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The low cost of communication and internet technology empower the common man to access various services online. But, these services are under watch of attackers who are trying to trick the online user by misleading or fake URLs. The URL communication may lead to phishing attack. Therefore accurate identification of malicious or phishing URL is one of the critical issues. In this paper, a ML (machine learning) model is proposed to recognize the phishing URL. The proposed model formulates the URL classification problem as unary classification problem. Thus two association rule mining algorithms are utilized to mine the association rules namely Apriori and FPTree. The model responsibly identifies the phishing URLs. The experiments on phish Tank database were carried out. Additionally, the mix set of URLs (Phish tank and legitimate) were also used with the association rule based classification model to validate the performance. The obtained results in terms of accuracy confirm the goodness of the proposed ML based phishing URL classification model. According to the results we found the Apriori based rules are accurate as compared to FP-Tree based URL classification. On the other hand the FP-Tree is efficient in terms of time and memory resource utilization.

In this paper, we have discussed our skills in training and testing of a malicious uniform resource locator detecting system. there have been many trends in the technology and security which inspire our analysis. First, being online has grown to be a progressively dangerous place for many people. In 2011, according to Symantec, there is an increase in internet attacks by 36%. This interprets into roughly 4,500 attacks on a daily basis. Second, there is a vital growth in personal and enterprise use of mobile for the internet. In 2012, according to the State of quality Survey by Symantec, it was noted that after principally proscribed by IT, mobile phones are getting used currently by much immeasurable staff throughout the planet. Online vulnerabilities are on the rise with the employment of smart phones and devices for every personal and skilled usage. This research targets machine learning answers to identify malicious uniform resource locators employing a combination of URL lexical options, payload size, and python supply options. we tend to use Support Vector Machine with a polynomial kernel and logistic regression to attain an accuracy of 98%

The purpose of this survey is to study the working of search engine using search engine optimization, web crawler and web mining. The large growth of World Wide Web (web) needs an efficient system that will manage the data over a web. This huge data get generated, deleted, manipulated every day and it is a source for millions of people all over the world. Search engine is the system provides relevant presentation of data that is to be searched. Search engine show the working of search engine optimization, web crawler, web mining. Search engine works on the architecture of web crawler that uses the web mining as well as search engine optimization to gather and represent the related data.

This paper is a finished study of various tools and strategies utilized for SEO. Search Engines which are utilized to discover any data crosswise over web. The primary goal of any site is to list at the top of all the links. Search Engine Optimization is the specialty of enhancing a site's perceivability in the fundamental assemblage of search engine results. This craft of enhancing a sites perceivability in the assemblage of search engines result is conceivable utilizing the methods and apparatuses accessible in the market.

Günümüzde kimlik avı (oltalama/phishing) saldırılarına çok sık rastlanmaktadır. Bu tür saldırılar insanların kişisel bilgilerini ele geçirmek ya da insanları dolandırmak amacıyla gerçekleştirilmektedir. Kimlik avı saldırılarının birden fazla türü bulunmaktadır. Bu türlerden birisi de tekdüzen kaynak bulucu (uniform resource locater – URL) yoluyla gerçekleştirilen ve yaygın olarak rastlanılan saldırılardır. Bu çalışmanın amacı, URL adreslerinin farklı makine öğrenmesi algoritmaları kullanarak zararlı olup olmadığını sınıflandırmaktır. Çalışmada destek vektör makineleri, rastgele orman, Gauss Naive Bayes, lojistik regresyon, k-en yakın komşu, karar ağaçları, çok katmanlı algılayıcılar ve XGBoost algoritmaları olmak üzere sekiz farklı makine öğrenmesi algoritması kullanılmıştır. Eğitim ve test amaçlı kullanılmak üzere USOM, Alexa ve Phishtank üzerinden veriler elde edilmiştir. Bu verilere çeşitli veri ön-işleme adımları uygulanarak özellik çıkarımı gerçekleştirilmiştir. Araştırma sonucunda birden fazla modelde %99.8 doğruluk oranına ulaşılarak, makine öğrenmesi algoritmalarının bu alandaki başarımı kanıtlanmıştır.

Recently, long-term, advanced cyber-attacks targeting a specific enterprise or organization have been occurring again. These attacks occur over a long period and bypass detection by security systems unlike the existing attack pattern. For such reason, they create problems such as delayed real-time response and detection after damages have already been incurred. This paper introduces the design of technology that applies real-time network traffic monitoring to detect unknown functional cyber-attack on the network. Specifically, the algorithm was verified and evaluated in terms of performance in an actual commercial environment. Cyber-attack detection performance is expected to be improved by enhancing the algorithm and processing large volumes of traffic.

Web Usage Mining (WUM) is the major application of data mining to the web data and analyzes the user's visiting activities and attains their interests by inspecting the web log files. The first and crucial step in WUM is preprocessing of log for cleaning data, after that it can be suitable for mining purposes. The cleaning includes removal of unnecessary data such as image files, unclear data and robots. The aim of this research is to identify Robot Requests by analyzing web server log file using different methods such as IP address check, User agent check, Head request check, URL weight calculation and URL similarity calculation. Web Robots are the software programs that retrieve Web resources by traversing the hyperlink structure of Web. These robots can be used for malicious intensions; therefore they must be detected and removed from the log files. Eradication of robots aids to reduce log data and discern genuine users and there access patterns.

This paper describes a new interaction concept, which is based on customizable self-adhesive stickers with an integrated button, manufacturable at low cost. By pressing the button, users can execute specialized functionalities on their mobile phones, eg dialing ...

The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.

The convergence of Web technology, wireless networks, and portable client devices provides new design opportunities for computer/communications systems. In the HP Labs' Cooltown project we have been exploring these opportunities through an infrastructure to support Web presence for people, places and things. We put Web servers into things like printers and put information into Web servers about things like artwork; we group physically related things into places embodied in Web servers. Using URLs for addressing, physical URL beaconing and sensing of URLs for discovery, and localized Web servers for directories, we can create a location-aware but ubiquitous system to support nomadic users. On top of this infrastructure we can leverage Internet connectivity to support communications services. Web presence bridges the World Wide Web and the physical world we inhabit, providing a model for supporting nomadic users without a central control point

The traditional banking system, such as transactions over the counter using bank book is enhanced by adding the elements of electronic banking, where nowadays all transaction can be done over the network, because of globalization and the advancement of information and communication technology. However, significant threats to this technology also come in parallel, such as from banking frauds. This is why building trust among banking users by providing security mechanisms is very important. In the electronic banking, security mechanisms mainly focus on proving a secure environment for the online transaction, especially user authentication. Many researchers have been proposed various models for the online user access authentication for the banking industry. Most of the researches are based on traditional the form of username and password, but with the varying mechanism of password forms. For example, password in arbitrary value, password in arbitrary value through secured channel, as well as biometric like fingerprint recognition, voice recognition, or retina recognition. In this paper we reviewed the current existing user access for online banking based on mobile phone, the functions of the SIM card, the characteristics mobile of the SIM card are described. Cloning, a threat to the SIM card is also described. Findings show that all the security model for online user access contains password in the form of value, biometric, or PIN. Thus, none of the existing user access proposed the idea of the user access based on International Mobile Equipment Identity (IMEI) number to strength the security of the user access. The IMEI is a 15-or 17-digit code that uniquely identifies mobile phone sets.

We developed a new content routing based on the virtual backbone structure, which groups wireless nodes and contents into a virtual architecture. Our approach is scalable, works with local information, and does not rely on address information. The naming system uses flat naming to identify nodes and contents, and organizes these identifiers together. Backbone nodes can be selected automatically or predefined to direct their associated normal nodes in a local area. The normal nodes are guided by the backbone nodes to full fill the searching and routing processes. With a virtual structure, the searching performance can be improved by using the DHT technique. Experiments using ns2 simulator demonstrate that this virtual backbone routing architecture has the following significances: workable without being aware address in a mobile situation; scalable with the size of network; efficient in terms of the reduced hop counts and short end-to-end delay, and also resistant to the dead-end problem.

Workflow management is a serious concern in any organisation and a clinical laboratory is a particularly good example of where the information system (LIS) needs to be well integrated with the physical system comprising the doctors, technicians and the machines necessary to analyse and report on samples. It is the points of synchronisation between events and decision making in the outside world and control flow inside the information system that constitute the problem domain of workflow management. We propose an extension to the workflow management solution described in the OpenLabs architecture specification in which the emphasis is moved away from an essentially message orientated workflow system to one where the system may reasonably expect to deal with data that includes many different types of information such as images, diagrams, graphs and sound. Our key point is extensibility; the proposed extension of the OpenLabs architecture is able to deal with new data types without significant modification of the workflow management software. Our approach has been based on using some of the desirable features of the Hypertext Markup Language (HTML) and Uniform Resource Locators (URLs) found in the World Wide Web (WWW) and the use of industry standard object technology based on the Object Management Group's (OMG) Common Object Request Broker Architecture (CORBA).

Web is a system of interlinked hypertext documents accessed via Internet. Internet is a global system of interconnected computer networks that serve billions of users worldwide. The huge amount of documents on the web is challenging for web search engines. Web contains multiple copies of the same content or same web page. Many of these pages on the Web are duplicates and near duplicates of other pages. Web search engines face substantial problems due to such duplicate and near duplicate web pages. These pages enlarge the space required to store the index, increase the cost of serving results and so frustrates the users. To assist search engines to provide search results free of redundancy to users and to provide distinct useful results on the first page, duplicate and near duplicate detection is required. The proposed approach will detect near duplicate web pages to increase search effectiveness and storage efficiency of search engine.

This paper introduces the GVIS framework and describes one of its applications built in support of user profile awareness. This application is aimed at opening part of users' profiles to their inspection by exploiting a graphical representation of their personal data. We developed an infrastructure for presenting these high level information in a configurable and adaptable way. The framework we developed is able to retrieve data from heterogeneous sources just by writing a small adapter and allows us to mix together different streams through an XML configuration that relies on a set of operations for elicitation of the most interesting fragments. The final goal is to provide an easily readable graphical representation of the most relevant information, in order to support the human visual system, more capable to have an overview with this kind of solution than with text. As an example application we have mashed up URLs from user browsing history with tags coming from del.icio.us: the resulting output, represented as a pie chart, shows the most relevant subjects followed by a user. Some open issues and problems, we hope to research next, are presented in the conclusion part.