LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection (2002.04902v2)

Abstract: Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing state-of-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.

• The paper introduces LUCID, a CNN-based model achieving near state-of-art accuracy and a 40-fold processing speedup for real-time DDoS detection.
• It employs a dataset-agnostic preprocessing technique and activation analysis to interpret key network traffic features affecting classification.
• The approach is optimized for resource-constrained environments, enabling effective deployment on IoT edge devices.

Overview of "LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection"

Introduction

Distributed Denial of Service (DDoS) attacks remain a potent threat in contemporary Internet infrastructure, severely affecting the availability of critical services. The paper "LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection" presents a novel approach designed to efficiently detect DDoS attacks using a lightweight deep learning model, Lucid. The system leverages Convolutional Neural Networks (CNNs) to classify network traffic flows as malicious or benign, emphasizing low processing overhead suitable for deployment in resource-constrained environments like the Internet of Things (IoT) edge devices.

Contributions and Methodology

The authors highlight several key contributions of their work:

1. CNN-Based Architecture: The Lucid model introduces an innovative use of CNNs for DDoS traffic detection, designed to operate with a minimal processing footprint.
2. Data Preprocessing: A dataset-agnostic preprocessing mechanism enables real-time traffic observation suitable for online detection, taking into account the structure of network flows split across time windows.
3. Explainability through Activation Analysis: Lucid incorporates an activation analysis to interpret which features of the network traffic are pivotal in classifying DDoS attacks.
4. Empirical Validation: Rigorous empirical evaluation establishes the model's performance on constrained hardware platforms, demonstrating its practical viability for edge computing scenarios.

Evaluation and Results

Lucid was tested across several contemporary datasets, including ISCX2012, CIC2017, and CSECIC2018, with commendable performance metrics. The system matches state-of-the-art accuracy levels, with particular effectiveness demonstrated by a 40-fold reduction in processing time relative to other deep learning approaches, such as the 3LSTM model from the DeepDefense framework. With an F1 score consistently near 0.99 across test datasets, Lucid shows promise for real-time application, particularly in environments where computational resources are limited.

Implications and Future Directions

The implications of this research are substantial for both theoretical and practical applications. Theoretically, it advances the knowledge on utilizing CNNs for network intrusion detection, particularly emphasizing feature learning over exhaustive feature engineering. Practically, it offers a scalable solution to safeguard IoT networks and other edge environments vulnerable to DDoS attacks. Future research might explore adaptive mechanisms within Lucid to enhance resilience against evolving threat patterns, potentially incorporating reinforcement learning for autonomous threat responses. Additionally, investigating robustness against adversarial attacks in network traffic represents a promising avenue for further study.

Conclusion

The paper "LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection" not only provides a high-performance DDoS detection mechanism but also demonstrates the applicability of deep learning models in resource-constrained environments. Its contributions lay a foundation for subsequent developments in efficient, scalable security infrastructures, leveraging deep learning's capabilities to protect next-generation network deployments.