FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches.

[1]  Ashutosh Kumar Singh,et al.  SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT From Data Breach , 2022, IEEE Transactions on Industrial Informatics.

[2]  Elena Sitnikova,et al.  X-IIoTID: A Connectivity-Agnostic and Device-Agnostic Intrusion Data Set for Industrial Internet of Things , 2022, IEEE Internet of Things Journal.

[3]  Naveen K. Chilamkurti,et al.  A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms , 2021, Sensors.

[4]  Thomas Newe,et al.  Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap , 2021, Sensors.

[5]  Pedro Miguel S'anchez S'anchez,et al.  Federated Learning for Malware Detection in IoT Devices , 2021, Comput. Networks.

[6]  Haomiao Yang,et al.  Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence , 2020, IEEE Transactions on Industrial Informatics.

[7]  Qi Li,et al.  DL-IDS: Extracting Features Using CNN-LSTM Hybrid Network for Intrusion Detection System , 2020, Secur. Commun. Networks.

[8]  Di Wu,et al.  LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[9]  Erchin Serpedin,et al.  Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation , 2020, IEEE Transactions on Smart Grid.

[10]  Mohammad Hossein Zavvar,et al.  Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree , 2020, 2020 6th International Conference on Web Research (ICWR).

[11]  Yun Yang,et al.  An overview of data fusion techniques for Internet of Things enabled physical activity recognition and measure , 2020, Inf. Fusion.

[12]  Yulei Wu,et al.  An Intelligent Anomaly Detection Scheme for Micro-Services Architectures With Temporal and Spatial Data Analysis , 2020, IEEE Transactions on Cognitive Communications and Networking.

[13]  Monica Nicoli,et al.  Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks , 2019, IEEE Internet of Things Journal.

[14]  Di Wu,et al.  Multi-Task Network Anomaly Detection using Federated Learning , 2019, SoICT.

[15]  Chai Kiat Yeo,et al.  Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model , 2019, MLN.

[16]  Ke Zhang,et al.  Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things , 2019, IEEE Network.

[17]  Xin Qin,et al.  FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare , 2019, IEEE Intelligent Systems.

[18]  Haipeng Yao,et al.  Blockchain-Based Software-Defined Industrial Internet of Things: A Dueling Deep ${Q}$ -Learning Approach , 2019, IEEE Internet of Things Journal.

[19]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[20]  Po Yang,et al.  Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review , 2018, Journal of Biomedical Informatics.

[21]  Walid Saad,et al.  Federated Learning for Ultra-Reliable Low-Latency V2V Communications , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[22]  Samuel Marchal,et al.  DÏoT: A Federated Self-learning Anomaly Detection System for IoT , 2018, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[23]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[24]  Nick Feamster,et al.  Machine Learning DDoS Detection for Consumer Internet of Things Devices , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[25]  Stephen Hailes,et al.  Security of smart manufacturing systems , 2018 .

[26]  Erol Gelenbe,et al.  Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments , 2018, Euro-CYBERSEC.

[27]  Qingju Wang,et al.  When Intrusion Detection Meets Blockchain Technology: A Review , 2018, IEEE Access.

[28]  Qi Shi,et al.  A Deep Learning Approach to Network Intrusion Detection , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[29]  Geyong Min,et al.  Time Series Anomaly Detection for Trustworthy Services in Cloud Computing Systems , 2017, IEEE Transactions on Big Data.

[30]  Yang Yu,et al.  A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks , 2016, Sensors.

[31]  Christian Diedrich,et al.  Accelerated deep neural networks for enhanced Intrusion Detection System , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[32]  Beibei Li,et al.  DDOA: A Dirichlet-Based Detection Scheme for Opportunistic Attacks in Smart Grid Cyber-Physical System , 2016, IEEE Transactions on Information Forensics and Security.

[33]  Beibei Li,et al.  BLITHE: Behavior Rule-Based Insider Threat Detection for Smart Grid , 2016, IEEE Internet of Things Journal.

[34]  K. K. Sahu,et al.  Normalization: A Preprocessing Stage , 2015, ArXiv.

[35]  M. Angela Sasse,et al.  Bringing security home: a process for developing secure and usable systems , 2003, NSPW '03.

[36]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[37]  Elena Sitnikova,et al.  Asynchronous Peer-to-Peer Federated Capability-based Targeted Ransomware Detection Model for Industrial IoT , 2021, IEEE Access.

[38]  Ismail Butun,et al.  Industrial Networks and IIoT: Now and Future Trends , 2020, Industrial IoT.

[39]  William Schneble.  Attack Detection Using Federated Learning in Medical Cyber-Physical Systems , 2019 .

[40]  Chunjie Zhou,et al.  Anomaly Detection Based on Zone Partition for Security Protection of Industrial Cyber-Physical Systems , 2018, IEEE Transactions on Industrial Electronics.