Alias Detection Research Articles

6Tree: Efficient dynamic discovery of active addresses in the IPv6 address space

Active IPv6 address data could support research and applications for the next generation of the Internet, but finding ways to gather such data through active scanning and deal with emerging large-scale alias is a challenge. In this paper, we propose 6Tree, which analyzes known active addresses as seeds to learn their distribution feature and offers suggested search directions for scanners. It comprehends IPv6 addresses as high-dimensional vectors and performs a divisive hierarchical clustering (DHC) on corresponding vectors of seeds to generate a data structure, named a space tree, that characterizes value variabilities in different dimensions. Moreover, it can dynamically adjust proper directions based on real-time scanning results and embed alias detection into the search for the first time. Compared with the state-of-the-art method 6Gen, 6Tree has a faster linear time complexity to finish million-scale data training at a minute level for supporting a timely application, as well as better robustness for maintaining address discovery performance in context variations, including uneven seed sampling and workload division. According to aliased prefixes collected from a nascent research, 6Tree discovered approximately 4.69 million dealiased active addresses based on 2.74 million seeds, including 1.67 million aliased addresses, by scanning 0.3 billion addresses in one experiment. Additionally, 99.5% of detected aliased addresses are in the gathered aliased prefixes, and some undiscovered aliased prefixes were also found. We design the visualization technique Iris to visualize the address distribution based on discovery results and offer a novel perspective on the IPv6 deployment.

Design an Accurate Algorithm for Alias Detection

Design an Accurate Algorithm for Alias Detection

A Novel Fuzzy Logic Model to Identify Closeness for Alias Detection

The detection of person alias names are important for improving the accuracy of the information quality. The fuzzy-based decision system is proposed for alias detection, which is a rule-based system that uses fuzzy logic to make a decision about the closeness between the given name pairs. A fuzzy logic is formulated by a set of linguistic variables based on feature’s score value. An entity pair’s association score values are calculated using string and link-based features like Hamming Distance, Leventein Distance, Normalized String Edit Distance, Common Friends, Normalized Dot Product and Co-occurrence Relevance and an output variable accuracy as closeness. These features are transformed into fuzzy input variables and designed with proper membership functions. The proposed novel fuzzy inference system gives the decision of aliases closeness in the form of crisp values ranging from 0 to 1. In this work, the model achieves upto 90% accuracy compared to estimated accuracy.

An Improved Alias Classification using Logistic Regression with Particle Swarm Optimization

An improvement in detection of alias names of an entity is an important factor in many cases like terrorist and criminal network. In this paper, the social network properties are used to construct a feature set for classification. The proposed particle swarm optimization is used to optimize the regularization parameter of the logistic regression and improve the accuracy of the entity alias classification significantly to 4.98% compared to that of the logistic regression. The experimental results demonstrated its performance and the results are compared to the logistic regression with alias Detection Dataset from Auton Lab.

Framework for pattern generation from discriminating datasets

Searching for the exact name of a person on the web is a challenging task when a single name is shared by many people. The proposed method is based on alias detection to extract the lexical pattern, attributes of a person and candidate alias ranking. Initially we construct training semi-structured datasets which consist of alias name or nickname or real name; the profession and location names of a person are framed with the help of social media networks such as Wikipedia. Secondly, pattern generator includes pattern extraction algorithm and attributes extraction algorithm. Pattern extraction is used to generate lexical patterns manually with the use of dataset, and then generated patterns are passed to the Google search engine. According to the evaluation scheme the extracted lexical patterns are ranked. For ranking the candidate alias of a person, a graph mining ranking algorithm with various similarity measures is used. This paper presents the overview of the people search engine, definition, proposed model, training semi-structured dataset and evaluation scheme of manually created lexical patterns.

A Novel Framework for Alias Detection in Web Search Using Extreme Learning Machine (ELM) Approach

This is an era of search engines which have become powerful tools to retrieve information from the Web. It is the practice of using multiple names to mention the same entity that creates problem while collecting data. For example, Mohandas Karamchand Gandhi, Father of Nation, is also known as Gandhiji and with other pseudonyms. Synonyms (different names of an entity) can affect the relevance of a search engine. Extracting alias of an entity is significant for various tasks in the web such as automatic metadata extraction, entity disambiguation and social network analysis. In this paper, an alias detection framework is proposed to find alternate names of a given entity through the automatically downloaded web snippets using lexical patterns. The extracted alias names similarity scores are calculated using the string similarity measures and a novel method is introduced for detecting irrelevant alias names by ranking with the help of ELM. It is a learning algorithm for single hidden layer feed forwards neural networks having a generalization performance with a faster learning speed to train neural networks in a single iteration and its ranking performance is examined against Support Vector Machine (SVM). The ELM outperformed in terms of precision, recall and fscore as 17.28%, 90.70% and 0.34% for giving alias dataset better than SVM.

Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias Detection

Alias detection has been the significant subject being extensively studied for several domain applications, especially intelligence data analysis. Many preliminary methods rely on text-based measures, which are ineffective with false descriptions of terrorists' name, date-of-birth, and address. This barrier may be overcome through link information presented in relationships among objects of interests. Several numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, as a result of exceptional cases with unduly high measure, these methods usually generate inaccurate similarity descriptions. Yet, they are either computationally inefficient or ineffective for alias detection with a single-property based model. This paper presents a novel orders-of-magnitude based similarity measure that integrates multiple link properties to refine the estimation process and derive semantic-rich similarity descriptions. The approach is based on order-of-magnitude reasoning with which the theory of fuzzy set is blended to provide quantitative semantics of descriptors and their unambiguous mathematical manipulation. With such explanatory formalism, analysts can validate the generated results and partly resolve the problem of false positives. It also allows coherent interpretation and communication within a decision-making group, using this computing-with-word capability. Its performance is evaluated over a terrorism-related data set, with further generalization over publication and email data collections.

Automatic Discovery of Personal Name Aliases from the Web

An individual is typically referred by numerous name aliases on the web. Accurate identification of aliases of a given person name is useful in various web related tasks such as information retrieval, sentiment analysis, personal name disambiguation, and relation extraction. We propose a method to extract aliases of a given personal name from the web. Given a personal name, the proposed method first extracts a set of candidate aliases. Second, we rank the extracted candidates according to the likelihood of a candidate being a correct alias of the given name. We propose a novel, automatically extracted lexical pattern-based approach to efficiently extract a large set of candidate aliases from snippets retrieved from a web search engine. We define numerous ranking scores to evaluate candidate aliases using three approaches: lexical pattern frequency, word co-occurrences in an anchor text graph, and page counts on the web. To construct a robust alias detection system, we integrate the different ranking scores into a single ranking function using ranking support vector machines. We evaluate the proposed method on three data sets: an English personal names data set, an English place names data set, and a Japanese personal names data set. The proposed method outperforms numerous baselines and previously proposed name alias extraction methods, achieving a statistically significant mean reciprocal rank (MRR) of 0.67. Experiments carried out using location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities, and for different languages. Moreover, the aliases extracted using the proposed method are successfully utilized in an information retrieval task and improve recall by 20 percent in a relation-detection task.

Nearest-Neighbor Guided Evaluation of Data Reliability and Its Applications

The intuition of data reliability has recently been incorporated into the main stream of research on ordered weighted averaging (OWA) operators. Instead of relying on human-guided variables, the aggregation behavior is determined in accordance with the underlying characteristics of the data being aggregated. Data-oriented operators such as the dependent OWA (DOWA) utilize centralized data structures to generate reliable weights, however. Despite their simplicity, the approach taken by these operators neglects entirely any local data structure that represents a strong agreement or consensus. To address this issue, the cluster-based OWA (Clus-DOWA) operator has been proposed. It employs a cluster-based reliability measure that is effective to differentiate the accountability of different input arguments. Yet, its actual application is constrained by the high computational requirement. This paper presents a more efficient nearest-neighbor-based reliability assessment for which an expensive clustering process is not required. The proposed measure can be perceived as a stress function, from which the OWA weights and associated decision-support explanations can be generated. To illustrate the potential of this measure, it is applied to both the problem of information aggregation for alias detection and the problem of unsupervised feature selection (in which unreliable features are excluded from an actual learning process). Experimental results demonstrate that these techniques usually outperform their conventional state-of-the-art counterparts.

Social Network Signatures: A Framework for Re-Identification in Networked Data

Data on large dynamic social networks, such as telecommunications networks and the Internet, are pervasive. However, representing these networks in a manner that is conducive to efficient large-scale analysis is often a challenge. In this paper, we focus on the analysis task of re-identification. Re-identification in the context of dynamic networks is essentially a matching problem that involves comparing the behavior of networked entities across two time periods. An entity's social network behavior can be represented as a signature. A similarity score that measures the degree of overlap in signatures can be assigned to pairs of entities observed across specified time periods. The score can then be used as an attribute in a predictive model to classify pairs of entities as matching or non-matching. Prior research has reported success in the domains of e-mail alias detection, author attribution, and identifying fraudulent consumers in the telecommunications industry. In this work, we address the question of why are we able to re-identify entities on real world dynamic networks? Our contribution is two-fold. First, we address the challenge of scale with a framework for matching that does not require pair-wise comparisons to ascertain the similarity scores. We assume a random network structure to estimate performance and show that our estimates are good predictors for simulated networks with different characteristics including clustering coefficient, average degree, size, and different network types such as random, small world and scale-free. Second, we show our method is robust against missing links in the second time period but less tolerant to noise, which is modeled by changes in behavior from the first to second time period. Using our framework, we provide a performance estimate for prediction on networks based solely on their degree distribution and dynamics. This work has significant implications for re-identification problems where scale is a challenge as well as when false negatives (e.g., when fraudulent consumers are not labeled as fraudulent) cannot be observed.

Problems with using MPI 1.1 and 2.0 as compilation targets for parallel language implementations

MPI support is nearly ubiquitous on high-performance systems today and is generally highly tuned for performance. It would thus seem to offer a convenient 'portable network assembly language' to developers of parallel programming languages who wish to target different network architectures. Unfortunately, neither the traditional MPI 1.1 API nor the newer MPI 2.0 extensions for one-sided communication provide an adequate compilation target for global address space languages, and this is likely to be the case for many other parallel languages as well. Simulating one-sided communication under the MPI 1.1 API is too expensive, while the MPI 2.0 one-sided API imposes a number of significant restrictions on memory access patterns that would need to be incorporated at the language level, as a compiler cannot effectively hide them given current conflict and alias detection algorithms.