Intrusion Detection Using Deep Learning in Wireless Body Area Networks
Subject Areas : Multimedia Processing, Communications Systems, Intelligent SystemsElham Hajian 1 , Navid Asadi 2
1 - Assistant Professor, Department of Computer Engineering, Univeristy of Bojnord, Bojnord, Iran.
2 - MSc. Student, Department of Computer Engineering, Univeristy of Bojnord, Bojnord, Iran
Keywords: Deep Learning, Intrusion Detection, Cyber Attacks, Wireless Body Area Networks, Precision, Accuracy,
Abstract :
Abstract
The widespread use of information technology and computer networks has led to the emergence of numerous attacks, the main purpose of which is to compromise the security of networks and databases. Wireless body sensor networks, which are a new technology for tracking patient status, are no exception. These networks are of particular importance due to their sensitive medical applications. Any attack and intrusion into these networks will cause irreparable damage to the patient. For this purpose, intrusion detection systems can be used as a security supplement in body sensor network communications. Since common destructive techniques are increasing randomly, traditional methods are unable to respond to attacks. In addition to identifying attacks, the tasks of intrusion detection systems in body sensor networks include learning the behavioral pattern of attacks in the system. One of the challenging issues in these systems is accuracy. New methods have been developed to improve the correct detection rate and minimize the false detection rate, which increases the efficiency of the system by improving the accuracy. In this study, the accuracy increase is done using multilayer perceptron networks, which is one of the deep learning methods. By increasing the number of hidden layers, more efficient learning is done in these networks. The WBAN RSSI dataset, which is taken from Kaggle, is examined in 3 different classes: normal, type 1 attack, and type 2 attack. Then, the proposed algorithm is plotted for precision, accuracy, recall, and F1 score using the dataset alone and in 3 different classes, which shows an accuracy of 0.72.
Introduction: This paper examines intrusion detection in wireless body area networks. A wireless body area network is a network that sends a lot of clinical data remotely to a server for further processing and then to the doctor for further review. Intrusion due to data diversion in a medical system can have dangerous consequences. Therefore, a mechanism is needed to detect and prevent it.
Method: Intrusion detection in this research has been done using deep learning. By increasing the number of hidden layers in the neural network, data processing and learning are increased and they give more accurate results. Each layer has an activation function. The output layer has 3 classes, which are related to the normal class and types of attacks. The most likely class related to these classes is the prediction of this method for the input data, which attacks this data is most exposed to.
Results: Given the data set considered for testing, there are 3 different classes with different precisions. Class 0(normal data) has the highest precision. Class 0 also has the highest F1 score, indicating good performance in detecting normal data. Class 1 has lower recall, meaning it has difficulty identifying some examples of this class. Class 2 has good recall and lower precisions, indicating some false positives in this class.
Discussion: Other improvements were also made to the model in this regard. These improvements include Hyperparameter tuning: can be tested with different learning rates, batch sizes, and optimal number of epochs. Class balance: handling unbalanced datasets can improve recall of minority classes. Advanced architectures: can be tested and researched using recurrent neural networks or convolutional neural networks to improve model performance.
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