scholarly journals Obfuscated Tor Traffic Identification Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenliang Xu ◽  
Futai Zou
Keyword(s):  
Arrival Time ◽  
Sliding Window ◽  
Recall Rate ◽  
Machine Learning Algorithms ◽  
Time Interval ◽  
Traffic Identification ◽  
Packet Arrival ◽  
Packet Length ◽  
Precision Rate ◽  
The Web

Tor is an anonymous communication network used to hide the identities of both parties in communication. Apart from those who want to browse the web anonymously using Tor for a benign purpose, criminals can use Tor for criminal activities. It is recognized that Tor is easily intercepted by the censorship mechanism, so it uses a series of obfuscation mechanisms to avoid censorship, such as Meek, Format-Transforming Encryption (FTE), and Obfs4. In order to detect Tor traffic, we collect three kinds of obfuscated Tor traffic and then use a sliding window to extract 12 features from the stream according to the five-tuple, including the packet length, packet arrival time interval, and the proportion of the number of bytes sent and received. And finally, we use XGBoost, Random Forest, and other machine learning algorithms to identify obfuscated Tor traffic and its types. Our work provides a feasible method for countering obfuscated Tor network, which can identify the three kinds of obfuscated Tor traffic and achieve about 99% precision rate and recall rate.

A Method of Web Information Extraction Based on Building Different Sub Trees

2013 ◽  
Vol 694-697 ◽  
pp. 2513-2521
Author(s):  
Yuan Long Wang ◽  
Hong Jiang ◽  
Zhao Hong Bing ◽  
Li Zhang
Keyword(s):  
Recall Rate ◽  
Automatic Extraction ◽  
Web Page ◽  
Web Information Extraction ◽  
Web Information ◽  
Data Source ◽  
Dom Tree ◽  
Text Content ◽  
Precision Rate ◽  
The Web

When extracting Web information, most researchers mixed the structure labels of DOM Tree with the text content. For solving this problem, we put forward a method of Web Information automatic extraction. Firstly, we get the set of DOM sub trees by partitioning the DOM Tree of the Web Page. Secondly, the nodes of all DOM sub trees are set the corresponding weights by the method this paper proposes. Based on this method, we get each set of different sub trees by comparing with the DOM sub trees which come from two the same data source and belongs to the same category. Thirdly, we get the data zone which contains the extracted information by computing the similarity of every two DOM sub trees in the set of different sub trees. Finally, the node path of every DOM sub tree in the data zone will be taken as the extraction rules which will be used to automatically extract the information from the new Web page of the same category. The experiment demonstrates that there are higher precision rate and recall rate. Meanwhile this method can save the time which the users spend on filtering the information.

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

scholarly journals Exploring high reliable substructures in auto-reconstructions of a neuron

2021 ◽  
Author(s):  
Yishan He ◽  
Jiajin Huang ◽  
Gaowei Wu ◽  
Jian Yang
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Neuron Tracing ◽  
Digital Reconstruction ◽  
High Recall Rate ◽  
Multiple Species ◽  
Multiple Reference ◽  
Precision Rate

Abstract The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron’s reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

A Method for Controlling Arrival Time to Prevent Late Arrival by Manipulating Vehicle Timetable Information

2020 ◽  
Vol 1 (1) ◽  
pp. 1-17
Author(s):  
Kyosuke Futami ◽  
Tsutomu Terada ◽  
Masahiko Tsukamoto
Keyword(s):  
Public Transportation ◽  
Psychological Factors ◽  
Arrival Time ◽  
Time Interval ◽  
Prototype System ◽  
Time Intervals ◽  
Target Vehicle ◽  
Late Arrival ◽  
Departure Time ◽  
The Relationship

Although it is socially and ethically important not to be late for a specified arrival time, late arrivals sometimes happen to people using public transportation. Although many methods aim to smooth a user's movement by providing useful information, there are few approaches to prevent late arrivals due to psychological factors. In this research, to make a user's arrival time earlier and thus prevent late arrival, we propose a method that manipulates time allowance by presenting information based on a psychological and cognitive tendency. We apply this method to a vehicle timetable system for the purpose of preventing public transit users from arriving after a target vehicle's departure time. Our proposed timetable system manipulates the time intervals between a user's target vehicle and other vehicles by introducing fictional elements such as hidden vehicles and inserted fictional vehicles. This method uses the relationship between the time allowance and the departure time interval, and it can make a user desire and accept arriving at a station earlier. We implemented a prototype system and conducted four experiments. The evaluation results confirmed that our proposed method is effective for changing a user's time allowance and actual arrival time.

Characteristics Analysis of the Photons Arrival Time Interval of Laser Beat Signal

2012 ◽  
Vol 32 (4) ◽  
pp. 0403001 ◽  
Author(s):  
刘立生 Liu Lisheng ◽  
张合勇 Zhang Heyong ◽  
赵帅 Zhao Shuai ◽  
郭劲 Guo Jin
Keyword(s):  
Arrival Time ◽  
Beat Signal ◽  
Time Interval ◽  
Characteristics Analysis

scholarly journals Method for Retrieving Digital Agricultural Text Information Based on Local Matching

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1103
Author(s):  
Yue Song ◽  
Minjuan Wang ◽  
Wanlin Gao
Keyword(s):  
Recall Rate ◽  
Retrieval Method ◽  
Retrieval Time ◽  
Search Results ◽  
Retrieval Efficiency ◽  
Query Tree ◽  
Text Information ◽  
Relationship Of ◽  
The Relationship ◽  
Precision Rate

In order to improve the retrieval results of digital agricultural text information and improve the efficiency of retrieval, the method for searching digital agricultural text information based on local matching is proposed. The agricultural text tree and the query tree are constructed to generate the relationship of ancestor–descendant in the query and map it to the agricultural text. According to the retrieval method of the local matching, the vector retrieval method is used to calculate the digital agricultural text and submit the similarity between the queries. The similarity is sorted from large to small so that the agricultural text tree can output digital agricultural text information in turn. In the case of adding interference information, the recall rate and precision rate of the proposed method are above 99.5%; the average retrieval time is between 4s and 6s, and the average retrieval efficiency is above 99%. The proposed method is more efficient in information retrieval and can obtain comprehensive and accurate search results, which can be used for the rapid retrieval of digital agricultural text information.

scholarly journals Approximating the Temporal Neighbourhood Function of Large Temporal Graphs

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 211 ◽  
Author(s):  
Pierluigi Crescenzi ◽  
Clémence Magnien ◽  
Andrea Marino
Keyword(s):  
Public Transportation ◽  
Arrival Time ◽  
Time Interval ◽  
Temporal Network ◽  
Temporal Networks ◽  
Starting Time ◽  
Neighborhood Function ◽  
Temporal Graphs ◽  
Temporal Path

Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.

scholarly journals An Encrypted Traffic Identification Scheme Based on the Multilevel Structure and Variational Automatic Encoder

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiangtao Zhai ◽  
Huaifeng Shi ◽  
Mingqian Wang ◽  
Zhongjun Sun ◽  
Junjun Xing
Keyword(s):  
Monte Carlo ◽  
Recognition Accuracy ◽  
Recall Rate ◽  
Random Forest Classifier ◽  
Optimal Result ◽  
Identification Scheme ◽  
Traffic Identification ◽  
Multilevel Structure ◽  
C4.5 Decision Tree ◽  
Encrypted Traffic

With the rapid growth of the encrypted network traffic, the identification to it becomes a hot topic in information security. Since the existing methods have difficulties in identifying the application which the encrypted traffic belongs to, a new encrypted traffic identification scheme is proposed in this paper. The proposed scheme has two levels. In the first level, the entropy and estimation of Monte Carlo π value as features are used to identify the encrypted traffic by C4.5 decision tree. In the second level, the application types are distinguished from the encrypted traffic selected above. First, the variational automatic encoder is used to extract the layer features, which is combined with the frequently-used stream features. Meanwhile, the mutual information is used to reduce the dimensionality of the combination features. Finally, the random forest classifier is used to obtain the optimal result. Compared with the existing methods, the experimental results show that the proposed scheme not only has faster convergence speed but also achieves better performance in the recognition accuracy, recall rate, and F1-Measure, which is higher than 97%.

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