Deep learning-enabled anomaly detection for IoT systems

Abusitta, Adel; Carvalho, Glaucio H.S. de; Wahab, Omar Abdel; Halabi, Talal; Fung, Benjamin C. M.; Mamoori, Saja Al

doi:10.1016/j.iot.2022.100656

Cited by 44 publications

(16 citation statements)

References 27 publications

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“…Increasing levels of data (traffic) intricacy and a requirement for greater classification accuracy have led researchers to investigate applications of deep learning neural networks in the mainstream IoT domain. DL network structures have been shown to aid dynamic feature selection and significantly reduce false positives during traffic classification in heterogeneous IoT settings [26,27]. Abusitta et al [26] highlighted a preference for DL-based classification to recognize abnormal behavior in IoT traffic, prone to external noise over common ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…DL network structures have been shown to aid dynamic feature selection and significantly reduce false positives during traffic classification in heterogeneous IoT settings [26,27]. Abusitta et al [26] highlighted a preference for DL-based classification to recognize abnormal behavior in IoT traffic, prone to external noise over common ML algorithms. Using de-noising autoencoders during the data pre-processing stage, the DL classifier was able to discriminate malicious traffic patterns by dynamically extracting useful features despite an unstable operational environment.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional machine learning techniques including Iterative Dichotomiser 3 (ID3), decision tree (C 5.0), classification and regression tree (CART), and artificial neural network (ANN) have been previously recommended to provide a measure of security for IoBNT biocyber interfaces [25,26]. Deep learning is a sub-field of traditional machine learning, providing increased flexibility and accuracy over classical learning algorithms, and offering the ability to incrementally learn and extrapolate new features from a limited set of training data [34,35].…”

Section: Deep-learning Enabled Traffic Analysismentioning

confidence: 99%

“…Although the overall difference in recorded metrics for the CNN, and CNN + LSTM model was minimal, the LSTM layer in the latter provides the additional benefit of extracting temporal correlations present in the in vivo traffic diffusion parameters such as the cumulative concentration rate released molecules (Ψ 0 ), the diffusion rate of molecules (m 0 ), and the drug delivery release rate (λ). Compared to ML, and DL models for generic and IoBNT-interface-specific anomaly detection classifiers in [23][24][25][26][27][28][29][30]33], the optimal hybrid model can classify anomalous parameter traffic by dynamically reducing feature dimensionality which would be manually intensive if using traditional ML. Summarization of key differences and advantages of the proposed hybrid model over complimentary schemes are detailed in 7.…”

Section: Performance Comparisonmentioning

confidence: 99%

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Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things

Bakhshi,

Zafar

2023

Sensors

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The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~1–100 nm) biological transceivers to collect in vivo signaling information from the human body and communicate it to healthcare providers over the Internet. Bio-nano-things (BNT) offer external actuation of in-body molecular communication (MC) for targeted drug delivery to otherwise inaccessible parts of the human tissue. BNTs are inter-connected using chemical diffusion channels, forming an in vivo bio-nano network, connected to an external ex vivo environment such as the Internet using bio-cyber interfaces. Bio-luminescent bio-cyber interfacing (BBI) has proven to be promising in realizing IoBNT systems due to their non-obtrusive and low-cost implementation. BBI security, however, is a key concern during practical implementation since Internet connectivity exposes the interfaces to external threat vectors, and accurate classification of anomalous BBI traffic patterns is required to offer mitigation. However, parameter complexity and underlying intricate correlations among BBI traffic characteristics limit the use of existing machine-learning (ML) based anomaly detection methods typically requiring hand-crafted feature designing. To this end, the present work investigates the employment of deep learning (DL) algorithms allowing dynamic and scalable feature engineering to discriminate between normal and anomalous BBI traffic. During extensive validation using singular and multi-dimensional models on the generated dataset, our hybrid convolutional and recurrent ensemble (CNN + LSTM) reported an accuracy of approximately ~93.51% over other deep and shallow structures. Furthermore, employing a hybrid DL network allowed automated extraction of normal as well as temporal features in BBI data, eliminating manual selection and crafting of input features for accurate prediction. Finally, we recommend deployment primitives of the extracted optimal classifier in conventional intrusion detection systems as well as evolving non-Von Neumann architectures for real-time anomaly detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Deep-learning Enabled Traffic Analysismentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

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Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things

Bakhshi,

Zafar

2023

Sensors

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“…Among the big contributors to the security of the IoT, Abusitta et al [11] Pan et al [13] address anomaly detection in SHM for civil infrastructures through transfer learning-based methods. Their approach works by exploiting pattern similarity that occurs across several different bridges, dealing with sensor faults and environmental noise.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Metro Rail Efficiency: A Predictive Maintenance Approach Leveraging Machine Learning and Deep Learning Technologies

Nair,

et al. 2024

Preprint

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This paper looks into the modeling and implementation of a predictive maintenance system of an air production unit for a metro rail designed to suit the challenges detailed by the industrial sector. Using modern machine learning, deep learning, and AI techniques, the system identifies the faulty equipment well in advance when applied to the huge volume of sensor data. One of the major functionalities of the system is an interface designed to alert users, whereby the instant alerts are made to the maintenance personnel for faster intervention, minimization of the possible downtime. The basis of the study is the application of the predictive maintenance system within the unit of air production. It indicates great efficacy toward the prediction of a failure. A wide variety of ML and deep learning models were experimented with and fine-tuned carefully by training and evaluation over the training set and also over the testing set to ensure predictive accuracies. For example, from the above comparative model analysis, the most suitable predictive approach was indicated through the use of accuracy. Deep Learning Models, including LSTM, RNN, and BiLSTM, have been exceedingly good, with all the above models giving an accuracy of above 99.7 percent. Notably, Adaboost, a Boosting technique also has performed well. The culmination of this project highlights the pivotal role of AI and ML technologies in advancing predictive maintenance strategies within the industrial sector. The findings illustrate the potential of these technologies to transform maintenance practices, optimize operational processes and contribute to the overall sustainability of industrial operations. This paper contributes valuable insights into the feasibility and effectiveness of AI-driven predictive maintenance systems.

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