Feature selection encompasses a wide variety of methods for selecting a restricted number of input variables or “features”, which are “relevant” to a problem at hand. In this chapter, we guide practitioners through the maze of methods, which have recently appeared in the literature, particularly for supervised feature selection. Starting from the simplest methods of feature ranking with correlation coefficients, we branch in various direction and explore various topics, including “conditional relevance”, “local relevance”, “multivariate selection”, and “causal relevance”. We make recommendations for assessment methods and stress the importance of matching the complexity of the method employed to the available amount of training data. Software and teaching material associated with this tutorial are available [12].
Intrusion refers to any malicious activity done in order to access confidential data. An intrusion detection system (IDS) detects these attacks and, on detection, it reports them to the administrator. It does so either by comparing the new activity with the past activities or by analyzing the network performance. This system forms a part of the vast security module and works with several other such sub-modules in order to make sure that these unwanted intrusions do not go unreported. The system that has been implemented in this paper is an anomaly-based Intrusion Detection System (IDS). The primary purpose of this implementation is to develop an efficient system in order to detect any external or internal unauthenticated activity. Several models have been experimented with in order to find one that suits the system the best and gives a good enough accuracy. The models that have been experimented with include Logistic Regressor, Random Forest Classifier, K Nearest Neighbor classifier, XGBoost Classifier, Gaussian Naive Bayes Classifier and a Multi-Layer Perceptron Classifier (MLP). Further, the accuracy of each of these models was calculated, and a comparative analysis was done between the performance of these models. The model that performed the best in this particular use case was the Random Forest Classifier giving an accuracy of 99.8% and a macro average F1-Score of 0.98.
Open Source Intelligence can be defined as the retrieval, extraction and analysis of information from publicly available sources. Each of these three processes is the subject of ongoing research resulting in specialised techniques. Today the largest source of open source information is the Internet. Most newspapers and news agencies have web sites with live updates on unfolding events, opinions and perspectives on world events. Most governments monitor news reports to feel the pulse of public opinion, and for early warning and current awareness of emerging crises. The phenomenal growth in knowledge, data and opinions published on the Internet requires advanced software tools which allow analysts to cope with the overflow of information. Malicious use of the Internet has also grown rapidly, particularly on-line fraud, illegal content, virtual stalking, and various scams. These are all creating major challenges to security and law enforcement agencies. The alarming increase in the use of the Internet by extremist and terrorist groups has also emerged. The Joint Research Centre has developed significant experience in Internet content monitoring through its work on media monitoring (EMM) for the European Commission. EMM forms the core of the Commission’s daily press monitoring service, and has also been adopted by the European Council Situation Centre for their ODIN system. This paper will review this growing area of research. A new research topic at the JRC is Web mining and open source intelligence. This applies EMM technology to the wider Internet and not just to news sites. A Google search or user selection is used to identify potential web resources and the extraction and download of all the textual content. This is then followed by automatic change detection, the recognition of places, names and relationships, and further analysis of the resultant large bodies of text. These tools help analysts to process large amounts of documents and derive structured data which is easier to analyse.
A lot of information is hidden in foreign language text, making multilingual information extraction tools – and applications that allow cross-lingual information access – particularly useful. Only a few system developers offer their products for more than two or three languages. Typically, they develop the tools for one language and then adapt them to the others. Information on guidelines how to produce highly multilingual applications with the least possible effort is scarce. Due to the multinational character of the European institutions, the Text Mining applications developed at the Joint Research Centre (JRC) had to be planned with the multilinguality requirements in mind. The purpose of this chapter is to present the design principles behind the in-house developments and to show a number of different applications that are built according to these guidelines. The results of the text analysis applications presented here are visible on the publicly accessible multilingual news gathering, analysis and exploration tool NewsExplorer.
In this paper we study privacy preservation for the publication of search engine query logs. We introduce a new privacy concern, website privacy as a special case of business privacy.We define the possible adversaries who could be interested in disclosing website information and the vulnerabilities in the query log, which they could exploit. We elaborate on anonymization techniques to protect website information, discuss different types of attacks that an adversary could use and propose an anonymization strategy for one of these attacks. We then present a graph-based heuristic to validate the effectiveness of our anonymization method and perform an experimental evaluation of this approach. Our experimental results show that the query log can be appropriately anonymized against the specific attack, while retaining a significant volume of useful data.
The Internet has enabled people to coactively create, share, rate and classify content on an unprecedented scale. Examples of coactive systems include open source software development, wiki communities, social news aggregators, and many others. This paper discusses regularities in online coactive systems of thousands or millions of participants. First, user participation levels are shown to follow a heavy-tail power-law distribution over their entire range, so that a small number very active users make the vast majority of contributions. The power law arises from a simple rule where the probability a person stops contributing varies inversely with the number of contributions. The power law exponent is moreover demonstrated to discriminate between systems according to the effort required to contribute. Next, the level of activity per topic is shown to follow a heavy-tailed distribution, this time lognormal, generated by a simple stochastic popularity reinforcement mechanism. The vast majority of activity thus occurs among a small number of very popular topics. The trends are demonstrated to hold for four large independent online communities with different scopes and purposes.
In this paper we consider a new paradigm of learning: learning using hidden information. The classical paradigm of the supervised learning is to learn a decision rule from labeled data (x i , y i ), x i isin X, y i isin {-1, 1}, i = 1, hellip , lscr. In this paper we consider a new setting: given training vectors in space X along with labels and description of this data in another space X * , find in space X a decision rule better than the one found in the classical paradigm.
Anomaly detection on graphs of social or communication networks has important security applications. The definition of a graph anomaly typically depends on the data and application of interest. Usually central in anomaly detection are the connections amongst the graph's entries and various methods have been developed for their analysis. We review these techniques and also discuss challenges currently faced for high-dimensional dynamic graphs.
In this paper we present a working event extraction and classification infrastructure, which monitors news articles, detects and extracts structured descriptions of Politically Motivated Violent Events (PMVE) and feeds them into an ontological knowledge base. A knowledge base of about 2800 PMVE was built semi-automatically.
Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. Here we consider information cascades in the context of recommendations and information propagation on the blogosphere. In particular, we study the patterns of cascading recommendations that arise in large social networks. We review recent studies of cascading behavior in product recommendation networks, and information diffusion on the blogosphere. Next, we examine theoretical models of information, virus and influence propagation. Last, we present developments on selecting and targeting nodes in networks to maximize the influence or detect cascades and disease/information outbreaks effectively.
Since the web is increasingly used by terrorist organizations, the ability to automatically detect multilingual terrorist-related content is extremely important. In this chapter, we present an efficient detection methodology based on the recently developed graph-based web document representation models. Evaluation is performed on real-world corpora in English and Arabic languages.
We report on some experiments using linguistic information as additional features as part of document representation. The use of linguistic features on several information retrieval and text mining tasks is a hot topic, due to the polarity of conclusions encountered by several researchers. In this work, extracted information of every word like the Part Of Speech, stem and morphological root have been combined in different ways for experimenting on a possible improvement in the classification performance and on several algorithms. Our results show that certain gain can be obtained when these varied features are combined in a certain manner, and that these results are independent from the set of classification algorithms applied or the evaluation paradigm chosen, providing certain consistency to our conclusions in text categorization on the Reuters-21578 collection.
The performance of unsupervised clustering and classification of knowledge fields is significantly improved by deeply merging textual contents with network structure.
Statistical, data mining, machine learning and other data analytic tools are important weapons in the battle against fraud. The fraud environment is reviewed and the magnitude of fraud described. Banking fraud, and especially plastic card fraud, is examined in detail. Tools for evaluating fraud detection systems are described and different classes of detection methods are outlined. To illustrate, the novel method of peer group analysis is described in detail, and illustrated in real applications.
In the area of web search, there is a disconnect between some of the research in the field and what is actually done within a large-scale commercial search engine. Everything from measuring the wrong things to the objectives of the research, have the potential to produce work that although academically interesting, has little commercial value. This paper attempts to reduce this gap by summarizing my experience as both a researcher and an engineer at commercial search engines.
In order to detect the extraordinary, a computer must have a model of ordinary interactions. There is enough text on the web in the 420 languages that are represented there for language models to be built. All the text available for most languages can be crawled in a few months. These language models can show how words are normally associated. Unusual associations can then be detected automatically. This article presents an overview our work in building a very large and complete language model from the web for the French language.
Using unlabeled data to unravel the structure of the data to leverage the learning process is the goal of semi supervised learning. Kernel framework allows to model the data structure using graphs and to build kernel machines such as Laplacian SVM [1]. But a remark is the lack of sparsity in variables of the obtained model leading to a long running time for classification of new points. We provide a way of alleviating this problem by using a L1penalty and a algorithm to efficiently compute the solution. Empirical evidence shows the benefit of the algorithm.
Today data mining is more and more extensively used by very competitive enterprises. This development, brought by the increasing availability of massive datasets, is only possible if solutions to challenges, both theoretic and operational, are found: identify algorithms which can be used to produce models when datasets have thousands of variables and millions of observations; learn how to run and control the correct execution of hundreds of models; automate the data mining process. We will present these constraints in industrial contexts; we will show how to exploit theoretical results (coming from Vladimir Vapnik's work) to produce robust models; we will give a few examples of real-life applications. We will thus demonstrate that it is indeed possible to industrialize data mining so as to turn it into an easy-to-use component whenever data is available.
The paper gives an overview of GPS and radio interface based geolocalisation techniques for cellular telephone networks, including the E911 and E112 initiatives, Location Based Services, and law enforcement/security applications. An example of localisation using the Database Correlation Method is also presented.
