Scuttling Web Opportunities By Application Cramming

The web contains large data and it contains innumerable websites that is monitored by a tool or a program known as Crawler. The main goal of this paper is to focus on the web forum crawling techniques. In this paper, the various techniques of web forum crawler and challenges of crawling are discussed. The paper also gives the overview of web crawling and web forums. Internet is emergent exponentially and has become progressively more. Now, it is complicated to retrieve relevant information from internet. The rapid growth of the internet poses unprecedented scaling challenges for general purpose crawlers and search engines. In this paper, we present a novel Forum Crawler under Supervision (FoCUS) method, which supervised internet-scale forum crawler. The intention of FoCUS is to crawl relevant forum information from the internet with minimal overhead, this crawler is to selectively seek out pages that are pertinent to a predefined set of topics, rather than collecting and indexing all accessible web documents to be capable to answer all possible ad-hoc questions. FoCUS is continuously keeps on crawling the internet and finds any new internet pages that have been added to the internet, pages that have been removed from the internet. Due to growing and vibrant activity of the internet; it has become more challengeable to navigate all URLs in the web documents and to handle these URLs. We will take one seed URL as input and search with a keyword, the searching result is based on keyword and it will fetch the internet pages where it will find that keywor

The Best Trail Algorithm for Assisted Navigation of Web Sites

We present an algorithm called the Best Trail Algorithm, which helps solve the hypertext navigation problem by automating the construction of memex-like trails through the corpus. The algorithm performs a probabilistic best-first expansion of a set of navigation trees to find relevant and compact trails. We describe the implementation of the algorithm, scoring methods for trails, filtering algorithms and a new metric called \emph{potential gain} which measures the potential of a page for future navigation opportunities.Comment: 11 pages, 11 figure

Introducing CARONTE: a Crawler for Adversarial Resources Over Non Trusted Environments

The monitoring of underground criminal activities is often automated to maximize the data collection and to train ML models to automatically adapt data collection tools to different communities. On the other hand, sophisticated adversaries may adopt crawling-detection capabilities that may significantly jeopardize researchers' opportunities to perform the data collection, for example by putting their accounts under the spotlight and being expelled from the community. This is particularly undesirable in prominent and high-profile criminal communities where entry costs are significant (either monetarily or for example for background checking or other trust-building mechanisms). This work presents CARONTE, a tool to semi-automatically learn virtually any forum structure for parsing and data-extraction, while maintaining a low profile for the data collection and avoiding the requirement of collecting massive datasets to maintain tool scalability. We showcase CARONTE against four underground forum communities, and show that from the adversary's perspective CARONTE maintains a profile similar to humans, whereas state-of-the-art crawling tools show clearly distinct and easy to detect patterns of automated activity

Parallel Sampling-Pipeline for Indefinite Stream of Heterogeneous Graphs using OpenCL for FPGAs

In the field of data science, a huge amount of data, generally represented as graphs, needs to be processed and analyzed. It is of utmost importance that this data be processed swiftly and efficiently to save time and energy. The volume and velocity of data, along with irregular access patterns in graph data structures, pose challenges in terms of analysis and processing. Further, a big chunk of time and energy is spent on analyzing these graphs on large compute clusters and/or data-centers. Filtering and refining of data using graph sampling techniques are one of the most effective ways to speed up the analysis. Efficient accelerators, such as FPGAs, have proven to significantly lower the energy cost of running an algorithm. To this end, we present the design and implementation of a parallel graph sampling technique, for a large number of input graphs streaming into a FPGA. A parallel approach using OpenCL for FPGAs was adopted to come up with a solution that is both time- and energyefficient. We introduce a novel graph data structure, suitable for streaming graphs on FPGAs, that allows time- and memory-efficient representation of graphs. Our experiments show that our proposed technique is 3x faster and 2x more energy efficient as compared to serial CPU version of the algorithm

Security Analysis of Web and Embedded Applications

As we put more trust in the computer systems we use the need for securityis increasing. And while security features like HTTPS are becomingcommonplace on the web, securing applications remains dicult. This thesisfocuses on analyzing dierent computer ecosystems to detect vulnerabilitiesand develop countermeasures. This includesweb browsers,web applications,and cyber-physical systems such as Android Automotive.For web browsers, we analyze how new security features might solve aproblem but introduce new ones. We show this by performing a systematicanalysis of the new Content Security Policy (CSP) directive navigate-to.In our research, we nd that it does introduce new vulnerabilities, to whichwe recommend countermeasures. We also create AutoNav, a tool capable ofautomatically suggesting navigation policies for this directive.To improve the security of web applications, we develop a novel blackboxmethod by combining the strengths of dierent black-box methods. Weimplement this in our scanner Black Widow, which we compare with otherleading web application scanners. Black Widow both improves the coverageof the web application and nds more vulnerabilities, including ones inPrestashop, WordPress, and HotCRP.For embedded systems,We analyze the new attack vectors introduced bycombining a phone OS with vehicle APIs and nd new attacks pertaining tosafety, privacy, and availability. Furthermore, we create AutoTame, which isdesigned to analyze third-party apps for vehicles for the vulnerabilities wefound

Acquisition des contenus intelligents dans l’archivage du Web

Web sites are dynamic by nature with content and structure changing overtime; many pages on the Web are produced by content management systems (CMSs). Tools currently used by Web archivists to preserve the content of the Web blindly crawl and store Web pages, disregarding the CMS the site is based on and whatever structured content is contained in Web pages. We first present an application-aware helper (AAH) that fits into an archiving crawl processing chain to perform intelligent and adaptive crawling of Web applications, given a knowledge base of common CMSs. The AAH has been integrated into two Web crawlers in the framework of the ARCOMEM project: the proprietary crawler of the Internet Memory Foundation and a customized version of Heritrix. Then we propose an efficient unsupervised Web crawling system ACEBot (Adaptive Crawler Bot for data Extraction), a structure-driven crawler that utilizes the inner structure of the pages and guides the crawling process based on the importance of their content. ACEBot works intwo phases: in the offline phase, it constructs a dynamic site map (limiting the number of URLs retrieved), learns a traversal strategy based on the importance of navigation patterns (selecting those leading to valuable content); in the online phase, ACEBot performs massive downloading following the chosen navigation patterns. The AAH and ACEBot makes 7 and 5 times, respectively, fewer HTTP requests as compared to a generic crawler, without compromising on effectiveness. We finally propose OWET (Open Web Extraction Toolkit) as a free platform for semi-supervised data extraction. OWET allows a user to extract the data hidden behind Web formsLes sites Web sont par nature dynamiques, leur contenu et leur structure changeant au fil du temps ; de nombreuses pages sur le Web sont produites par des systèmes de gestion de contenu (CMS). Les outils actuellement utilisés par les archivistes du Web pour préserver le contenu du Web collectent et stockent de manière aveugle les pages Web, en ne tenant pas compte du CMS sur lequel le site est construit et du contenu structuré de ces pages Web. Nous présentons dans un premier temps un application-aware helper (AAH) qui s’intègre à une chaine d’archivage classique pour accomplir une collecte intelligente et adaptative des applications Web, étant donnée une base de connaissance deCMS courants. L’AAH a été intégrée à deux crawlers Web dans le cadre du projet ARCOMEM : le crawler propriétaire d’Internet Memory Foundation et une version personnalisée d’Heritrix. Nous proposons ensuite un système de crawl efficace et non supervisé, ACEBot (Adaptive Crawler Bot for data Extraction), guidé par la structure qui exploite la structure interne des pages et dirige le processus de crawl en fonction de l’importance du contenu. ACEBot fonctionne en deux phases : dans la phase hors-ligne, il construit un plan dynamique du site (en limitant le nombre d’URL récupérées), apprend une stratégie de parcours basée sur l’importance des motifs de navigation (sélectionnant ceux qui mènent à du contenu de valeur) ; dans la phase en-ligne, ACEBot accomplit un téléchargement massif en suivant les motifs de navigation choisis. L’AAH et ACEBot font 7 et 5 fois moins, respectivement, de requêtes HTTP qu’un crawler générique, sans compromis de qualité. Nous proposons enfin OWET (Open Web Extraction Toolkit), une plate-forme libre pour l’extraction de données semi-supervisée. OWET permet à un utilisateur d’extraire les données cachées derrière des formulaires Web

Partitioning networks into cliques: a randomized heuristic approach

In the context of community detection in social networks, the term community can be grounded in the strict way that simply everybody should know each other within the community. We consider the corresponding community detection problem. We search for a partitioning of a network into the minimum number of non-overlapping cliques, such that the cliques cover all vertices. This problem is called the clique covering problem (CCP) and is one of the classical NP-hard problems. For CCP, we propose a randomized heuristic approach. To construct a high quality solution to CCP, we present an iterated greedy (IG) algorithm. IG can also be combined with a heuristic used to determine how far the algorithm is from the optimum in the worst case. Randomized local search (RLS) for maximum independent set was proposed to find such a bound. The experimental results of IG and the bounds obtained by RLS indicate that IG is a very suitable technique for solving CCP in real-world graphs. In addition, we summarize our basic rigorous results, which were developed for analysis of IG and understanding of its behavior on several relevant graph classes

Prometheus: a generic e-commerce crawler for the study of business markets and other e-commerce problems

Dissertação de mestrado em Computer ScienceThe continuous social and economic development has led over time to an increase in consumption, as well as greater demand from the consumer for better and cheaper products. Hence, the selling price of a product assumes a fundamental role in the purchase decision by the consumer. In this context, online stores must carefully analyse and define the best price for each product, based on several factors such as production/acquisition cost, positioning of the product (e.g. anchor product) and the competition companies strategy. The work done by market analysts changed drastically over the last years. As the number of Web sites increases exponentially, the number of E-commerce web sites also prosperous. Web page classification becomes more important in fields like Web mining and information retrieval. The traditional classifiers are usually hand-crafted and non-adaptive, that makes them inappropriate to use in a broader context. We introduce an ensemble of methods and the posterior study of its results to create a more generic and modular crawler and scraper for detection and information extraction on E-commerce web pages. The collected information may then be processed and used in the pricing decision. This framework goes by the name Prometheus and has the goal of extracting knowledge from E-commerce Web sites. The process requires crawling an online store and gathering product pages. This implies that given a web page the framework must be able to determine if it is a product page. In order to achieve this we classify the pages in three categories: catalogue, product and ”spam”. The page classification stage was addressed based on the html text as well as on the visual layout, featuring both traditional methods and Deep Learning approaches. Once a set of product pages has been identified we proceed to the extraction of the pricing information. This is not a trivial task due to the disparity of approaches to create a web page. Furthermore, most product pages are dynamic in the sense that they are truly a page for a family of related products. For instance, when visiting a shoe store, for a particular model there are probably a number of sizes and colours available. Such a model may be displayed in a single dynamic web page making it necessary for our framework to explore all the relevant combinations. This process is called scraping and is the last stage of the Prometheus framework.O contínuo desenvolvimento social e económico tem conduzido ao longo do tempo a um aumento do consumo, assim como a uma maior exigência do consumidor por produtos melhores e mais baratos. Naturalmente, o preço de venda de um produto assume um papel fundamental na decisão de compra por parte de um consumidor. Nesse sentido, as lojas online precisam de analisar e definir qual o melhor preço para cada produto, tendo como base diversos fatores, tais como o custo de produção/venda, posicionamento do produto (e.g. produto âncora) e as próprias estratégias das empresas concorrentes. O trabalho dos analistas de mercado mudou drasticamente nos últimos anos. O crescimento de sites na Web tem sido exponencial, o número de sites E-commerce também tem prosperado. A classificação de páginas da Web torna-se cada vez mais importante, especialmente em campos como mineração de dados na Web e coleta/extração de informações. Os classificadores tradicionais são geralmente feitos manualmente e não adaptativos, o que os torna inadequados num contexto mais amplo. Nós introduzimos um conjunto de métodos e o estudo posterior dos seus resultados para criar um crawler e scraper mais genéricos e modulares para extração de conhecimento em páginas de Ecommerce. A informação recolhida pode então ser processada e utilizada na tomada de decisão sobre o preço de venda. Esta Framework chama-se Prometheus e tem como intuito extrair conhecimento de Web sites de E-commerce. Este processo necessita realizar a navegação sobre lojas online e armazenar páginas de produto. Isto implica que dado uma página web a framework seja capaz de determinar se é uma página de produto. Para atingir este objetivo nós classificamos as páginas em três categorias: catálogo, produto e spam. A classificação das páginas foi realizada tendo em conta o html e o aspeto visual das páginas, utilizando tanto métodos tradicionais como Deep Learning. Depois de identificar um conjunto de páginas de produto procedemos à extração de informação sobre o preço. Este processo não é trivial devido à quantidade de abordagens possíveis para criar uma página web. A maioria dos produtos são dinâmicos no sentido em que um produto é na realidade uma família de produtos relacionados. Por exemplo, quando visitamos uma loja online de sapatos, para um modelo em especifico existe a provavelmente um conjunto de tamanhos e cores disponíveis. Esse modelo pode ser apresentado numa única página dinâmica fazendo com que seja necessário para a nossa Framework explorar estas combinações relevantes. Este processo é chamado de scraping e é o último passo da Framework Prometheus