Intrusion Detection Attacks Classification using Machine Learning Techniques

Al-qdah, Majdi

doi:10.53409/mnaa/jcsit/2201

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Both these model plays a substantial role in the IoT environment, and the analysis is done with MATLAB simulation environment. The outcomes of the approach are compared with various existing methods like Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Artificial Neural Networks (ANN), respectively [32][33][34][35].…”

Section: Methodsmentioning

confidence: 99%

Routing with Cooperative Nodes Using Improved Learning Approaches

Raja¹,

Satheesh²,

Dennis³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, etc. The anticipated s − LSTM and b − LSTM model intends to ensure Quality of Service (QoS) even in changing network topology. The performance of the proposed b − LSTM and s − LSTM is measured by comparing the significance of the model with various prevailing approaches. Sometimes, the performance is measured with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for measuring the error rate of the model. The prediction of error rate is made with Learning-based Stochastic Gradient Descent (L − SGD). This gradual gradient descent intends to predict the maximal or minimal error through successive iterations. The simulation is performed in a MATLAB 2020a environment, and the model performance is evaluated with diverse approaches. The anticipated model intends to give superior performance in contrast to prevailing approaches.

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Section: Methodsmentioning

confidence: 99%

Routing with Cooperative Nodes Using Improved Learning Approaches

Raja¹,

Satheesh²,

Dennis³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…This model is naturally asymmetric as output layers' weight is not required during the encoding process but prevails during the decoding process. The unnecessary mapping can eliminate the necessity of low-dimensionality features [29][30][31][32]. The anticipated model can remove the learning system naturally.…”

Section: Consider the Training Samplesmentioning

confidence: 99%

An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

Sherubha¹,

Sasirekha²,

Anguraj³

et al. 2023

Computer Systems Science and Engineering

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The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence, to boost the functionality of unsupervised learning, an effectual auto-encoder is applied for feature selection to select good features. Finally, the Naïve Bayes classifier is used for classification purposes. This approach exposes the finest generalization ability to train the data. The unlabelled data is also used for adoption towards data analysis. Here, redundant and noisy samples over the dataset are eliminated. To validate the robustness and efficiency of NIDS, the anticipated model is tested over the NSL-KDD dataset. The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%, which is higher compared to J48, AB tree, Random Forest (RF), Regression Tree (RT), Multi-Layer Perceptrons (MLP), Support Vector Machine (SVM), and Fuzzy. Similarly, False Alarm Rate (FAR) and True Positive Rate (TPR) of Naive Bayes (NB) is 0.3 and 0.99, respectively. When compared to prevailing techniques, the anticipated approach also delivers promising outcomes.

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“…the vector x was specified in (27) and after that, the bounded constant c is provided as in (28). This demonstrates that output of the sensor nodes may be approximated as the RNN with input of m prior output sample from the similar nodes, as well as present and m prior output samples from surrounding sensor.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

Varatharajan,

Ramasamy,

Marappan

et al. 2023

IJEECS

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Sensor networks have helped wireless communication systems. Over the last decade, researchers have focused on energy efficiency in wireless sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors have limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using the witness-based data fusion technique, the evaluated network lifetime, energy consumption, communication overhead, end-to-end delay, and data delivery ratio. Energyefficient data fusion optimizes energy utilization using squirrel search optimization and a recurrent neural network. The method allows the system to recognize a sensor with excessive energy dissipation and relocate data fusion to a more energy-efficient node. The proposed model was compared against artificial neural network-particle swarm optimization (ANN-PSO), cuckoo optimization algorithm-back propagation neural network (COABPNN), Elman neural network-whale optimization algorithm (ENN-WOA), and extreme learning machine-particle swarm optimization (ELM-PSO). The model achieved 94.50% network lifetime, 26.63% communication overhead, 93.85% data delivery ratio, 10.50 ms end-to-end delay, and 282 J energy usage.

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Intrusion Detection Attacks Classification using Machine Learning Techniques

Cited by 8 publications

References 6 publications

Routing with Cooperative Nodes Using Improved Learning Approaches

Routing with Cooperative Nodes Using Improved Learning Approaches

An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

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