scholarly journals A Deep Random Forest Model on Spark for Network Intrusion Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhenpeng Liu ◽  
Nan Su ◽  
Yiwen Qin ◽  
Jiahuan Lu ◽  
Xiaofei Li
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Detection Efficiency ◽  
Original Data ◽  
Learning Ability ◽  
Network Intrusion Detection ◽  
Complex Data ◽  
Cache Replacement ◽  
Network Intrusion

This paper focuses on an important research problem of cyberspace security. As an active defense technology, intrusion detection plays an important role in the field of network security. Traditional intrusion detection technologies have problems such as low accuracy, low detection efficiency, and time consuming. The shallow structure of machine learning has been unable to respond in time. To solve these problems, the deep learning-based method has been studied to improve intrusion detection. The advantage of deep learning is that it has a strong learning ability for features and can handle very complex data. Therefore, we propose a deep random forest-based network intrusion detection model. The first stage uses a slide window to segment original features into many small pieces and then trains a random forest to generate the concatenated class vector as rerepresentation. The vector will be used to train the multilevel cascade parallel random forest in the second stage. Finally, the classification of the original data is determined by voting strategy after the last layer of cascade. Meanwhile, the model is deployed in Spark environment and optimizes cache replacement strategy of RDDs by efficiency sorting and partition integrity check. The experiment results indicate that the proposed method can effectively detect anomaly network behaviors, with high F1-measure scores and high accuracy. The results also show that it can cut down the average execution time on different scaled clusters.

scholarly journals Network intrusion detection method based on deep learning

2021 ◽  
Vol 1966 (1) ◽  
pp. 012051
Author(s):  
Shuai Zou ◽  
Fangwei Zhong ◽  
Bing Han ◽  
Hao Sun ◽  
Tao Qian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Method ◽  
Network Intrusion Detection ◽  
Network Intrusion

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

Power information network intrusion detection based on deep learning and cloud computing

2021 ◽  
pp. 1-9
Author(s):  
Xiangbing Zhao ◽  
Jianhui Zhou
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Intrusion Detection ◽  
High Speed ◽  
Information Networks ◽  
Network Intrusion Detection ◽  
Information Network ◽  
Advantages And Disadvantages ◽  
Network Intrusion ◽  
Capture Mechanism

With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.

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