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A Federated Learning Framework for On-Device Anomaly Data Detection

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A Federated Learning Framework for On-Device Anomaly Data Detection Federated Learning for Internet of Things Tuo Zhang*, Chaoyang He*, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr Univerisity of Southern California Los Angeles, California, USA ABSTRACT anomaly-detection-based intrusion detection in IoT devices. IoTDe- Federated learning can be a promising solution for enabling IoT fender [2] is another similar framework but obtains a personalized cybersecurity (i.e., anomaly detection in the IoT environment) model by fine-tuning the global model trained with FL.[4] evaluates while preserving data privacy and mitigating the high communica- FL-based anomaly detection framework with learning tasks such tion/storage overhead (e.g., high-frequency data from time-series as aggressive driving detection and human activity recognition. [8] sensors) of centralized over-the-cloud approaches. In this paper, to further proposes an attention-based CNN-LSTM model to detect further push forward this direction with a comprehensive study anomalies in an FL manner, and reduces the communication cost in both algorithm and system design, we build FedIoT platform by using Top-: gradient compression. Recently, [15] even evaluates that contains FedDetect algorithm for on-device anomaly data the impact of malicious clients under the setting of FL-based anom- detection and a system design for realistic evaluation of federated aly detection. However, these works do not evaluate the efficacy learning on IoT devices. Furthermore, the proposed FedDetect of FL in a wide range of attack types and lack system design and learning framework improves the performance by utilizing a local performance analysis in a real IoT platform. adaptive optimizer (e.g., Adam) and a cross-round learning rate To further push forward the research in FL-based IoT cybersecu- scheduler. In a network of realistic IoT devices (Raspberry PI), we rity, we build FedIoT platform with a simple but effective design evaluate FedIoT platform and FedDetect algorithm in both model philosophy that lays the foundation for future scientific research. and system performance. Our results demonstrate the efficacy of The overall design spans dataset, model, algorithm, and system federated learning in detecting a wider range of attack types oc- design. More specifically, we propose a federated learning algorith- curred at multiple devices. The system efficiency analysis indicates mic framework, FedDetect which utilizes adaptive optimizer (e.g., that both end-to-end training time and memory cost are affordable Adam) and cross-round learning rate scheduler, rather than naive and promising for resource-constrained IoT devices. The source FedAvg [10] for local training. Furthermore, FedDetect supports code is publicly available at https://github.com/FedML-AI/FedIoT both global threshold and personalized threshold for different sce- narios. In order to verify the effectiveness of FL for IoT security, we CCS CONCEPTS design a novel method to synthesize the testset from public dataset for FL-based IoT cybersecurity research. Its design aims to evaluate • Security and privacy ! Distributed systems security. whether the global model obtained through FL training can recog- nize more attack types and has higher detection performance (the KEYWORDS evaluation and test dataset cover all attack types on the entire IoT federated learning, cybersecurity, hardware system design network). In addition, we build FedIoT platform for realistic IoT devices with a high degree of modularization and flexible APIs. As 1 INTRODUCTION such, we can add new data, models, and algorithms with only light- weight modification. Most importantly, FedIoT supports both IoT Along with the faster Internet speed and more endpoints brought edge training (e.g., Raspberry PI) and CPU/GPU-based distributed by the 5G, billions of IoT devices online will be deployed [16]. training with the support of MQTT and MPI communication back- Meanwhile, the traffic volume of IoT-based DDoS attacks reaches end, respectively. We evaluate FedIoT platform and FedDetect unprecedented levels [1]. Consequently, the upgrade for anomaly algorithm with both global threshold and personalized threshold detection in the IoT field is imminent. The traditional anomaly on N-BaIoT [11] and LANDER [19] dataset, and also analyze the arXiv:2106.07976v2 [cs.LG] 2 Sep 2021 detection model is trained with centralized data from many IoT system performance comprehensively (i.e., computational speed, devices or solely trained on data from a small scale of devices. How- communication cost, and memory cost). ever, the centralized over-the-cloud approaches no longer fit the 5G Our results demonstrate the efficacy of federated learning in era due to data privacy and extremely high communication/storage detecting a large range of attack types. More specially, we find that overhead (e.g., high-frequency data from time-series sensors) for the global model obtained through FedDetect training has higher pooling data from many IoT devices. This is especially the case detection performance, while only centralizing the data from a when the attacks spread to large-scale attack types and devices, few IoT devices has worse performance due to insufficient benign since centralizing the data from small-scale devices is difficult to training samples or attack types. FedDetect with personalized obtain an effective detection model. threshold also suggests that the detection based on FL may beat Federated Learning (FL) can offer an alternative approach for IoT solely training on insufficient local data of a single device. Our cybersecurity while mitigating the above challenges. FL is a trend- system efficiency analysis shows that the training time memory ing paradigm that can train a global or personalized model with- cost occupies only a small fraction of the entire host memory of out centralizing data from edge devices [5]. There have also been the IoT platform (Raspberry Pi), and the end-to-end training time several recent works that bring FL to the realm of cybersecurity. is feasible (less than 1 hour) for practical applications, although the For example, DIoT [12] employs a federated learning approach to AIChallengeIoT ’21, November 15-17, 2021, Coimbra, Portugal Tuo and Chaoyang, et al. Figure 1: Overview of FedIoT Platform ratio of communication cost is high. In essence, we summarize our FL system, FedIoT, for this setting to analyze both algorithmic and contributions as follows: system performance in real IoT devices (e.g., Raspberry Pi). We build FedIoT platform with a simple but effective design phi- • We provide a novel method to synthesize dataset for FL- losophy that lays the foundation for future scientific research. The based IoT cybersecurity research, aiming at evaluating the overall design is illustrated in Figure 1. More specifically, the entire efficacy of FL in recognizing attack types in a wide range. software architecture consists of three layers: the application layer, • We propose a federated learning framework, FedDetect, the algorithm layer, and the infrastructure layer. We make each for IoT cybersecurity. Most importantly, FedDetect incorpo- layer and module performs its duty and has a high degree of modu- rates local adaptivity and cross-round learning rate scheduler larization. In the application layer, FedIoT provides a one-line API for effective distribution optimization and also supports both to launch the federated training on IoT devices in a distributed com- global and personalized threshold for different scenarios. puting manner. This API takes non-I.I.D. datasets (Section 2.2) and a • We build FedIoT platform for realistic IoT devices (e.g., Rasp- simple but effective deep Autoencoder model (Section 2.3) as itsin- berry PI). Our performance analysis, system design, and flexi- put; at the algorithm layer, FedIoT supports various FL algorithms ble APIs demonstrate the feasibility and generality for future such as FedAvg [9], FedOPT [14] and their customized versions exploration. FedDetect for anomaly detection (Section 2.4); at the infrastruc- ture layer, FedIoT aims to support lightweight communication APIs 2 ALGORITHM AND SYSTEM DESIGN with MQTT [3] (i.e., Message Queuing Telemetry Transport, a stan- dard for IoT messaging), and customized PyTorch library [13] that 2.1 Overview can execute primitives of on-device model training such as for- Federated learning (FL)-based IoT cybersecurity aims to detect net- ward propagation, calculating loss function and back-propagation work intrusion in IoT devices without centralizing a large amount (Section A). Our proposed light-weighted FedIoT framework could of high frequent edge data. A generic setting is that many IoT de- support the direct implementation of Federated Learning on AI- vices collaboratively train a deep Autoencoder model for anomaly enabled IoT edge devices, such as Raspberry Pi and NVIDIA Jetson detection via federated learning. In this work, our goal is to build a Nano. Federated Learning for Internet of Things AIChallengeIoT ’21, November 15-17, 2021, Coimbra, Portugal 2.2 Dataset and Preprocessing Intuitively, the global model obtained through FL training can We introduce a widely used public IoT dataset for anomaly detection recognize more attack types and have higher detection performance, and then synthesize a dataset for realistic evaluation on the FL- while the local training alone may have poor performance due to based method. In addition, we also introduce another novel private insufficient benign training samples or attack types. dataset to consolidate
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