Cybersecurity in Deep Learning Techniques: Detecting Network Attacks

Abstract

Deep learning techniques have been found to be useful in a variety of fields. Cybersecurity is one such area. In cybersecurity, both Machine Learning and Deep Learning classification algorithms can be used to monitor and prevent network attacks. Additionally, it can be utilized to identify system irregularities that may signal an ongoing attack. Cybersecurity experts can utilize machine learning and deep learning to help make systems safer. Eleven classification techniques, including eight machine learning algorithms (Decision Tree, Random Forest, and Gradient Boosting) and one statistical technique, were employed to examine the popular HTTP DATASET CSIC 2010. (K-Means). Along with XGBoost, AdaBoost, Multilayer Perceptrons, and Voting, three deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and LSTM plus CNN. To evaluate the performance of such models, precision, accuracy, f1-score, and recall are often used metrics. The results showed that when comparing the three deep learning algorithms by the aforementioned metrics, the LSTM with CNN produced the best performance outcomes in this paper. These findings will show that our use of this algorithm allows us to detect multiple attacks and defend against any external or internal threat to the network.