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The quality and reliability of internet of thing (IoT) ecosystems heavily rely on accurate and dependable sensor data. However, resource limited sensors are prone to failure due to various factors like environmental disturbances and electrical noise in which they can produce erroneous and faulty measurements. These can have significant consequences across different domains, including a threat to safety in critical systems. Though many researches have been conducted, the existing literature primarily focuses on fault detection in the sensor data, while fault detection is useful, it is still a reactive approach that identifies the faults after they have occurred, meaning that actions are taken after the fault has already impacted the system, potentially leading to negative consequences. In this study, a proactive approach has been proposed by developing a two-stage solution. In the first stage, a hybrid convolutional neural network-long short term memory (CNN-LSTM) model was trained to forecast sensor measurements based on historical data, while in the second stage, the forecasted measurements were passed to a hybrid convolutional neural network-multi layer perceptron (CNN-MLP) model that has been trained to recognize different types of sensor faults and classify the new measurements accordingly. By passing the forecasted sensor values as input to the classification model and categorizing them as normal, bias, drift, random or poly-drift, anticipated the potential faults before they manifest. The publicly available Intel Lab data raw dataset is used, which has been annotated and fault-injected. For regression, gated recurrent unit (GRU), Long short term memory (LSTM), bidirectional long short term memory (BiLSTM), convolutional neural network-gated recurrent unit (CNN-GRU), convolutional neural network-long short term memory (CNN-LSTM), and convolutional neural network-bidirectional long short term memory (CNN-BiLSTM), were evaluated and compared their performance using root mean squared error (RMSE), mean squared error (MSE) and mean absolute error (MAE) with 2-split time series cross-validation. CNN-LSTM outperformed the other models with a Mean Absolute Error of 2.0957 for a 45 time steps forecast. For the classification task, convolutional neural network (CNN), multi-layer perceptron (MLP), and convolutional neural network-multi layer perceptron (CNN-MLP) evaluated using the metrics accuracy, precision, recall, and F1-score with 5 and tenfold cross-validations. CNN-MLP outperformed the others with accuracy of 96.11% for bias, 99.33% for drift, and 98.61% for random and 98.81% for poly-drift. The average accuracy across the 4 faults is 98.21%, which is a 0.3% increase from the baseline work 97.91%. By adopting a proactive approach to sensor fault prediction and classification, this research aims to enhance the reliability and efficiency of IoT systems, allowing for preventive measures to be taken before faults have a detrimental impact.
The quality and reliability of internet of thing (IoT) ecosystems heavily rely on accurate and dependable sensor data. However, resource limited sensors are prone to failure due to various factors like environmental disturbances and electrical noise in which they can produce erroneous and faulty measurements. These can have significant consequences across different domains, including a threat to safety in critical systems. Though many researches have been conducted, the existing literature primarily focuses on fault detection in the sensor data, while fault detection is useful, it is still a reactive approach that identifies the faults after they have occurred, meaning that actions are taken after the fault has already impacted the system, potentially leading to negative consequences. In this study, a proactive approach has been proposed by developing a two-stage solution. In the first stage, a hybrid convolutional neural network-long short term memory (CNN-LSTM) model was trained to forecast sensor measurements based on historical data, while in the second stage, the forecasted measurements were passed to a hybrid convolutional neural network-multi layer perceptron (CNN-MLP) model that has been trained to recognize different types of sensor faults and classify the new measurements accordingly. By passing the forecasted sensor values as input to the classification model and categorizing them as normal, bias, drift, random or poly-drift, anticipated the potential faults before they manifest. The publicly available Intel Lab data raw dataset is used, which has been annotated and fault-injected. For regression, gated recurrent unit (GRU), Long short term memory (LSTM), bidirectional long short term memory (BiLSTM), convolutional neural network-gated recurrent unit (CNN-GRU), convolutional neural network-long short term memory (CNN-LSTM), and convolutional neural network-bidirectional long short term memory (CNN-BiLSTM), were evaluated and compared their performance using root mean squared error (RMSE), mean squared error (MSE) and mean absolute error (MAE) with 2-split time series cross-validation. CNN-LSTM outperformed the other models with a Mean Absolute Error of 2.0957 for a 45 time steps forecast. For the classification task, convolutional neural network (CNN), multi-layer perceptron (MLP), and convolutional neural network-multi layer perceptron (CNN-MLP) evaluated using the metrics accuracy, precision, recall, and F1-score with 5 and tenfold cross-validations. CNN-MLP outperformed the others with accuracy of 96.11% for bias, 99.33% for drift, and 98.61% for random and 98.81% for poly-drift. The average accuracy across the 4 faults is 98.21%, which is a 0.3% increase from the baseline work 97.91%. By adopting a proactive approach to sensor fault prediction and classification, this research aims to enhance the reliability and efficiency of IoT systems, allowing for preventive measures to be taken before faults have a detrimental impact.
No abstract
This state-of-the-art review comprehensively examines the landscape of Distributed Denial of Service (DDoS) anomaly detection in Software Defined Networks (SDNs) through the lens of advanced Machine Learning (ML) and Deep Learning (DL) techniques. The application domain of this work is focused on addressing the inherent security vulnerabilities of SDN environments and developing an automated system for detecting and mitigating network attacks. The problem focused on in this review is the need for effective defensive mechanisms and detection methodologies to address these vulnerabilities. Conventional network measurement methodologies are limited in the context of SDNs, and the proposed ML and DL techniques aim to overcome these limitations by providing more accurate and efficient detection and mitigation of DDoS attacks. The objective of this work is to provide a comprehensive review of related works in the field of SDN anomaly detection recent advances, categorized into two groups via ML and DL techniques. The proposed systems utilize a variety of techniques, including Supervised Learning (SL), Unsupervised Learning (UL) Ensemble Learning (EL) and DL solutions, to process IP flows, profile network traffic, and identify attacks. The output comprises the mitigation policies learned by ML/DL techniques, and the proposed systems act as sophisticated gatekeepers, applying automated mitigation policies to curtail the extent of damage resulting from these attacks. The results obtained from the evaluation metrics, including accuracy, precision, and recall, confirm the marked effectiveness of the proposed systems in detecting and mitigating various types of attacks, including Distributed Denial of Service (DDoS) attacks. The proposed systems' foundational contributions are manifest in their efficacy for both DDoS attack detection and defense within the SDN environment. However, the review acknowledges certain inherent limitations and the pressing need for further validation within real-world scenarios to assess the proposed methods' practicality and effectiveness. In summary, this systematic review offers valuable perspectives on the present status of Distributed Denial-of-Service detection in Software-Defined Networks employing Machine Learning and Deep Learning methodologies, highlighting the strengths and limitations of various proposed systems and identifying areas for future research and development.INDEX TERMS Anomaly Detection, deep learning (DL), distributed denial of service (DDoS), machine Learning (ML), software defined network (SDN).
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