Title: Applications of Deep Learning in Intrusion Detection: A Literature Review

Abstract: Intrusion detection is an important aspect of information security. Traditional intrusion detection methods are based on signature-based or anomaly-based techniques, which have limitations in detecting unknown attacks and reducing false positives. In recent years, deep learning has been widely used in intrusion detection due to its ability to automatically learn complex features from raw data. This paper reviews the latest research on the application of deep learning in intrusion detection. We summarize the different types of neural networks used for intrusion detection and discuss their advantages and disadvantages. The review also covers the datasets commonly used for training and testing these models, as well as the evaluation metrics used to assess their performance. Finally, we identify some open challenges and future directions for research.

Keywords: Intrusion detection, Deep learning, Neural networks, Datasets, Evaluation metrics

Introduction: Information security has become an increasingly important concern with the growing reliance on digital technologies in our daily lives. One critical aspect of information security is detecting and preventing unauthorized access to computer systems by intruders. Traditional intrusion detection methods are typically based on signature-based or anomaly-based techniques. Signature-based methods match known attack patterns against incoming traffic while anomaly-based methods detect deviations from normal behavior patterns. However, these approaches have limitations in detecting unknown attacks and reducing false positives.

Deep learning is a branch of machine learning that has gained significant attention in recent years due to its ability to automatically learn complex features from raw data without human intervention. Deep learning algorithms can be applied to various fields including image recognition, speech recognition, natural language processing, and now even intrusion detection.

The goal of this paper is to provide a comprehensive review of recent research efforts that apply deep learning techniques for intrusion detection purposes. We will survey different neural network architectures proposed for this purpose along with their respective strengths and weaknesses.

Literature Review: Recent studies have shown that deep learning can outperform traditional methods in detecting intrusions. In particular, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs) have been widely used for intrusion detection.

CNNs are typically applied to image classification problems, but they can also be used for intrusion detection by treating network traffic as a 2D signal. They have been shown to outperform traditional machine learning algorithms such as decision trees and support vector machines.

RNNs are effective in capturing temporal dependencies in network traffic data. By modeling the sequence of packets, RNNs can detect patterns that are indicative of an ongoing attack.

DBNs are unsupervised models that can learn hierarchical representations of input data. They have been used for anomaly detection based on their ability to learn complex distributions without human intervention.

To train and evaluate deep learning models for intrusion detection, researchers typically use publicly available datasets such as KDD Cup 99, UNSW-NB15, and NSL-KDD. Evaluation metrics commonly used include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).

Despite the promising results achieved by deep learning techniques for intrusion detection, there are still some challenges that need to be addressed. One major challenge is the lack of large-scale labeled datasets with diverse attack scenarios. Additionally, interpreting the internal workings of these models is still a difficult task which hinders their adoption in real-world applications.

Conclusion: This paper has provided a comprehensive review of recent research efforts on applying deep learning techniques for intrusion detection purposes. We have discussed different types of neural networks proposed for this purpose along with their respective strengths and weaknesses. We have also reviewed the datasets commonly used to train and test these models along with evaluation metrics commonly used to assess performance. Finally, we identified some open challenges and future directions for research in this field.

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