scholarly journals Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 324 ◽  
Author(s):  
Chencheng Ma ◽  
Xuehui Du ◽  
Lifeng Cao
Keyword(s):  
Network Flow ◽  
Nearest Neighbor ◽  
Network Flows ◽  
Supervised Machine Learning ◽  
Traffic Classification ◽  
K Nearest Neighbor ◽  
Content Type ◽  
Fine Grained ◽  
Feature Weight

The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.

Research on Evaluation of Urban Road Congestion

2014 ◽  
Vol 536-537 ◽  
pp. 849-853
Author(s):  
Yi Dan Gao
Keyword(s):  
Nearest Neighbor ◽  
Recognition Rate ◽  
Supervised Machine Learning ◽  
Traffic Classification ◽  
K Nearest Neighbor ◽  
Traffic Identification ◽  
Research On Evaluation ◽  
Road Congestion ◽  
Network Traffic Classification ◽  
K Nearest Neighbor Algorithm

The samples marked bottlenecks and imbalanced protocol flows restrict the development of the network traffic classification technology, to solve this problem a semi-supervised machine learning traffic identification method is presented. Employ K-means algorithm to partition a training datasets that consists of a few labeled flows combined with abundant unlabeled flows.Then, identify the unlabeled samples using the labeled samples in the cluster based on k Nearest Neighbor algorithm. The theoretical analysis and experimental results show that the algorithm can improve the recognition rate of minority flows in the case of the imbalanced protocol flows.

scholarly journals Network Traffic Classification Model Based on Attention Mechanism and Spatiotemporal Features

2021 ◽  
Author(s):  
Feifei Hu ◽  
Situo Zhang ◽  
Xubin Lin ◽  
Liu Wu ◽  
Niandong Liao ◽  
...  
Keyword(s):  
Network Traffic ◽  
Network Flow ◽  
Network Flows ◽  
Attention Mechanism ◽  
Classification Model ◽  
Traffic Classification ◽  
Spatial Features ◽  
Network Traffic Classification ◽  
Encrypted Traffic

Abstract Traffic classification has been widely used in network security and network management. Previous research has focused on mapping network traffic to different non­-encrypted applications, However, there are few researches on network traffic classification of encryption applications, especially the underlying traffic of encryption application. In order to solve the above problems, this paper proposes a network encrypted traffic classification model which combines attention mechanism with spatial and temporal characteristics. The model first uses LSTM (Long Short­Term Memory) to analyze the time series of the continuous network flows and find out the time characteristics between the network flows. Secondly, CNN(Convolutional Neural Network) is used to extract the high­-order spatial features of the network flow, and then the high-­order spatial features are weighted and redistributed through the SE(Squeeze­ and­ Excitation)module to obtain the key spatial features of encrypted traffic. Finally, through the two-­stage training and learning , fast classification of network flow is achieved. The main advantages of this model are as follows: 1) the mapping relationship between network flow and corresponding labels is constructed end­-to­-end without manual extraction of network flow characteristics; 2)It has a powerful generalization ability which is able to be compatible with different types of data sets; 3) there is still a high recognition rate for encryption application and the underlying traffic of encryption application. The experimental results show that this model can be well qualified for the classification of non­-encrypted and encrypted application, moreover, greatly improves the classification accuracy of the underlying traffic of encryption application.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
Noor Azuan Abu Osman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Olympic Games ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
Machine Learning Approach ◽  
Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of  three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

scholarly journals KNN classifier based approach for multi-class sentiment analysis of twitter data

2018 ◽  
Vol 7 (3) ◽  
pp. 1372
Author(s):  
Soudamini Hota ◽  
Sudhir Pathak
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Online News ◽  
Classification Algorithm ◽  
Sentiment Classification ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
News Reports

‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters. 

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