity-related textual data, SecurityBERT is able to identify a broad spectrum of attacks with remarkable efficiency. Notably, the introduction of the Fixed-Length Language Encoding (FLLE) technique and the Byte-level Byte-Pair Encoder (BBPE) Tokenizer significantly enhances the model’s ability to handle structured network data, fostering a notable improvement in performance.

FalconLLM Model

Building upon the detection capabilities of SecurityBERT, FalconLLM serves as the model’s complementary incident response and recovery system. Trained on a massive corpus of data and boasting 40 billion parameters, FalconLLM’s adeptness in analyzing, interpreting, and suggesting mitigation strategies against identified threats is unparalleled. It extends SecurityLLM’s functionality beyond mere detection, offering actionable insights for threat resolution.

Experimental Evaluation

The experimental evaluation of SecurityLLM utilized an extensive IoT cybersecurity dataset, facilitating the model’s exposure to real-world attack scenarios. Through rigorous testing, SecurityLLM achieved an overall accuracy of 98% in detecting fourteen distinct types of attacks. Furthermore, detailed comparisons with traditional ML methods and deep learning models, such as CNNs and RNNs, underscored SecurityLLM’s superiority in performance, affirming the transformative potential of LLMs in cybersecurity.

Future Directions

The promising results of SecurityLLM signal a fertile ground for further exploration and advancement in the application of LLMs within cybersecurity. Future studies could explore expanding the model’s capabilities to encompass a wider array of attack types and more complex threat scenarios. Additionally, continuous model refinements, updated with the latest threat intelligence data, would ensure its sustained effectiveness.

Conclusion

SecurityLLM represents a novel intersection of LLMs and cybersecurity, offering an effective solution for cyber threat detection and incident response. By harnessing the power of SecurityBERT and FalconLLM, this model sets a new benchmark in the cybersecurity domain, promising enhanced security measures against the ever-evolving landscape of cyber threats. As we look ahead, the potential for further ingenuity and improvements in this space remains vast, signaling a promising trajectory for the utilization of LLMs in safeguarding digital assets.