Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions (2106.09527v1)

Abstract: The rapid development of the Internet and smart devices trigger surge in network traffic making its infrastructure more complex and heterogeneous. The predominated usage of mobile phones, wearable devices and autonomous vehicles are examples of distributed networks which generate huge amount of data each and every day. The computational power of these devices have also seen steady progression which has created the need to transmit information, store data locally and drive network computations towards edge devices. Intrusion detection systems play a significant role in ensuring security and privacy of such devices. Machine Learning and Deep Learning with Intrusion Detection Systems have gained great momentum due to their achievement of high classification accuracy. However the privacy and security aspects potentially gets jeopardised due to the need of storing and communicating data to centralized server. On the contrary, federated learning (FL) fits in appropriately as a privacy-preserving decentralized learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system. In order to establish the need for FL, various types of IDS, relevant ML approaches and its associated issues are discussed. The paper presents detailed overview of the implementation of FL in various aspects of anomaly detection. The allied challenges of FL implementations are also identified which provides idea on the scope of future direction of research. The paper finally presents the plausible solutions associated with the identified challenges in FL based intrusion detection system implementation acting as a baseline for prospective research.

• The paper highlights federated learning as a decentralized IDS solution that preserves data privacy by training on local devices instead of a central server.
• It demonstrates how diverse FL architectures enhance anomaly detection in non-IID, heterogeneous network environments, reducing false alarm rates.
• The study identifies challenges such as communication overhead and poisoning attacks, proposing blockchain integration and lightweight DL models as potential solutions.

An Overview of Federated Learning for Intrusion Detection Systems

The paper "Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions" offers a comprehensive examination of applying federated learning (FL) to intrusion detection systems (IDS). With the proliferation of smart devices and the expansion of the Internet, network infrastructures are becoming increasingly complex and heterogeneous. This complexity introduces vulnerabilities that necessitate robust security mechanisms, such as IDS, which are pivotal in protecting network integrity, confidentiality, and availability.

Conventional intrusion detection methodologies often leverage ML and deep learning (DL) techniques. However, these approaches generally rely on central servers to aggregate data from various clients, posing privacy risks due to centralized data storage and transmission. Federated learning presents a promising alternative that mitigates privacy concerns by enabling decentralized model training on local data while sharing only model parameters with the central server.

The Role of Federated Learning

Federated learning is a decentralized AI training framework that facilitates collaborative model learning across multiple devices without sharing the underlying data. This approach not only safeguards user privacy but also optimizes communication and computation costs. The paper delineates several IDS deployment architectures—centralized, distributed, and decentralized—with a particular emphasis on the FL architecture for enhancing IDS efficiency. FL's decentralized nature is particularly advantageous for handling the diverse and large-scale datasets inherent to intrusion detection tasks.

The application of FL is explored across various facets of anomaly detection, particularly within heterogeneous environments. The paper discusses FL's capacity to improve anomaly detection precision by utilizing a broader range of data from different clients, addressing data scarceness and the non-IID nature of intrusion datasets. Moreover, the paper evaluates FL's effectiveness in DDoS attack detection and emphasizes FL's ability to uphold the privacy of sensitive data handled by IDS.

Challenges and Future Directions

Despite its potential, FL application to IDS is accompanied by several challenges, including communication overhead, model poisoning attacks, the false alarm rate due to non-IID data, and resource constraints in low-power IoT devices. The paper reviews existing literature on these challenges and suggests future research directions to mitigate them.

1. Communication Overhead: The communication cost associated with transmitting model parameters can be substantial, especially in large-scale networks. Asynchronous federated learning and advanced communication protocols such as 5G are proposed to alleviate these challenges.
2. Poisoning and Security Concerns: FL systems are vulnerable to poisoning attacks, where malicious clients can manipulate training data. The paper discusses blockchain integration and digital twin technologies as potential solutions to enhance FL security.
3. Handling Non-IID Data: The variability in data across clients can lead to inefficient training and high false alarm rates. Hierarchical clustering and reinforcement learning protocols can aid in managing non-IID data effects.
4. Resource Management in IoT: Efficient resource allocation and utilization strategies are critical for enabling FL in resource-constrained devices. Lightweight DL models and optimization algorithms are proposed to enhance computational feasibility.

Implications and Conclusions

The research outlines practical and theoretical implications of adopting FL in IDS. By ensuring privacy, scalability, and decentralized decision-making, FL offers a viable solution to the evolving challenges of securing complex network environments. Prospective research should focus on refining FL methodologies to address inherent challenges and leverage cutting-edge technologies for optimal deployment in diverse and dynamic settings.

The paper serves as a pivotal reference for academics and industry practitioners alike, paving the way for further innovation in secure and privacy-preserving intrusion detection solutions. The seamless integration of FL with IDS marks significant advancement in addressing cybersecurity challenges posed by modern networking environments.