Deep Learning Based Intrusion Detection Models for Next-Generation Cyber Security Applications

Abstract

Since digital technologies such as cloud computing, IoT, and next-generation communication network
work at an extremely fast pace, there has been an increase in the frequency and complexity of the cyber-attack,
making the IDS irrelevant in the current world of cybersecurity. Signature-based IDS and rule-based security
systems cannot forecast the changing attack patterns as well as the zero-day attacks. Auto-extraction of complex
data from the huge network information or improving intrusion detection ability can be done through deep
learning. This paper presents the test of the Deep Learning-Based Intrusion Detection Model for Next-Generation
Cyber Security Applications. We wish to develop and evaluate a deep learning system for the detection of regular
and malicious network traffic, and to remove false alarms. Implementation: Python. The public intrusion detection
dataset is prepared for training with cleaning, feature encoded, normalised and split into training, validation and
testing set. We will design a Deep Neural Network (DNN) with Tensor Flow and Keras having multiple hidden
layers, ReLU activation function, Batch Normalization, Dropout Regularization, Softmax classifier, and the Adam
optimizer. Using measures like accuracy, precision, recall, F1 Score, Specificity, ROC-AUC, and Confusion Matrix
analysis, our deep learning approach is compared with Decision Tree, Support Vector Machine, Random Forest,
and XGBoost approaches. Our results indicate that our approach yields a well-classifying deep learning approach,
multiple categories cyberattack detection capabilities, and minimal false positives/ false negatives. This research
proves that deep learning is paving the way for the future of intrusion detection in cloud computing, IoT, 5G, and
other next-generation network environments.