Investigating network traffic through compressed graph visualization: VAST 2012 Mini Challenge 2 award: “Good adaptation of graph analysis techniques”

2012 ◽  
Author(s):  
Lei Shi ◽  
Qi Liao ◽  
Chunxin Yang
Keyword(s):  
Network Traffic ◽  
Graph Analysis ◽  
Graph Visualization ◽  
Analysis Techniques

scholarly journals Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data

AI Magazine ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 33-46 ◽  
Author(s):  
Juan Liu ◽  
Eric Bier ◽  
Aaron Wilson ◽  
John Alexis Guerra-Gomez ◽  
Tomonori Honda ◽  
...  
Keyword(s):  
Network Structure ◽  
Heterogeneous Network ◽  
Graph Analysis ◽  
Challenging Problem ◽  
Analysis Techniques ◽  
Healthcare Data ◽  
On Demand ◽  
Potential Value ◽  
Changes Over Time ◽  
Over Time

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the Network Explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand. The system has been deployed on multiple sites and datasets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month.

scholarly journals A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Eva Papadogiannaki ◽  
Sotiris Ioannidis
Keyword(s):  
Network Traffic ◽  
State Of The Art ◽  
Traffic Analysis ◽  
Research Community ◽  
The Internet ◽  
Secure Communications ◽  
User Privacy ◽  
Analysis Techniques ◽  
Network Traffic Analysis ◽  
Wide Domain

The adoption of network traffic encryption is continually growing. Popular applications use encryption protocols to secure communications and protect the privacy of users. In addition, a large portion of malware is spread through the network traffic taking advantage of encryption protocols to hide its presence and activity. Entering into the era of completely encrypted communications over the Internet, we must rapidly start reviewing the state-of-the-art in the wide domain of network traffic analysis and inspection, to conclude if traditional traffic processing systems will be able to seamlessly adapt to the upcoming full adoption of network encryption. In this survey, we examine the literature that deals with network traffic analysis and inspection after the ascent of encryption in communication channels. We notice that the research community has already started proposing solutions on how to perform inspection even when the network traffic is encrypted and we demonstrate and review these works. In addition, we present the techniques and methods that these works use and their limitations. Finally, we examine the countermeasures that have been proposed in the literature in order to circumvent traffic analysis techniques that aim to harm user privacy.

scholarly journals Upaya Meningkatkan Keterampilan Membedakan Toilet Pria dan Toilet Wanita Melalui Media Kartu Gambar di SLB Amanah Bunda Sitapung Ampek Angkek

2020 ◽  
Vol 5 (1) ◽  
pp. 23-31
Author(s):  
Gina Hapsari ◽  
Kasiyati Kasiyati
Keyword(s):  
Research Method ◽  
Baseline Condition ◽  
Single Subject ◽  
Graph Analysis ◽  
Male And Female ◽  
Analysis Techniques ◽  
Single Subject Research ◽  
Intervention Condition ◽  
Condition B ◽  
Visual Graph

Penelitian ini bertujuan untuk meningkatkan keterampilan anak dalam membedakan toilet laki-laki dan toilet perempuan yang  dilakukan melalui media kartu gambar. Penelitian dilaksanakan selama 13 kali. Metode yang digunakan penelitian Single Subject Research (SSR) bentuk A-B-A. Kondisi baseline (A1) dilakukan empat kali. Kondisi intervensi (B) merupakan kondisi setelah diberikan perlakuan dilakukan sebanyak enam kali. Sedangkan kondisi baseline (A2) merupakan pemberhentian perlakuan dilakukan sebanyak tiga kali pengamatan. Analisis grafik visual sangat diperlukan salam teknik analisis data. Persentase overlap diperoleh pada kondisi A1-B yaitu 0% sedangkan persentase overlap yang diperoleh pada kondisi A2-B yaitu 50%. Berdasarkan hasil penelitian ini maka dalam meningkatkan keterampilan untuk membedakan toilet laki-laki dan toilet perempuan efektif menggunakan media kartu gambar. This study aims to improve children's skills in distinguishing male toilets and female toilets which are carried out through self-drawing cards. This research was conducted 13 times observation. This research method uses a Single Subject Research (SSR) type of study with A-B-A design. Baseline condition (A1) is the initial capability before it is given as many as four observations. The intervention condition (B) is a condition given after six examinations have been carried out. While the baseline condition (A2) is a dismissal carried out three times. Data analysis techniques using visual graph analysis. The percentage of overlap obtained at condition A1 / B is 0% while the percentage of overlap obtained at condition A2 / B is 50%. Based on the results of this study, the image card media is effective in improving the skills of calculating male and female toilets

scholarly journals Finding a Needle in a Haystack: The Traffic Analysis Version

2019 ◽  
Vol 2019 (2) ◽  
pp. 270-290
Author(s):  
Abdullah Qasem ◽  
Sami Zhioua ◽  
Karima Makhlouf
Keyword(s):  
Network Traffic ◽  
Traffic Analysis ◽  
Machine Learning Algorithms ◽  
Bloom Filters ◽  
Sensitive Information ◽  
Traffic Splitting ◽  
Analysis Techniques ◽  
Network Traffic Analysis ◽  
Signature Generation ◽  
Single Size

Abstract Traffic analysis is the process of extracting useful/sensitive information from observed network traffic. Typical use cases include malware detection and website fingerprinting attacks. High accuracy traffic analysis techniques use machine learning algorithms (e.g. SVM, kNN) and require to split the traffic into correctly separated blocks. Inspired by digital forensics techniques, we propose a new network traffic analysis approach based on similarity digest. The approach features several advantages compared to existing techniques, namely, fast signature generation, compact signature representation using Bloom filters, efficient similarity detection between packet traces of arbitrary sizes, and in particular dropping the traffic splitting requirement altogether. Experimental results show very promising results on VPN and malware traffic, but low results on Tor traffic due mainly to the single-size cells feature.

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