TiSEFE: Time Series Evolving Fuzzy Engine for Network Traffic Classification

Monitoring and analyzing network traffic are very crucial in discriminating the malicious attack. As the network traffic is becoming big, heterogeneous, and very fast, traffic analysis could be considered as big data analytic task. Recent research in big data analytic filed has produces several novel large-scale data processing systems. However, there is a need for a comprehensive data processing system to extract valuable insights from network traffic big data and learn the normal and attack network situations. This paper proposes a novel evolving fuzzy system to discriminate anomalies by inspecting the network traffic. After capturing traffic data, the system analyzes it to establish a model of normal network situation. The normal situation is a time series data of an ordered sequence of traffic information variable values at equally spaced time intervals. The performance has been analyzed by carrying out several experiments on real-world traffic dataset and under extreme difficult situation of high-speed networks. The results have proved the appropriateness of time series evolving fuzzy engine for network classification

A semantic framework for discovering casual relationships

The explosive growth of information at a mind-boggling scale has become an emerging phenomenon of our times. Discovering knowledge from a vast pool of resources is expected to remain a major challenge. In this respect, the extraction of semantic relations then becomes an important research area. While the extraction of ontological relations has been widely explored, the discovery of non-taxonomic relations is still a major bottleneck. Current approaches tend to predominantly employ syntactic approaches and rely largely on extensive manual efforts in the construction of linguistic resources. Our literature review has revealed major gaps in terms of the extraction of non-taxonomic relationships, particularly when it comes to implicit relationships. As a response to this problem, our research then explores a semantic approach for addressing the discovery of non-taxonomic relations such as causal relationships. Based on an empirical study of causality theory and related works, we have formulated a semantic approach for extracting causal patterns in text. The proposed framework incorporates a novel causality sense extraction method, “Purpose Based Word Sense Disambiguation”, together with a context-specific approach, “Graph based Semantics”, for uncovering causality structural patterns. Our approach has produced a set of causality features that is even able to highlight implicit causality patterns. We have employed benchmark data sets of SemEval 2007 and SemEval 2010 data sets together with standard linguistic resources such as WordNet, SemCore and XWNGloss in producing a series of intermediary linguistic resources as building blocks of the framework. A new qualitative measure for determining causal patterns has been formulated and used in conjunction with a gold standard for validating the significance of the findings. We have employed the C5.0 classifier to evaluate the effectiveness of the causality patterns as derived v from the framework. We have demonstrated via the realization of the framework, a purely semantic approach is possible without the need for extensive manual efforts. This research will serve as a key milestone and basis for ensuing discovery of non-taxonomic semantic relations such as causality

An automated learner for extracting new ontology relations

Recently, the NLP community has shown a renewed interest in automatic recognition of semantic relations between pairs of words in text which called lexical semantics. This approach to semantics is concerned with psychological facts associated with the meaning of words. Lexical semantics is an important task with many potential applications including but not limited to, Information Retrieval, Information Extraction, Text Summarization, and Language Modeling. As this task 'automatic recognition of semantic relations between pairs of words in text' can be used in many NLP applications, its implementation are demanding and may include many potential methodologies. And as it includes semantic processing, the results produced still need enhancements and the outcome was always limited in terms of domain or coverage. In this research we developed a buffered system that handle the whole process of extracting causation relations in general domain ontologies. The main achievement of this work is the heavy analysis of statistical and semantic information of causation relation context to generate the learner. The system also builds relation resources that made it possible to learn from itself, were each time it runs the resources incremented with new relations information recording all the statistics of such relation, making its performance enhanced each time it runs. Also we present a novel approach of learning based on the best lexical patterns extracted, besides two new algorithms the CIA and PS that provide the final set of rules for mining causation to enrich ontologies

Ontology enrichment with causation relations

Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. However it suffers from a lack of standards to define concepts, besides the lack of fully automatic knowledge acquisition methods. In performing this learning process, the discovery of non-taxonomic relationships has been identified as being the most difficult. This study is then an attempt to create an enhanced framework for discovering and classifying ontological relationships by using a machine learning strategy. We take into consideration the context of the input text in performing the classification of the semantic relations, in particular, causation relations. The proposed framework extracts initial semantic patterns for causation relation from the input samples, then filters these patterns using two novel algorithms, namely, the “Purpose Based Word Sense Disambiguation” which helps in determining the causation senses for input pair of words and the “Graph Based Semantics” which determines the existence of the causation relations in the sentence and to extract their cause-effect parts. The results show a good performance and the implemented framework cut off many steps of the usual process to produce the final results

A framework for classifying semantic relationships

Recently, the NLP community has shown a renewed interest in lexical semantics in the extent of automatic recognition of semantic relationships between pairs of words in text. Lexical semantics has become increasingly important in many natural language applications, this approach to semantics is concerned with psychological facts associated with meaning of words and how these words can be connected in semantic relations to build ontologies that provide a shared vocabulary to model a specified domain. And represent a structural framework for organizing information across fields of Artificial Intelligence (AI), Semantic Web, systems engineering and information architecture. But current systems mainly concentrate on classification of semantic relations rather than to give solutions for how these relations can be created [14]. At the same time, systems that do provide methods for creating the relations tend to ignore the context in which the conceptual relationships occur. Furthermore, methods that address semantic (non-taxonomic) relations are yet to come up with widely accepted ways of enhancing the process of classifying and extracting semantic relations. In this research we will focus on the learning of semantic relations patterns between word meanings by taking into consideration the surrounding context in the general domain. We will first generate semantic patterns in domain independent environment depending on previous specific semantic information, and a set of input examples. Our case of study will be causation relations. Then these patterns will classify causation in general domain texts taking into consideration the context of the relations, and then the classified relations will be used to learn new causation semantic patterns