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Erin Taylor, Ian Frankenburg, and Eric Peshkin worked with client Dr. John Harer of Geometric Data Analytics to detect abnormal behavior in communications networks. They built models representing networks as nodes for IP addresses and edges for communications between them. Topological and statistical tools were used to study changes in network connectivity and fit probability distributions to the data to identify anomalous events. When comparing test networks to training networks, some nodes in the test networks showed unusual communication patterns. The team identified many potentially anomalous communications and believe further analysis of more comprehensive data sets could yield interesting findings, especially at the network level.
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This document presents a critical review report on a course about algorithm design and analysis. It summarizes a paper that studied graph-based forecasting techniques for social networks. The paper generated a social graph from user activity data and used rule-based mining to predict future social behaviors. It found that this method was more efficient than the Apriori algorithm as it did not require multiple scans of the dataset. The real-world simulation achieved accuracy rates of over 80% for predictions made over 10 days. However, the review critiques that the paper's title was too broad and its impact was limited by only being cited 3 times in the last 7 years. Overall, the paper presented a good recommender system for social networks.
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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Email is one of the most popular communication media in the current century; it has become an effective
and fast method to share and information exchangeall over the world. In recent years, emails users are
facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It
consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for
spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most
powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of
dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is
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message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and
Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO
algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of
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Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within
each iterative process of PSO depending the fitness (error) function. The experiments are conducted on
spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental
results show that our approach is performed in accuracy compared with other approaches that use the
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The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
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3) However, the diameter and average path length are often lowest on weekends, suggesting information spreads more quickly at those times due to the network structure.
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Email is one of the most popular communication media in the current century; it has become an effective
and fast method to share and information exchangeall over the world. In recent years, emails users are
facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It
consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for
spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most
powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of
dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is
the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam
message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and
Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO
algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of
PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest
Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within
each iterative process of PSO depending the fitness (error) function. The experiments are conducted on
spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental
results show that our approach is performed in accuracy compared with other approaches that use the
same dataset.
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The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
Final Poster for Engineering Showcase
This document summarizes a study analyzing social media influence and credibility. A team of students and professors extracted different types of data from Twitter, including mentions of users, tweets by authorities, and keywords. They developed a semantic parser to analyze tweet content using ontological semantic technology. An initial linear score was formulated to measure user influence, and network analysis identified pivotal users between communities. Validation will compare content analysis to real-world events to supplement credibility assessment. The study has potential applications in public policy, business, psychology, and traffic monitoring.
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This document discusses a methodology for monitoring internet traffic and detecting anomalous behavior. It begins by noting the challenges of understanding vast quantities of internet traffic data due to the diversity of applications and services. Recent cyber attacks have made it important to develop techniques to analyze communication patterns in traffic data for network security purposes.
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This document summarizes an analytical model for localized peer-to-peer (P2P) streaming systems that considers the impact of network address translation (NAT). It introduces theoretical boundaries for the number of peers that may be expelled from the system due to NAT incompatibility. It also presents a mathematical model for startup delay in P2P streaming that accounts for peers' NAT types. The document proposes a new neighbor selection algorithm that considers both autonomous system numbers and NAT types to improve connectivity while reducing transit traffic and startup delays.
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Poster Presented at XXXVII Sunbelt Conference
of The International Network For Social Network Analysis (INSNA)
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Network Forensics is fairly a new area of research which would be used after an intrusion in various
organizations ranging from small, mid-size private companies and government corporations to the defence
secretariat of a country. At the point of an investigation valuable information may be mishandled which
leads to difficulties in the examination and time wastage. Additionally the intruder could obliterate tracks
such as intrusion entry, vulnerabilities used in an entry, destruction caused, and most importantly the
identity of the intruder. The aim of this research was to map the correlation between network security and
network forensic mechanisms. There are three sub research questions that had been studied. Those have
identified Network Security issues, Network Forensic investigations used in an incident, and the use of
network forensics mechanisms to eliminate network security issues. Literature review has been the
research strategy used in order study the sub research questions discussed. Literature such as research
papers published in Journals, PhD Theses, ISO standards, and other official research papers have been
evaluated and have been the base of this research. The deliverables or the output of this research was
produced as a report on how network forensics has assisted in aligning network security in case of an
intrusion. This research has not been specific to an organization but has given a general overview about
the industry. Embedding Digital Forensics Framework, Network Forensic Development Life Cycle, and
Enhanced Network Forensic Cycle could be used to develop a secure network. Through the mentioned
framework, and cycles the author has recommended implementing the 4R Strategy (Resistance,
Recognition, Recovery, Redress) with the assistance of a number of tools. This research would be of
interest to Network Administrators, Network Managers, Network Security personnel, and other personnel
interested in obtaining knowledge in securing communication devices/infrastructure. This research
provides a framework that can be used in an organization to eliminate digital anomalies through network
forensics, helps the above mentioned persons to prepare infrastructure readiness for threats and also
enables further research to be carried on in the fields of computer, database, mobile, video, and audio.
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Social applications implemented on a peer-to-peer (P2P) architecture mine the social graph of their users for improved performance in search, recommendations, resource
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In this work we introduce the model of a projection graph that is the result of mapping a social graph onto a peer-to-peer network. We analytically formulate the relation between metrics in the social graph and in the projection graph. We focus on three such graph metrics: degree centrality, node betweenness centrality, and edge betweenness centrality. We evaluate experimentally the feasibility of estimating these metrics in the projection graph from the metrics of the social graph. Our experiments on real networks show that when mapping communities of 50-150 users on a peer, there is an optimal organization of the projection graph with respect to degree and node betweenness centrality. In this range, the association between the properties of the social graph and the projection graph is the highest, and thus the properties of the (dynamic) projection graph can be inferred from
the properties of the (slower changing) social graph. We discuss the applicability of our findings to aspects of peer-to-peer systems such as data dissemination, social search, peer vulnerability, and data placement and caching.
Inferring Peer Centrality in Socially-Informed Peer-to-Peer Systems. Nicolas Kourtellis and Adriana Iamnitchi. In Proceedings of 11th IEEE International Conference on Peer-to-Peer Computing (P2P'11), Kyoto, Japan, Aug 2011
On Using Network Science in Mining Developers Collaboration in Software Engin...
Background: Network science is the set of mathematical frameworks, models, and measures that are used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network represent entities and the edges represent relationships between these entities. Network science has been used in many research works for mining human interaction during different phases of software engineering (SE). Objective: The goal of this study is to identify, review, and analyze the published research works that used network analysis as a tool for understanding the human collaboration on different levels of software development. This study and its findings are expected to be of benefit for software engineering practitioners and researchers who are mining software repositories using tools from network science field. Method: We conducted a systematic literature review, in which we analyzed a number of selected papers from different digital libraries based on inclusion and exclusion criteria. Results: We identified 35 primary studies (PSs) from four digital libraries, then we extracted data from each PS according to a predefined data extraction sheet. The results of our data analysis showed that not all of the constructed networks used in the PSs were valid as the edges of these networks did not reflect a real relationship between the entities of the network. Additionally, the used measures in the PSs were in many cases not suitable for the used networks. Also, the reported analysis results by the PSs were not, in most cases, validated using any statistical model. Finally, many of the PSs did not provide lessons or guidelines for software practitioners that can improve the software engineering practices. Conclusion: Although employing network analysis in mining developers’ collaboration showed some satisfactory results in some of the PSs, the application of network analysis needs to be conducted more carefully. That is said, the constructed network should be representative and meaningful, the used measure needs to be suitable for the context, and the validation of the results should be considered. More and above, we state some research gaps, in which network science can be applied, with some pointers to recent advances that can be used to mine collaboration networks.
Dataset-driven research to improve TEL recommender systems
This document discusses the dataTEL theme team, which aims to improve technology-enhanced learning (TEL) recommender systems through dataset-driven research. The dataTEL team addresses challenges related to connecting learners and contextualization. Its objectives are to develop policies and methods for sharing, preprocessing, evaluating, and monitoring the performance of TEL recommender systems using different datasets. The goal is to standardize research on TEL recommender systems.
Big Data Analytics and Advanced Computer Networking Scenarios
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The document discusses sampling techniques for online social networks. It proposes using an outlier indexing algorithm to sample large datasets from social networks. The key advantages of this approach are that random samples can be used for a wide range of analytical tasks and outlier detection. The paper also reviews related literature on estimating search tree sizes and sampling nodes in social networks. It then presents the proposed outlier indexing sampling algorithm for compressing social network structure and interest correlations across users.
Ppt manqing
This document summarizes a research paper on robust unsupervised feature selection on networked data. It introduces the challenges of high dimensionality and noise in networked data. The proposed NetFS framework addresses this by (1) modeling link information with latent representations learned from the network structure, and (2) embedding latent representation learning into the feature selection process to reduce noise. The framework is optimized using an alternating optimization approach. Experiments on blog, Flickr, and Epinions networks demonstrate that NetFS improves clustering performance over other methods by selecting more informative features. Future work could apply the framework to other network types and dynamic networks.
NS-CUK Seminar: H.B.Kim, Review on "metapath2vec: Scalable representation le...
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On Using Network Science in Mining Developers Collaboration in Software Engin...
Background: Network science is the set of mathematical frameworks, models, and measures that are
used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network
represent entities and the edges represent relationships between these entities. Network science has been used in
many research works for mining human interaction during different phases of software engineering (SE)
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Geom_ExecSummSlides
- 1. Erin Taylor Ian Frankenburg Eric Peshkin Mentor: Joe Marion Client: Dr. John Harer, Geometric Data Analytics Geometry and Topology for Data Problem: ● Detect abnormal behaviour in communications networks using principles of statistics, topological data analysis, and machine learning. ● Capture both local and global anomalies using a comprehensive combination of mathematical methods. Computer Network Data Features Anomaly Detection Algorithms Leveraging topological and statistical tools to uncover structure in data
- 2. Methods We built a network with nodes representing different IP addresses and edges representing communications between them. Topological tools allowed us to study how the connectivity within the network changed as edges were added between nodes. We automated the process of fitting a probability distribution to our data. Our utility fit many potential probability distributions and used AIC to determine the best one. This gave us a probabilistic basis for separating improbable (potentially anomalous) events from typical events.
- 3. Results When compared with the training network, communication within the testing network appears more sparse, and some nodes exhibit unusual communication. Conclusion: We identified many potentially anomalous communications using both the topological and statistical methods. Our analysis indicates that further exploration on more comprehensive data sets could result in interesting findings, especially on the network level. Using a moving average of the frequency of communication between two nodes, we found many anomalous communications. Training Testing