scholarly journals Short-term prediction of traffic state, statistical approach versus machine learning approach

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Arash Rasaizadi ◽  
Elahe Sherafat ◽  
Seyedehsan Seyedabrishami
Keyword(s):  
Machine Learning ◽  
Statistical Approach ◽  
Learning Approach ◽  
Short Term ◽  
Traffic State ◽  
Term Prediction ◽  
Machine Learning Approach ◽  
Short Term Prediction

scholarly journals Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Arash Rasaizadi ◽  
Seyedehsan Seyedabrishami ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Road Traffic ◽  
Support Vector ◽  
Paper Machine ◽  
Short Term ◽  
Traffic State ◽  
Rural Road ◽  
Term Prediction ◽  
Short Term Prediction

Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.

A Deep Learning Approach for Short Term Prediction of Industrial Plant Working Status

2021 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Daniele Cenni ◽  
Luciano Alessandro Ipsaro Palesi ◽  
Paolo Nesi ◽  
Gianni Pantaleo
Keyword(s):  
Deep Learning ◽  
Industrial Plant ◽  
Learning Approach ◽  
Short Term ◽  
Term Prediction ◽  
Working Status ◽  
Short Term Prediction

scholarly journals Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

2021 ◽  
Author(s):  
Merlin James Rukshan Dennis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Statistical Approach ◽  
Denial Of Service ◽  
Attack Detection ◽  
Learning Approach ◽  
Ddos Attack ◽  
Machine Learning Approach ◽  
Ddos Detection ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

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