Ramesh Paudel

Ramesh Paudel
George Washington University | GW · Department of Electrical & Computer Engineering

Doctor of Philosophy
Research Scientist | George Washington University

About

11
Publications
6,107
Reads
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50
Citations
Introduction
Research Scientist at The George Washington University
Additional affiliations
May 2018 - August 2018
Tennessee Technological University
Position
  • Instructor
Description
  • Teach Discrete Structure for Computer Science (CSC 2700)
Education
August 2016 - December 2019
Tennessee Technological University
Field of study
  • Computer Science
August 2012 - May 2014
Tennessee Technological University
Field of study
  • Computer Science

Publications

Publications (11)
Conference Paper
Full-text available
Enterprise networks evolve constantly over time. In addition to the network topology, the order of information flow is crucial to detect cyber-threats in a constantly evolving network. Majority of the existing technique uses static snapshot to learn from dynamic network. However, using static snapshots is not sufficient as it largely ignores highly...
Article
Network protocol analyzers such asWireshark are valuable for analyzing network traffic but pose a challenge in that it can be difficult to determine which behaviors are out of the ordinary due to the volume of data that must be analyzed. Network anomaly detection systems can provide vital insights to security analysts to supplement protocol analyze...
Article
Full-text available
The emergence of mining complex networks like social media, sensor networks, and the worldwide web has attracted considerable research interest. In a streaming scenario, the concept to be learned can change over time. However, while there has been some research done for detecting concept drift in traditional data streams, little work has been done...
Conference Paper
Full-text available
In this paper, we propose a novel unsupervised graph representation approach in a graph stream called SNAPSKETCH that can be used for anomaly detection. It first performs a fixed-length random walk from each node in a network and constructs n-shingles from a walk path. The top discriminative n-shingles identified using a frequency measure are proje...
Poster
Full-text available
Anomaly detection in a network/graph can be accomplished using standard machine learning algorithms. However, the primary challenge is in the extraction of informative, discriminating, and independent features that can represent or encode a graph structure so that it be used as input by a machine learning model. In this work, an unsupervised graph...
Chapter
Full-text available
In recent years, social media has changed the way people communicate and share information. For example, when some important and noteworthy event occurs, many people like to “tweet” (Twitter) or post information, resulting in the event trending and becoming more popular. Unfortunately, spammers can exploit trending topics to spread spam more quickl...
Conference Paper
Full-text available
The use of the Internet of Things (IoT) devices has surged in recent years. However, due to the lack of substantial security , IoT devices are vulnerable to cyber-attacks like Denial-of-Service (DoS) attacks. Most of the current security solutions are either computationally expensive or unscalable as they require known attack signatures or full pac...
Conference Paper
Full-text available
A denial-of-service (DoS) attack is a malicious act with the goal of interrupting the access to a computer network. The result of a DoS attack can cause the computers on the network to squander their resources to serve illegitimate requests that result in a disruption of the network's services to legitimate users. With a sophisticated DoS attack, i...
Conference Paper
Full-text available
Sensor-based smart home provide the ability to track resident activities without interfering in their daily routine. It is useful to detect and predict the behaviors of anelderly resident in order to improve the safety of the residents’ home environment andprovide aid for their caregiver. This paper presents a graph-based approach that successfully...
Conference Paper
Full-text available
The percentage of people living over 65 years has in- creased steadily over the last few decades, and with it is coming a rapid increase in cognitive health issues among the baby boomers. In order to address the issue of caring for this particular aging population, intelligent solutions need to be provided. It is our hypothesis that through the app...
Conference Paper
Full-text available
Every year, billions of dollars are lost due to fraud in the U.S. health care system. Health care claims are complex as they involve multiple parties including service providers, insurance subscribers, and insurance carriers. Medicare is susceptible to fraud because of this complexity. To build a comprehensive fraud detection system, one must take...

Questions

Question (1)
Question
Is there any pre-trained API or model on MNIST handwritten digit (with noises )that we can use for detecting the new handwritting test data?
We need trained model on MNIST dataset with agumented noise so that the model is robust for new handwritting test data

Network

Cited By

Projects

Projects (3)
Project
With the growing senior citizen population, it is imperative to detect and try to predict these kinds of behaviours because it can improve the quality and safety of the residents home environment as well as provide aid and well-being for their caregiver.
Archived project
This research is focus on discovering normal patterns and anomalies in graph streams. The data for this research represents the collection of two heterogeneous data sources that have a connection/relationship. One data set is a collection of news feeds, gathered from News API (https://newsapi.org). The other data set is a collection of tweets, captured using the Twitter REST API (https://dev.twitter.com/rest/public), that are associated with a particular news story.
Project
Every year, billions of dollars are lost due to fraud in the U.S. health care system. Health care claims are complex as they involve multiple parties including service providers, insurance subscribers, and insurance carriers. Medicare is susceptible to fraud because of this complexity. To build a comprehensive fraud detection system, one must take into consideration all of the financial practices involved among the associated parties. Our research focus on graph-based analysis of CMS provided Medicare claims data to look for anomalies in the relationships and transactions among patients, service providers, claims, physicians, diagnosis, procedures, drug prescribed and payment received. In our experiments, we create separate graphs for inpatient, outpatient, and carrier claims data of the beneficiary. Also we create graphs for prescription drug prescribed by physicians and payment he/she received under the Medicare part D prescription drug program. We then demonstrate the potential effectiveness of applying graph-based anomaly detection to the problem of discovering anomalies and potential fraud scenarios.