The widespread adoption of web vulnerability scanners and the differences in the functionality provided by these tool-based vulnerability detection approaches increase the demand for testing their detection effectiveness. Despite the advantages of dynamic testing approaches, the literature lacks studies that systematically evaluate the performance of open source web vulnerability scanners. The main objectives of this study are to assess the performance of open source scanners from multiple perspectives and to examine their detection capability. This paper presents the results of a comparative evaluation of the security features as well as the performance of four web vulnerability detection tools. We followed this comparative assessment with a case study in which we evaluate the level of agreement between the results reported by two open source web vulnerability scanners. Given that the results of our comparative evaluation did not show significant performance differences among the scanners while the results of the conducted case study revealed high level of disagreement between the reports generated by different scanners, we conclude that the inconsistencies between the reports generated by different scanners might not necessarily correlate with their performance properties. We also present some recommendations for helping developers of web vulnerabilities scanners to improve their tools’ capabilities.

Sentiment Analysis (SA) is an active research area nowadays due to the tremendous interest in aggregating and evaluating opinions being disseminated by users on the Web.SA of English has been thoroughly researched; however research on SA of Arabic has just flourished.Twitter is considered a powerful tool for disseminating information and a rich resource for opinionated text containing views on many different topics.In this paper we attempt to bridge a gap in Arabic SA of Twitter which is the lack of sentiment lexicons that are tailored for the informal language of Twitter.We generate two lexicons extracted from a large dataset of tweets using two approaches and evaluate their use in a simple lexicon based method.The evaluation is performed on internal and external datasets.The performance of these automatically generated lexicons was very promising, albeit the simple method used for classification.The best F-score obtained was 89.58% on the internal dataset and 63.1-64.7% on the external datasets.

The feature of posting comments enables websites visitors (e.g., Youtube and Amazon) to interact and contribute to the posted content by adding comments. The fact that such comments are becoming part of the website content so that many visitors read them and that such comments are usually unvetted make them attractive to spammers for the purposes of advertising, spreading malware, phishing attacks, or spreading political or religious views. Due to large volume of comment spam, using manual filtration and vetting is unpractical and hence automatic spam detection techniques play a de-facto role in fighting spam content. In this paper, we propose and develop a comment spam detection mechanism that can be deployed as a browser plugin for inspecting the Document Object Model (DOM) of the web page in question and remove comments with spam content. We examine most detection features in the literature along with proposing new features to build a comment spam classifier. In order to test the accuracy of our classifier, we manually label a new corpus of blogs comments. We encourage other researchers to build upon our work and we hope that our corpus will benefit the research community in this area.

Abstract The prevalence and severity of application‐layer vulnerabilities increase dramatically their corresponding attacks. In this paper, we present an extension to PHPIDS, an open source intrusion detection and prevention system for PHP‐based web applications, to visualize its security log. Our usage of security data visualization is motivated by the fact that most security defense systems are mainly based on text‐based logs for recording security‐related events, which are difficult to analyze and correlate. The proposed extension analyzes PHPIDS logs, correlates these logs with the corresponding web server logs, and plots the security‐related events. We use a set of tightly coupled visual representations of hypertext transfer protocol server requests containing known and suspicious malicious content, to provide system administrators and security analysts with fine‐grained visual‐based querying capabilities. We present multiple case studies to demonstrate the ability of our PHPIDS visualization extension to support security analysts with analytic reasoning and decision making in response to ongoing web server attacks. Experimenting the proposed PHPIDS visualization extension on real‐world datasets shows promise for providing complementary information for effectivesituational awareness. Copyright © 2014 John Wiley & Sons, Ltd.

Recently, subjectivity and sentiment analysis of Arabic has received much attention from the research community. In the past two years, an enormous number of references in the field have emerged compared to what has been published in previous years. In this paper, we present an updated survey of the emerging research on subjectivity and sentiment analysis of Arabic. We also highlight the challenges and future research directions in this field.

The user location information represents a core dimension as understanding user context is a prerequisite for providing human-centered services that generally improve quality of life. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task due in part to the expected various objects (such as walls and people) that reflect and disperse signals. In this paper, we survey the related work in the field of indoor positioning by providing a comparative analysis of the state-of-the-art technologies, techniques, and algorithms. Unlike previous studies and surveys, our survey present new taxonomies, review some major recent advances, and argue on the area open problems and future potential. We believe this paper would spur further exploration by the research community of this challenging problem space.

Fake identities and user accounts (also called "Sybils") in online communities represent today a treasure for adversaries to spread fake product reviews, malware and spam on social networks, and Astroturf political campaigns. State-of-the-art in the defense mechanisms includes Automated Turing Tests (ATTs such as CAPTCHAs) and graph-based Sybil detectors. Sybil detectors in social networks leverage the assumption that Sybils will find it hard to befriend real users which leads to Sybils being connected to each other forming strongly connected sub graphs that can be detected using graph theory. However, the large majority of Sybils are in fact successful in integrating themselves into real user communities (such as the case in Twitter and Facebook). In this paper, we first study and compare the current detection mechanisms of Sybil accounts. We also explore various types of Twitter Sybil accounts detection features with the objective of building an effective and practical classifier. In order to build and evaluate our classifier, we collect and manually label a dataset of twitter accounts, including human users, bots, and hybrid (i.e., Tweets are posted by both human and bots). We believe this Twitter Sybils corpus will help researchers in conducting sound measurement studies. We also develop a browser plug-in (that we call Twitter Sybils Detector or TSD for short) that utilizes our classifier and warns the user about possible Sybil accounts before accessing them, upon clicking on a Twitter account.

Corpora have opened up many new areas of research in the linguistic domain, which would never been possible without them. Moreover, corpora have proved their usefulness not only in the linguistic domain but also in other domains, such as medical, economic, legal, pharmacological, etc. English is considered to have the richest language resources in most of these domains, while Arabic reveals a gap in most of them. This paper tries to fill the gap in the pharmacological domain, especially for drugs, by constructing the first Arabic drug corpus, which is composed of 202 drugs, each drug is saved in a text file with UTF-8 character encoding. The corpus was manually annotated with four-entity types: generic (for drug's generic name), brand for (trade names), chemical formula and class (for drug classes).

This paper serves to introduce an initial haptic based system for teaching handwriting of Arabic letters to students with visual impairments. The proposed system provides full and partial guidance through haptic playback. In addition, the system automatically evaluates student progress.

Web spam techniques aim to mislead search engines so that web spam pages get ranked higher than they deserve. This leads to misleading search results as spam pages might appear in search results although the content of these spam pages might not be related to the search terms. Despite the effort of search engines to deploy various techniques to detect and filter out web spam pages from being listed in their search results, spammers continue to develop new tactics to evade search engines detection mechanisms. In this paper, we study the effectiveness and accuracy of newly developed anti-spamming techniques in Google search engine. Focusing on Arabic spam pages, our study results show that Google anti-spamming techniques are ineffective against spam pages with Arabic content. We explore various types of web spam detection features to obtain an appropriate set of detection features that yield a reasonable detection accuracy. In order to build and evaluate our classifier, we collect and manually label a dataset of Arabic web pages, including both benign and spam pages. We believe this Arabic web spam corpus helps researchers in conducting sound measurement studies. We also develop a browser plug-in that utilizes our classifier and warns the user about web spam pages before accessing them, upon clicking on a search term. The plug-in has also the ability to filter out search engine results.

According to World Health Organization (WHO), in June, 2012 there were 285 million Visually Impaired People (VIP) around the world. Unfortunately, many face difficulties when browsing and getting information from the Internet especially when using traditional methods. In our paper, we aim at taking advantage of context-aware computing to assist VIP with contextualized web pages. We propose an integrable proxy service to minimize VIP's browsing problems and make it more effective. This service can reorganize the requested Web page content according to the user's preferences, so screen readers would not read content that is not of interest to the user. The result of the proxy was evaluated using two approaches: (1) manual inspection and (2) user testing. In the first experiment, 64% of the 50 converted websites got satisfactory results. In the second experiment, five VIP have tested the output of our service and expressed their general satisfaction with it.

The prevalence and severity of application-layer vulnerabilities increase dramatically their corresponding attacks. In this paper, we present an extension to PHPIDS, an open source intrusion detection and prevention system for PHP-based web applications, to visualize its security log. The proposed extension analyzes PHPIDS logs, correlates these logs with the corresponding web server logs, and plots the security-related events. We use a set of tightly coupled visual representations of HTTP server requests containing known and suspicious malicious content, to provide system administrators and security analysts with fine-grained visual-based querying capabilities. We present multiple case studies to demonstrate the ability of our PHPIDS visualization extension to support security analysts with analytic reasoning and decision making in response to ongoing web server attacks. Experimenting the proposed PHPIDS visualization extension on real-world datasets shows promise for providing complementary information for effective situational awareness.

Visual impairment is one of the disabilities that could injure people congenitally or adventitiously. The number of individuals who suffer from visual impairment is increasing rapidly. Therefore, there is an urgent need to develop assistive technologies that aid blind people. Our proposed system aims to provide an assistive technology for blind and visually impaired individuals by exploiting the power of smart phones, which are existing, popular, and often used by blind individuals. Our proposed system provides a novel navigation system that operates on smart phones and provides a route to any destination in a building from the user's current position.

Sentiment analysis is the process of identifying the polarity of sentiments held in opinions found in pieces of text and classifying them as positive, negative or neutral. In this paper, we propose the implementation of a sentiment analysis tool that is conducted over text found in Arabic new media including web forums, comments on newspaper articles and other websites with evaluative content. The expected input of the tool, which is informal Colloquial Arabic, is characterized to be of highly non-structured nature and subject to trends used to express sentiments. Our solution is a novel technique that merges the area of human computation with the task of natural language processing.

In this paper, we propose the building of ontologies from Arabic Wikipedia. Our solution relies on a hybrid approach where Wikipedia categories' hierarchy will serve as a backbone of the ontology, and then they are refined and extended by semantic relations extracted from Wikipedia articles infoboxes and hyperlinks. Moreover, we plan to investigate the effectiveness of using each approach independently to build the required ontology.

The need of having a topic segmentation system for Arabic text is to improve the functionalities of Arabic Information Retrieval (AIR). Topic segmentation of texts has been used to improve the accuracy of the subsequent processes such as question answering and information retrieval. In this paper, we present the assessment of two algorithms for Arabic text segmentation which are TextTilling and C99. We evaluate the performance of these algorithms using the classical Recall/Precision metrics and the Reader Judgment method.

Due to the large amount of information available on the web that is not necessarily true or believable, credibility of web information is becoming an increasingly important area to understand. Recently, most research has been available to develop automatic measures for web information credibility in languages such as Japanese, Germen and English websites. Unfortunately, there is no research for the credibility of Arabic web content.

OpenGL is an industry standard for rendering computer graphics. This paper brings Arabic language support to OpenGL in the form of a library, named GLAL. The library can be used easily and efficiently by programmers to render Arabic script in their OpenGL applications. GLAL uses a widely available application programming interface, Uniscribe API, to contextually analyze the text. Then it builds OpenGL display lists that render the text right to left. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract People with visual disabilities face many difficulties and barriers when using computers and the Internet. Such people need the help of IT developers to create adaptive technologies that facilitate their interaction with the computers and Internet. This paper presents the design and implementation of an Arabic Braille environment (ABE). The paper also exposes to the reader the ABE's functionality and unique features. The ABE is designed to facilitate Arabic‐speaking visually impaired people interaction with computers, as well as helping sighted users to communicate with the visually impaired. Copyright © 2003 John Wiley & Sons, Ltd.