Deep Learning Applications for Intrusion Detection in Network Traffic
The paper discusses the issues of applying deep learning methods for detecting computer attacks in network traffic. The results of the analysis of relevant studies and reviews of deep learning applications for intrusion detection are presented. The most used deep learning methods are discussed and compared. The classification system of deep learning methods for intrusion detection is proposed. Current trends and challenges of applying deep learning methods for detecting computer attacks in network traffic are identified. The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection. The synthesized neural network is compared to the previously developed model based on the use of the Random Forest classifier. The usage of the deep learning method enabled to simplify the feature engineering stage, and evaluation metrics of Random Forest and CNN-BiLSTM models are close. This confirms the prospects for the application of deep learning methods for intrusion detection.
Code (1)
Tasks
Deep LearningFeature EngineeringIntrusion DetectionNetwork Intrusion DetectionSimilar Papers 제목 키워드 기반
Using EBGAN for Anomaly Intrusion Detection
As an active network security protection scheme, intrusion detection system (IDS) undertakes the important responsibility of detecting network attacks in the form of malicious network traffic. Intrusion detection technol…
Intrusion DetectionA Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks
A significant increase in the number of interconnected devices and data communication through wireless networks has given rise to various threats, risks and security concerns. Internet of Things (IoT) applications is dep…
Edge-computingIntrusion DetectionPWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks
With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack…
Generative Adversarial NetworkIntrusion DetectionNetwork Intrusion DetectionHybrid Model For Intrusion Detection Systems
With the increasing number of new attacks on ever growing network traffic, it is becoming challenging to alert immediately any malicious activities to avoid loss of sensitive data and money. This is making intrusion dete…
Intrusion DetectionmodelNetwork Intrusion DetectionTANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack
Network intrusion attacks are a known threat. To detect such attacks, network intrusion detection systems (NIDSs) have been developed and deployed. These systems apply machine learning models to high-dimensional vectors …
Intrusion DetectionNetwork Intrusion Detection