What\u27s Mine is YOURLS

Hyperlink management is critical to website functionality because a site with dead links is not fully operable for the end user. In libraries, links used for marketing, course materials, electronic resources, social media, and other uses are laborious to maintain. Often, these links are long, unreadable, and unmemorable. In order to streamline link maintenance, improve link usability, and promote resources, an open source, short link manager called Your Own URL Shortener (YOURLS), was implemented at an academic library. This primer describes this process. Not only does YOURLS shorten links, it also acts as a database link manager. Long URLs are then shortened into compact readable formats on a hosted domain. With YOURLS, updating URLs for existing resources can be done in one place. This negates the need to update all instances of a URL on different platforms

Integrated approach to detect spam in social media networks using hybrid features

Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques

Using Four Learning Algorithms for Evaluating Questionable Uniform Resource Locators (URLs)

Malicious Uniform Resource Locator (URL) is a common and serious threat to cyber security. Malicious URLs host unsolicited contents (spam, phishing, drive-by exploits, etc.) and lure unsuspecting internet users to become victims of scams such as monetary loss, theft, loss of information privacy and unexpected malware installation. This phenomenon has resulted in the increase of cybercrime on social media via transfer of malicious URLs. This situation prompted an efficient and reliable classification of a web-page based on the information contained in the URL to have a clear understanding of the nature and status of the site to be accessed. It is imperative to detect and act on URLs shared on social media platform in a timely manner. Though researchers have carried out similar researches in the past, there are however conflicting results regarding the conclusions drawn at the end of their experimentations. Against this backdrop, four machine learning algorithms:Naïve Bayes Algorithm, K-means Algorithm, Decision Tree Algorithm and Logistic Regression Algorithm were selected for classification of fake and vulnerable URLs. The implementation of algorithms was implemented with Java programming language. Through statistical analysis and comparison made on the four algorithms, Naïve Bayes algorithm is the most efficient and effective based on the metrics used

Prediction of drive-by download attacks on Twitter

The popularity of Twitter for information discovery, coupled with the automatic shortening of URLs to save space, given the 140 character limit, provides cybercriminals with an opportunity to obfuscate the URL of a malicious Web page within a tweet. Once the URL is obfuscated, the cybercriminal can lure a user to click on it with enticing text and images before carrying out a cyber attack using a malicious Web server. This is known as a drive-by download. In a drive-by download a user’s computer system is infected while interacting with the malicious endpoint, often without them being made aware the attack has taken place. An attacker can gain control of the system by exploiting unpatched system vulnerabilities and this form of attack currently represents one of the most common methods employed. In this paper we build a machine learning model using machine activity data and tweet metadata to move beyond post-execution classification of such URLs as malicious, to predict a URL will be malicious with 0.99 F-measure (using 10-fold cross-validation) and 0.833 (using an unseen test set) at 1 second into the interaction with the URL. Thus providing a basis from which to kill the connection to the server before an attack has completed and proactively blocking and preventing an attack, rather than reacting and repairing at a later date

Roskapostin tunnistaminen koneoppimisen avulla sosiaalisessa mediassa

Tiivistelmä. Tämä työn tarkoitus on selvittää, miten koneoppimista hyödynnetään suodattamaan roskapostia sosiaalisesta mediasta. Tämän lisäksi koneoppimista vertaillaan muihin tapoihin suodattaa roskapostia. Aihe on tärkeä, koska lähivuosina roskapostista on tullut suuri ongelma sosiaalisen median alustoille. Roskapostin tunnistamiseen manuaalisesti liittyy kuitenkin haittoja, joita ovat suurella sosiaalisen median alustalla suuret kulut sekä epäkäytännöllisyys suuren viestimäärän tarkastamiseen. Työ suoritettiin kirjallisuuskatsauksena. Aiempien tutkimusten perusteella koneoppimista voidaan hyödyntää tämän ongelman lieventämiseen. Koneoppimisen avulla roskapostia pystytään suodattamaan automatisoidusti ilman että tarvitsee tehdä monimutkaisia käsin kirjoitettuja sääntöjä. Koneoppimisalgoritmeina voidaan käyttää esimerkiksi Naive Bayesia ja neuroverkkoja. Työssä käsitellyn aiemman tutkimuksen mukaan Naive Bayes suoriutuu roskapostin suodattamisesta kokonaisuudessa neuroverkkoja paremmin. Työ tarjoaa yleisen katsauksen aiheeseen

How we browse: Measurement and analysis of digital behavior

Accurately analyzing and modeling online browsing behavior play a key role in understanding users and technology interactions. In this work, we design and conduct a user study to collect browsing data from 31 participants continuously for 14 days and self-reported browsing patterns. We combine self-reports and observational data to provide an up-to-date measurement study of online browsing behavior. We use these data to empirically address the following questions: (1) Do structural patterns of browsing differ across demographic groups and types of web use?, (2) Do people have correct perceptions of their behavior online?, and (3) Do people change their browsing behavior if they are aware of being observed? In response to these questions, we find significant differences in level of activity based on user age, but not based on race or gender. We also find that users have significantly different behavior on Security Concerns websites, which may enable new behavioral methods for automatic detection of security concerns online. We find that users significantly overestimate the time they spend online, but have relatively accurate perceptions of how they spend their time online. We find no significant changes in behavior over the course of the study, which may indicate that observation had no effect on behavior, or that users were consciously aware of being observed throughout the stud

Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey

We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building a trustworthy SNSs. In this paper, we conducted an extensive survey, covering (i) the multidisciplinary concepts of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons from existing literature. We conclude this survey paper with an in-depth discussions on the limitations of the state-of-the-art and recommend future research directions in this area.Comment: 35 pages, 8 figures, submitted to ACM Computing Survey