Model 1: Per-Time Instance Classifier - Feedforward Neural Network

[Audio] Model 1: Per-Time Instance Classifier - Feedforward Neural Network Intrusion Detection for IoT Wireless Sensor Networks (WSN-DS Dataset) Model 1: Per-Time Instance Classifier - Feedforward Neural Network Intrusion Detection for IoT Wireless Sensor Networks (WSN-DS Dataset).

[Audio] FNN Model Feedforward Neural Network (FNN) is a type of artificial neural network in which information flows in a single direction. The architecture of an FNN consists of the input layer, one or more hidden layers, and the output layer. Nodes in each layer are connected to nodes in the next layer using weighted connections. A weight is a multiplier that determines the effect a given node's output has on the next layer. Explain the basic architecture of a Neural Network, model training and ... FNN Model Feedforward Neural Network (FNN) is a type of artificial neural network in which information flows in a single direction. The architecture of an FNN consists of the input layer, one or more hidden layers, and the output layer. Nodes in each layer are connected to nodes in the next layer using weighted connections. A weight is a multiplier that determines the effect a given node's output has on the next layer..

[Audio] FNN Model … FNN Model Architecture. The structure of neural network in which softmax is used as activation... | Download Scientific Diagram.

[Audio] FNN Model formula for softmax function. Softmax function Explained Clearly and in Depth ｜Deep Learning fundamental | by Suetsugu | Medium.

[Audio] Existing FNN Model The model 1 is evaluated using the existing research paper titled "Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems" by Ajay Shah, Sophine Clachar, Manfred Minimair, and Davis Cook from Seton Hall University, was published as a conference paper in October 2020. No Hyperparameter tuning is done for this model when we are trying this model. Existing FNN Model The model 1 is evaluated using the existing research paper titled "Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems" by Ajay Shah, Sophine Clachar, Manfred Minimair, and Davis Cook from Seton Hall University, was published as a conference paper in October 2020. No Hyperparameter tuning is done for this model when we are trying this model..

[Audio] Research Paper for the existing model. [image] Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems up *tmks is ISIDS, 191. a this An (AIDS' mk euving of Ap-:h In TCP US O. US of v.ying III. 895 mis* 2 "IAI 3", dim. —k IV DATA n. by all Which. Clmifi« 81 131. Ill. f. ASNM lt.'N of 0.1 09. with Ofwhich.

[Audio] Developing of the existing model Developing of the existing model.

[Audio] Model Architecture Input Layer: 17 features Hidden Layers: Dense (40 neurons, ReLU activation) Dense (60 neurons, ReLU activation) Dense (30 neurons, ReLU activation) Dense (10 neurons, ReLU activation) Output Layer: Dense (5 neurons, Softmax activation) Model Architecture Input Layer: 17 features Hidden Layers: Dense (40 neurons, ReLU activation) Dense (60 neurons, ReLU activation) Dense (30 neurons, ReLU activation) Dense (10 neurons, ReLU activation) Output Layer: Dense (5 neurons, Softmax activation).

[Audio] Model Compilation Details Optimizer: SGD (Learning Rate=0.01, Momentum=0.75) Loss Function: Categorical Crossentropy Metrics: Accuracy Training Settings: Epochs: 100 Batch Size: 500 Callbacks: EarlyStopping, TensorBoard Logging Class Weights: Applied Model Compilation Details Optimizer: SGD (Learning Rate=0.01, Momentum=0.75) Loss Function: Categorical Crossentropy Metrics: Accuracy Training Settings: Epochs: 100 Batch Size: 500 Callbacks: EarlyStopping, TensorBoard Logging Class Weights: Applied.

[Audio] Results of the Model Results of the Model.

[Audio] Confusion Matrix Model achieves strong performance for 'Normal' and 'Blackhole' classes. Some confusion between 'Grayhole' and 'Normal'. Confusion Matrix Model achieves strong performance for 'Normal' and 'Blackhole' classes. Some confusion between 'Grayhole' and 'Normal'..

[Audio] Performance Matrix Metric Results Accuracy 0.959704 Precision 0.967230 Recall 0.959704 F1 Score 0.96215 Performance Matrix.

[Audio] Model Summary As mentioned in the research paper they said that “During the training process, the initial accuracy for each model was less than 50% for the first 100 epochs. However, after approximately 400 epochs, the accuracy, for all the models, leveled off at around 85% with the best model having approximately 95% accuracy ”. But when we are trying this model it is given the same accuracy level in less epochs(i.e., 14 epochs) Model Summary As mentioned in the research paper they said that "During the training process, the initial accuracy for each model was less than 50% for the first 100 epochs. However, after approximately 400 epochs, the accuracy, for all the models, leveled off at around 85% with the best model having approximately 95% accuracy ". But when we are trying this model it is given the same accuracy level in less epochs(i.e., 14 epochs).

[Audio] High Accuracy: 32,331 'Normal' instances correctly classified Challenges: 532 'Grayhole' instances misclassified as 'Blackhole' 132 'Normal' instances misclassified as 'TDMA' High Accuracy: 32,331 'Normal' instances correctly classified Challenges: 532 'Grayhole' instances misclassified as 'Blackhole' 132 'Normal' instances misclassified as 'TDMA'.