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Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions

Shaashwat Agrawal, Sagnik Sarkar, Ons Aouedi, Gokul Yenduri, Kandaraj Piamrat, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu

arXiv:2106.09527v1cs.CRcs.LG

TL;DR

Centralized intrusion detection can jeopardize privacy and security when network data from heterogeneous devices must be stored or communicated centrally. This paper reviews federated learning for intrusion detection, surveying implementations, challenges, and plausible solutions, including reported anomaly-detection performance such as 95.6% accuracy in 257 ms for D¨IoT.

  • Problem

    Centralized storage and communication of intrusion-detection data can jeopardize privacy and security, while traditional signature-based detection cannot identify new attacks.

  • Method

    The paper presents an extensive review of federated learning for intrusion detection, covering IDS types, machine-learning approaches, anomaly-detection implementations, challenges, and plausible solutions.

  • Results

    95.6% accuracy in 257 ms was reported for D¨IOT anomaly detection, while federated multi-task learning performed multiple anomaly-detection tasks using one model and outperformed several single-task methods.

  • Takeaways & Limitations

    Federated learning offers a privacy-preserving approach for applying machine learning and deep learning to intrusion detection across distributed and heterogeneous networks.

  • Takeaways & Limitations

    Federated intrusion detection remains vulnerable to poisoning attacks, non-IID data that can increase false alarms, resource constraints on low-power devices, and sensitive information leakage from model weights.

Abstract

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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.

I. INTRODUCTION

Growing network complexity and evolving attacks expose limits in conventional intrusion detection, while centralized machine learning can threaten data privacy. The paper motivates federated learning as a decentralized approach for reviewing and improving machine-learning-based intrusion detection.

  • Traditional signature-based IDS methods recognize known attacks but cannot identify new ones.
  • Federated learning trains models collaboratively across devices without sharing their data with a centralized server.Its process alternates local learning with model transmission and global aggregation.
  • FL is categorized as horizontal, vertical, or transfer learning according to how client data are distributed.HFL uses shared features with different observations, VFL uses shared observations with different features, and TFL differs in both.
  • FL can reduce central-server computation, preserve privacy, improve bandwidth utilization, and handle diverse communications for IDS.
  • Few studies exclusively survey FL for IDS, motivating this paper’s comprehensive review of the field.Earlier surveys largely address FL and IDS separately or focus on narrower IDS techniques.

B. Contributions

This work surveys federated learning for intrusion detection, covering its role, applications, technologies, challenges, and future directions.

  • The paper surveys FL for IDS in terms of achievements and challenges.
  • It discusses FL’s role in intrusion detection and its use with ML/DL approaches.
  • The survey reviews ML, DL, and FL approaches and technologies applied to intrusion detection.
  • It highlights open research challenges and outlines possible future research directions for efficient future solutions.

II. BACKGROUND STUDY

The background introduces IDS as a security system for detecting unauthorized behavior and distinguishes host-based from network-based monitoring.

  • IDS supports resilient information exchange by investigating attacks, identifying malicious patterns, and helping administrators implement effective controls.
  • An IDS tracks networks for unauthorized behavior or policy breaches, logs intrusions, issues warnings, and supports mitigation or corrective measures.
  • HIDS monitors internal system activity, while NIDS examines incoming and outgoing traffic between network nodes.
  • HIDS records system events and device artifacts, but resource consumption can harm host performance and some attacks may remain undiscovered.

2) Network Intrusion Detection System (NIDS):

NIDS scrutinizes network traffic for anomalies, while signature- and anomaly-based detection provide different capabilities and limitations.

  • Network Intrusion Detection System (NIDS): NIDS monitors traffic passing through a subnet and identifies intrusions by comparing it with an anomaly library.
  • Network Intrusion Detection System (NIDS): NIDS can fail to detect attacks when heavy traffic makes scrutinizing every packet difficult.
  • Detection approaches: Signature-based detection matches new data against patterns from past attacks, so previously unknown attacks cannot be identified by this approach.
  • Detection approaches: Anomaly-based detection models normal behavior and treats differences between actual and predicted behavior as anomalies.
  • Detection approaches: Anomaly-based detection cannot recognize encrypted packets and modeling enormous dynamic data can produce false alarms.
  • Related applications: Recent IDS studies use approaches including random forest, gradient boosted decision trees, state observers, recurrent autoencoders, and deep learning.
  • Related applications: The reviewed techniques have demonstrated merits, but their adaptability to similar applications remains a concern.

E. Motivation to adapt FL

Centralized ML/DL-based IDS creates privacy, scalability, latency, and data-collection burdens, motivating federated learning as a potential alternative.

  • Centralized training requires users to upload private data to a central entity.
  • Increasing network scale can reduce performance and create a single point of failure affecting integrity and QoS.
  • Centralized processing is time-consuming even though IDS requires fast analysis.
  • IoT devices collect end-user data, creating a risk of exposing sensitive information.
  • Collecting diverse 5G/6G data is burdensome and costly, while FL is presented as supporting privacy, scalability, low latency, and energy efficiency.

III. FL FOR ANOMALY BASED IDS

FL-based anomaly IDS architectures address scalability, heterogeneous traffic, and false-alarm challenges through distributed learning and multi-task detection. The reviewed approaches combine local or distributed detection with federated coordination to reduce centralization constraints and support multiple anomaly tasks.

  • IDS deployment architectures: Centralized IDS architectures scale poorly because transmitting data to one server can create a network bottleneck.Distributed and decentralized architectures use multiple IDS agents but remain constrained by localized data.
  • Federated IDS architecture: FL divides IDS computation between client-side model training and server-side aggregation, reducing server bottlenecks while using edge computation.This server-client work division supports federated deployment across networked devices.
  • Detection challenges: False alarms in anomaly-based IDS arise from insufficient or unlabelled data, inefficient algorithms, and attack techniques that mask malicious traffic.Practical deployment therefore requires sufficient resources, low false alarms, and high precision.
  • Heterogeneous environments: Heterogeneous enterprise networks produce dissimilar traffic because departments, users, bandwidth, and communication requirements vary.A single protection strategy is difficult to manage across every node in a large multinational network.
  • Multi-task federated detection: MT-DNN-FL performs VPN traffic recognition and traffic classification using one model, conserving communication energy and training cost while outperforming several single-task methods.The approach uses the same data for multiple anomaly detection tasks.

C. FL for DDoS attack Detection

The review positions FL as a way to support DDoS detection and anomaly detection without centralizing sensitive network data. Reported approaches combine federated training with fog or cloud infrastructure and heterogeneous client data.

  • DDoS detection motivation: DDoS attacks coordinate multiple systems to overload target systems, making their detection important for end-user quality of service.FL has consequently attracted research attention for DDoS attack detection.
  • Federated mitigation: FLEAM combines FL, fog, and cloud resources to handle large volumes of DDoS attacks in Industrial Internet of Things networks.Fog nodes retrain downloaded models locally and upload model parameters to the cloud.
  • Data privacy: Centralized training can expose collected anomaly-detection data to theft because concentrating data at one location creates an attractive attack target.The cited risks include cloud intrusion attacks such as wrapping, flooding, browser malware injection, and honeypotting.
  • Heterogeneous federated data: Federated data remains on heterogeneous edge devices, where its location and availability change dynamically as new data are generated.The passage reports improved data confidentiality and intrusion-detection accuracy in such environments.

2) Data in Motion:

The reviewed systems address data-in-motion risks, constrained device resources, and network diversity through federated packet analysis and scenario-specific architectures. Reported results include strong anomaly accuracy, low detection latency, and improved performance from grouped client models.

  • Data in motion: Transmitting intrusion information over open or vulnerable routes exposes protocol, service, and addressing data to interception or modification.The passage identifies man-in-the-middle attacks as a relevant threat to packet transmission.
  • Data in motion: FL achieved about 98% output accuracy for most classes on the Aegean Wi-Fi Intrusion Dataset.This result is reported for the cited federated-learning setup.
  • Low-power IoT devices: DÏoT places federated self-learning anomaly detectors at communication nodes and aggregates their models at a predefined server.It represents packets as symbols and uses device-type-specific communication profiles without human interaction or pre-labelled training data.
  • Low-power IoT devices: 95.6% accuracy with 257 ms average detection time makes DÏoT suitable for low-power IoT devices and sensor networks.The system also reports exceptionally low false-alarm rates in real-time scenarios.
  • Various computer networks: FL-based IDS frameworks must be tailored to network type, expected data, data volume, client interaction, and attack scale.Satellite-terrestrial networks additionally differ substantially in computation resources, bandwidth, and dedicated energy.
  • Various computer networks: Grouping client models by a dynamic similarity metric and optimizing model hyperparameters yielded better performance than generic FL architectures.The approach is described for segmented FL in local-area networks.

IV. CHALLENGES

FL-based IDS introduces vulnerabilities and operational costs despite advantages over conventional deep-learning deployment. The principal challenges discussed are communication overhead, latency, false alarms, and poisoning attacks.

  • Key challenges: FL-based IDS remains susceptible to high latency, false alarms, and poisoning attacks during global-model broadcasts and trained-model transmission.These issues are identified as key challenges alongside the approach’s reported superiority over conventional deep-learning models.
  • Communication overhead: The primary FL limitation is communication cost per training round because model parameters travel from the server to clients and back.Bandwidth, packet loss, communication time, and heterogeneous device computation can reduce practical throughput.
  • Communication overhead: A network with millions of devices and a 1% client participation ratio would require the server to handle at least 10,000 clients per broadcast.This illustrates the scale of federated communication demands in intrusion-detection applications.
  • Poisoning attacks: Poisoning attacks can modify client-side labels and alter the global model when malicious data are repeatedly generated.The review describes GAN-assisted data cloning and malicious traffic insertion as attack strategies.

C. High False Alarms through the use of non-IID data

Non-IID and heterogeneous intrusion-detection data can produce asymmetric local models, under-fit global models, and more false alarms. FL deployments also face resource, parameter-tuning, and evolving-anomaly constraints that motivate lightweight and robust solutions.

  • Non-IID data: Non-IID, heterogeneous, imbalanced, redundant, or poisoned edge data can produce asymmetric local models, global under-fitting, and high false-alarm rates.Data size, type, ambiguity, and complexity vary across devices, while problematic local data can cause model failure regardless of training quantity.
  • Resource constraints: Low-power IoT devices may train slowly, drain quickly, or fail because local FL requires data collection, storage, training, and communication.Multiple device failures could create a server deadlock, motivating robust energy-efficient algorithms.
  • Unseen anomalies: FL-based IDS must address unfamiliar anomalies because deviations from labeled attacks may be treated as benign, producing false negatives and Trojan-horse exposure.
  • Parameter heterogeneity: Client-specific model and training parameters are difficult to fine-tune across heterogeneous federated devices.The parameters include learning rate, hidden-layer configuration, epochs, and initial weights.
  • Future directions: The review identifies communication protocols, encryption standards, blockchain, and lightweight deep-learning models as plausible responses to FL-IDS challenges.

A. Communication Efficient Federated IDS

Communication-efficient federated IDS requires reducing model-transfer cost, managing server traffic, and accommodating heterogeneous edge-device capabilities. The reviewed approaches include binary neural networks, encrypted compression, asynchronous participation, modern protocols, resource optimization, and lightweight models.

  • Communication overhead: Bulky model parameters and repeated transfers consume energy, while complex applications produce larger global models and greater communication overhead.
  • Lightweight models: Binary neural networks convert floating-point weights into binary values and bit-wise operations, enabling efficient communication and line-speed traffic classification.The discussed P4 implementation reports high classification accuracy and a low false-alarm rate.
  • Communication protocols: Modern communication technologies can improve feasibility because bandwidth differs substantially across Bluetooth, Wi-Fi, and LPWAN links.The passage gives approximate capacities of 24 Mbps, 1.7 Gbps, and 400 Kbps, respectively.
  • Asynchronous learning: Asynchronous federated learning selects clients according to training efficiency, data quality, and contribution to reduce server traffic accumulation.The associated passage states that final accuracy and throughput increase.
  • Encryption and compression: Encrypted and compressed gradient updates can protect confidentiality while lowering transmission charges for federated weight updates.The ENN framework performs compression and encryption simultaneously through client encoding sub-networks.
  • Resource management: Resource heterogeneity creates intra-structural latency because high-end smartphones can complete more training rounds than low-end IoT devices in the same time.Efficient resource management and optimization algorithms are proposed to accelerate reliable edge computation.
  • Resource management: Deep reinforcement learning and resource-management schemes optimize communication and computing allocation while balancing training time and test accuracy.
  • Lightweight models: Lightweight CNN and Random Neural Network designs support resource-constrained intrusion detection, with RaNN reporting about 99.20% accuracy.

D. Implementation of FL through secure channels

FL-based IDS introduces additional vulnerable points and remains exposed to poisoning, inference, and other attacks. The reviewed security directions include blockchain with verifiable updates, digital twins, and parameter optimization.

  • Security vulnerabilities: Deploying IDS through FL expands the attack surface from a single channel or virtual machine to an entire network of participating systems.
  • Blockchain protection: Blockchain-based collaborative intrusion detection stores raw alerts as secure transactions and uses a BiLSTM model for IDS training.The described framework is intended to protect against poisoning and inference attacks while maintaining good UNSW-NB15 accuracy.
  • Digital twins: Digital twins simulate physical systems with real-world data to predict vulnerabilities and exploitation before deployment.The ATTAIN approach uses GCN-LSTM, GAN, and timed-automation components for cyber-physical anomaly detection.
  • Digital twins: ATTAIN reports an average 8.39% increase in prediction accuracy relative to ADS.
  • Parameter optimization: Meta-heuristic optimization targets federated deep-learning parameters, connectivity, communication costs, and model accuracy.A multi-objective evolutionary approach uses non-dominated sorting, mutation, crossover, and crowding distance.

F. Efficient handling of non-IID data

FL-based IDS must address highly non-IID, noisy packet data caused by differing client usage contexts. Proposed directions include clustering client updates, filtering misleading updates, supporting heterogeneous models, and improving interpretability.

  • Non-IID data: IDS packet data varies substantially with usage, time, purpose, and other factors, so client data cannot be assumed uniform or independent.This heterogeneity creates a central challenge for federated aggregation.
  • Clustering-based handling: Hierarchical clustering groups client models by similar weight updates before training and aggregation to preserve greater uniformity within clusters.Cluster heads aggregate updates locally before forwarding them onward.
  • Robust aggregation: Filtering misleading weight updates helps train the server model properly and supports faster convergence with better global-model performance.
  • Heterogeneous devices: Using different models or architectures could exploit hardware heterogeneity because a common ML/DL model may not be feasible on every edge device.Existing solutions mainly assign the same model and architecture to all clients despite differences in CPU and memory.
  • Interpretability: Interpretability is needed to explain poor FL performance and identify the underlying source, issue, or strength of the system.
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