Virtual Private Network Flow Detection in Wireless Sensor Networks using Machine Learning Techniques

2021 ◽  
Vol 10 ◽  
Author(s):  
S. Phani Praveen ◽  
T. Bala Murali Krishna ◽  
Sunil K. Chawla ◽  
CH Anuradha
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Network Flow ◽  
Virtual Private Network ◽  
Machine Learning Techniques ◽  
Protocol Security ◽  
Learning Techniques ◽  
Private Network ◽  
Hyper Text Transfer Protocol ◽  
Transfer Protocol

Background: Every organization generally uses a VPN service individually to leather the actual communication. Such communication is actually not allowed by organization monitoring network. But these institutes are not in a position to spend huge amount of funds on secure sockets layer to monitor traffic over their computer networks. Objective: Our work suggests simple technique to block or detect annoying VPN clients inside the network activities. This method does not requires the network to decrypt or even decode any network communication. Method: The proposed solution selects two machine learning techniques Feature Tree and K-means as classifiction techniques which work on time related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / internet protocol computer network stack is identified and it is not to be considered as a normal traiffic flow if the domain name information is not available. The process also examines non-standard utilization of hyper text transfer protocol security and also conceal such communication from hyper text transfer protocol security dependent filters in firewall to detect as anomaly in largely. Results: we define the trafic flow as normal trafic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques Feature Tree and K-means. We have executed each experment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented. Conclusion: Once trafic flow is identified, it is classified and studied by machine learning techniques. Using time related features, the traffic flow is defined as normal flow or VPN traffic flow.

scholarly journals Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5213 ◽  
Author(s):  
Donato Impedovo ◽  
Fabrizio Balducci ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Machine Learning Techniques ◽  
Visual Features ◽  
Learning Approaches ◽  
Traffic State ◽  
State Classification ◽  
Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

scholarly journals Apply machine learning techniques to detect malicious network traffic in cloud computing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Amirah Alshammari ◽  
Abdulaziz Aldribi
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Network Traffic ◽  
Detection Efficiency ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Detection Model ◽  
Learning Techniques ◽  
Detection Quality ◽  
Future Work

AbstractComputer networks target several kinds of attacks every hour and day; they evolved to make significant risks. They pass new attacks and trends; these attacks target every open port available on the network. Several tools are designed for this purpose, such as mapping networks and vulnerabilities scanning. Recently, machine learning (ML) is a widespread technique offered to feed the Intrusion Detection System (IDS) to detect malicious network traffic. The core of ML models’ detection efficiency relies on the dataset’s quality to train the model. This research proposes a detection framework with an ML model for feeding IDS to detect network traffic anomalies. This detection model uses a dataset constructed from malicious and normal traffic. This research’s significant challenges are the extracted features used to train the ML model about various attacks to distinguish whether it is an anomaly or regular traffic. The dataset ISOT-CID network traffic part uses for the training ML model. We added some significant column features, and we approved that feature supports the ML model in the training phase. The ISOT-CID dataset traffic part contains two types of features, the first extracted from network traffic flow, and the others computed in specific interval time. We also presented a novel column feature added to the dataset and approved that it increases the detection quality. This feature is depending on the rambling packet payload length in the traffic flow. Our presented results and experiment produced by this research are significant and encourage other researchers and us to expand the work as future work.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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