A real-time and ubiquitous network attack detection based on deep belief network and support vector machine

2020 ◽  
Vol 7 (3) ◽  
pp. 790-799 ◽  
Author(s):  
Hao Zhang ◽  
Yongdan Li ◽  
Zhihan Lv ◽  
Arun Kumar Sangaiah ◽  
Tao Huang
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Attack Detection ◽  
Deep Belief Network ◽  
Support Vector ◽  
Network Attack ◽  
Belief Network ◽  
Ubiquitous Network

scholarly journals Fast outlier detection for high-dimensional data of wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096383
Author(s):  
Yan Qiao ◽  
Xinhong Cui ◽  
Peng Jin ◽  
Wu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Belief Network

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

scholarly journals Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B

2021 ◽  
Vol 5 (2) ◽  
pp. 27
Author(s):  
Dustin M. Mink ◽  
Jeffrey McDonald ◽  
Sikha Bagui ◽  
William B. Glisson ◽  
Jordan Shropshire ◽  
...  
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Network Attack ◽  
Ground Station ◽  
Traffic Systems ◽  
Attack Mitigation ◽  
The U.S

Modern-day aircraft are flying computer networks, vulnerable to ground station flooding, ghost aircraft injection or flooding, aircraft disappearance, virtual trajectory modifications or false alarm attacks, and aircraft spoofing. This work lays out a data mining process, in the context of big data, to determine flight patterns, including patterns for possible attacks, in the U.S. National Air Space (NAS). Flights outside the flight patterns are possible attacks. For this study, OpenSky was used as the data source of Automatic Dependent Surveillance-Broadcast (ADS-B) messages, NiFi was used for data management, Elasticsearch was used as the log analyzer, Kibana was used to visualize the data for feature selection, and Support Vector Machine (SVM) was used for classification. This research provides a solution for attack mitigation by packaging a machine learning algorithm, SVM, into an intrusion detection system and calculating the feasibility of processing US ADS-B messages in near real time. Results of this work show that ADS-B network attacks can be detected using network attack signatures, and volume and velocity calculations show that ADS-B messages are processable at the scale of the U.S. Next Generation (NextGen) Air Traffic Systems using commodity hardware, facilitating real time attack detection. Precision and recall close to 80% were obtained using SVM.

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