Cs.Columbia.Edu †Institute of Computer Science, Foundation for Research and Technology – Hellas, Greece {Polakis, Markatos}@Ics.Forth.Gr
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Whisperkey: Creating a User-Friendly URL Shortener
WhisperKey: Creating a User-Friendly URL Shortener Eunice Lin, Flora Tan, Linda Wang May 12, 2016 Abstract of links. Unlike other popular URL shorteners such as Tinyurl or Bitly that map the original URL to a URL shorteners convert long, unwieldy URLs into random string of letters and numbers, ShoutKey was shorter ones and redirect all requests to the short- designed to be user-friendly by using keys that are ened URL to the original website. In this project, standard English words that are easily pronounced we investigate the security flaws of ShoutKey, an or spelled. This makes it easy for users to share easy-to-use temporary URL shortener that maps a link to those in close proximity, for example, by simple dictionary words to a given URL of interest. verbally telling the key to a friend across the room or writing it on a whiteboard at the front of class. We first conduct a security analysis of the vulnera- Anyone who goes to the ShoutKey link can access bilities encompassed by Shoutkey. Then, we conduct the corresponding URL. a standard dictionary attack to access private URLs, storing the successful dictionary words in a separate database to conduct a second, more optimized dic- tionary attack, and analyzing the successful redirects to determine if there is any leakage of private user data. Finally, we propose a more secure implementa- tion of ShoutKey called WhisperKey and empirically analyze how WhisperKey is designed to be less prone to security attacks than ShoutKey is. 1 Introduction 1.1 URL Shorteners Figure 1: A ShoutKey with "laugh" as its key. -
Phishing Detection Using Machine Learning
Phishing detection using machine learning MSc Research Project MSc Cyber Security Suraj Wandhare Student ID: 18157432 School of Computing National College of Ireland Supervisor: Ben Fletcher www.ncirl.ie National College of Ireland Project Submission Sheet School of Computing Student Name: Suraj Wandhare Student ID: 18157432 Programme: MSc Cyber Security Year: 2019 Module: MSc Research Project Supervisor: Ben Fletcher Submission Due Date: 03/02/2020 Project Title: Phishing detection using machine learning Word Count: 3500 Page Count: 11 I hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project. ALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action. I agree to an electronic copy of my thesis being made publicly available on NORMA the National College of Ireland's Institutional Repository for consultation. Signature: Date: 3rd February 2020 PLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST: Attach a completed copy of this sheet to each project (including multiple copies). Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies). You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. -
Longitudinal Study of Links, Linkshorteners, and Bitly Usage on Twitter Longitudinella Mätningar Av Länkar, Länkförkortare Och Bitly An- Vänding På Twitter
Linköping University | Department of Computer and Information Science Bachelor’s thesis, 16 ECTS | Link Usage 2020 | LIU-IDA/LITH-EX-G--20/001--SE Longitudinal study of links, linkshorteners, and Bitly usage on Twitter Longitudinella mätningar av länkar, länkförkortare och Bitly an- vänding på Twitter Mathilda Moström Alexander Edberg Supervisor : Niklas Carlsson Examiner : Marcus Bendtsen Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet - eller dess framtida ersättare - under 25 år från publicer- ingsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka ko- pior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervis- ning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säker- heten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsman- nens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose. -
JETIR Research Journal
© 2018 JETIR April 2018, Volume 5, Issue 4 www.jetir.org (ISSN-2349-5162) Comparative Analysis of Malicious Detection of Short URLs from Tweets 1Tareek Pattewar, 2Manish Wagh, 3Umesh Bisnariya, 4Lomesh Patil 1Assistant Professor, 2 3 4 Students Department of Information Technology R C Patel Institute of Technology, Shirpur Dist. Dhule-425405 Maharashtra, India Abstract : Online Social Networks (OSNs) have become fundamental parts of our online lives, and their popularity is increasing at a surprising rate every day. Growing popularity of Twitter has attracted the attention of attackers who attempt to manipulate the features provided by Twitter to gain some advantage, such as driving Twitter users to other websites that they post as short URLs (Uniform Resource Locators) in their tweets. Even short URLs are also used to avoid sharing overly long URLs and save limited text space in tweets. Significant numbers of URLs shared in the OSNs are shortened URLs. Despite of its potential benefits from genuine usage, attackers use shortened URLs to hide the malicious URLs, which direct users to malicious pages. Although, OSN service providers and URL shortening services utilize certain detection mechanisms to prevent malicious URLs from being shortened, research has found that they fail to do so effectively. In this paper, we developed mechanism to develop a machine learning classifier which detect malicious short URLs. And comparative analysis of detection rate with various classification algorithm, and but also with online detection on Virus Total. Index Terms - Online Social Networks, twitter, short URLs, malicious URLs, machine Learning, classification ________________________________________________________________________________________________________ I. INTRODUCTION In the age of web 2.0, information is contained in the form of webpages and shared via Online Social Networks (OSNs), emails, messages, and texts. -
Steganography- a Data Hiding Technique
International Journal of Computer Applications (0975 – 8887) Volume 9– No.7, November 2010 Steganography- A Data Hiding Technique Arvind Kumar Km. Pooja Assistant Professor Vankateshwara institute of computer Vidya College of engineering, Meerut, India Science and technology, Meerut, India ABSTRACT In steganography, the possible cover carriers are innocent looking Steganography is the art of hiding information and an effort to carriers (images, audio, video, text, or some other digitally conceal the existence of the embedded information. It serves as a representative code) which will hold the hidden information. A better way of securing message than cryptography which only message is the information hidden and may be plaintext, cipher text, conceals the content of the message not the existence of the message. images, or anything that can be embedded into a bit stream. Together Original message is being hidden within a carrier such that the the cover carrier and the embedded message create a stego-carrier. changes so occurred in the carrier are not observable. In this paper we Hiding information may require a stego key which is additional secret will discuss how digital images can be used as a carrier to hide information, such as a password, required for embedding the messages. This paper also analyses the performance of some of the information. For example, when a secret message is hidden within a steganography tools. Steganography is a useful tool that allows covert cover image, the resulting product is a stego-image. transmission of information over an over the communications A possible formula of the process may be represented as: cover channel. -
Steganography & Steganalysis
June 6, 2003 Steganography & Steganalysis SpyHunter www.spy-hunter.com [email protected] 1 AgendaAgenda X Steganography – What is Steganography? – History – Steganography today – Steganography tools X Steganalysis – What is Steganalysis? – Identification of Steganographic files – Cracking Steganographic files – What’s in the future? 2 Steganography 3 SteganographySteganography -- DefinitionDefinition X Steganography – from the Greek word steganos meaning “covered” – and the Greek word graphie meaning “writing” X Steganography is the process of hiding of a secret message within an ordinary message and extracting it at its destination X Anyone else viewing the message will fail to know it contains hidden/encrypted data 4 SteganographySteganography -- HistoryHistory X Greek history – warning of invasion by scrawling it on the wood underneath a wax tablet. To casual observers, the tablet appeared blank. X Pirate legends tell of the practice of tattooing secret information, such as a map, on the head of someone, so that the hair would conceal it. 5 SteganographySteganography X Both Axis and Allied spies during World War II used such measures as invisible inks -- using milk, fruit juice or urine which darken when heated. X Invisible Ink is also a form of steganography 6 SteganographySteganography X The U.S. government is concerned about the use of Steganography. X Common uses in include the disguising of corporate espionage. X It’s possible that terrorist cells may use it to secretly communicate information X It’s also a very good Anti-forensics mechanism to mitigate the effectiveness of a forensics investigation 7 SteganographySteganography Terror groups hide behind Web encryption By Jack Kelley, USA TODAY AP WASHINGTON — Hidden in the X-rated pictures on several pornographic Web sites and the posted comments on sports chat rooms may lie the encrypted blueprints of the next terrorist attack against the United States or its allies. -
Using URL Shorteners to Compare Phishing and Malware Attacks
Using URL Shorteners to Compare Phishing and Malware Attacks Sophie Le Page Guy-Vincent Jourdan Gregor v. Bochmann University of Ottawa University of Ottawa University of Ottawa [email protected] [email protected] [email protected] Jason Flood Iosif-Viorel Onut CTO Security Data Matrices IBM Centre for Advanced Studies fl[email protected] [email protected] Abstract—In this work we use URL shortener click analytics to providers such as bit.ly1 or goo.gl2, allow viewing in real time compare the life cycle of phishing and malware attacks. We have the click traffic of a given short URL, including referrers and collected over 7,000 malicious short URLs categorized as phishing countries referring it. or malware for the 2 year period covering 2016 and 2017, along with their reported date. We analyze the short URLs and find Unfortunately attackers abuse URL shortening services, that phishing attacks are most active 4 hours before the reported perhaps to mask the final destination where the victim will date, while malware attacks are most active 4 days before the land after clicking on the malicious link. In our research we reported date. We find that phishing attacks have higher click make use of the Bitly URL shortening service, reportedly the through rate with shorter timespan. Conversely malware attacks most popular service [1], [2], to analyze clicks for phishing and have lower click through rate with longer timespan. We also show the comparisons of referrers and countries from which malware attacks. By phishing attacks we mean websites that short URLs are accessed, showing in particular an increased trick a user into providing personal or financial information by use of social media to spread both kinds of attacks. -
Dead.Drop: URL-Based Stealthy Messaging
dead.drop: URL-based Stealthy Messaging Georgios Kontaxis∗, Iasonas Polakis†, Michalis Polychronakis∗ and Evangelos P. Markatos† ∗Computer Science Department, Columbia University, USA {kontaxis, mikepo}@cs.columbia.edu †Institute of Computer Science, Foundation for Research and Technology – Hellas, Greece {polakis, markatos}@ics.forth.gr Abstract—In this paper we propose the use of URLs as straightforward approach is limited in terms of the amount a covert channel to relay information between two or more of data that can be encoded in each URL, due to the need parties. We render our technique practical, in terms of band- of blending in with the population of actual URLs. We width, by employing URL-shortening services to form URL chains of hidden information. We discuss the security aspects overcome such restrictions by employing URL-shortening of this technique and present proof-of-concept implementation services to form URL chains of hidden information. Multiple details along with measurements that prove the feasibility of long, information-carrying URL look-alikes are arranged our approach. and digested in reverse order, and each resulting short URL is appended to the next long URL before it is shortened. I. INTRODUCTION In the end, each short URL unwraps into an information- The Internet’s public character mandates the need for carrying long URL which contains the pointer to the next confidential communications in respect to users’ right to short URL, thereby forming a chained sequence of messages. privacy. Cryptography may not always suffice on its own, We discuss the security of our approach and present the as a user can be forced to surrender the key upon discovery details of our proof-of-concept implementation, along with of an encrypted message [1]. -
Detecting Spam Classification on Twitter Using URL Analysis, Natural Language Processing and Machine Learning
International Journal of Innovative and Emerging Research in Engineering Volume 3, Special Issue 1, ICSTSD 2016 Detecting Spam Classification on Twitter Using URL Analysis, Natural Language Processing and Machine Learning Ms Priyanka Chorey Ms. Sonika A. Chorey P.R.M.I.T.&R. Badnera, Amravati India P.R.M.I.T.& R. Badnera, Amravati, India [email protected] [email protected] Ms.Prof R.N.Sawade Ms.P.V.Mamanka P.R.M.I.T.&R. Badnera, Amravati India P.R.M.I.T.& R. Badnera, Amravati, India [email protected] [email protected] Abstract- In the present day world, people are so much downloading of malwares. Malware’s are malicious habituated to Social Networks. Because of this, it is very easy to software spread spam contents through them. One can access the details of any person very easily through these sites. No one is safe inside the social media. In this paper we are proposing an application which 2) Phishers: These are account users who spread uses an integrated approach to the spam classification in Twitter. malicious URLs through their tweets. Legitimate users The integrated approach comprises the use of URL analysis, who click on these links end up in wrong websites. This natural language processing and supervised machine learning leads to the stealing of passwords, credit card information techniques. In short, this is a three step process. In this paper we and so on. consider the problem of detecting spammers on twitter. We construct a large labeled collection of users, manually classified into 3) Adult Content Propagators: These spammers spammers and non-spammers. -
Longitudinal Measurements of Link Usage on Twitter Longitudinella Mätningar Av Länkanvändning På Twitter
Linköping University | Department of Computer and Information Science Bachelor’s thesis, 16 ECTS | Link Usage 2019 | LIU-IDA/LITH-EX-G--19/036--SE Longitudinal measurements of link usage on Twitter Longitudinella mätningar av länkanvändning på Twitter Oscar Järpehult and Martin Lindblom Supervisor : Niklas Carlsson Examiner : Markus Bendtsen Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet - eller dess framtida ersättare - under 25 år från publicer- ingsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka ko- pior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervis- ning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säker- heten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsman- nens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose. -
Steganography: Forensic, Security, and Legal Issues
Journal of Digital Forensics, Security and Law Volume 3 Number 2 Article 2 2008 Steganography: Forensic, Security, and Legal Issues Merrill Warkentin Mississippi State University Ernst Bekkering Northeastern State University Mark B. Schmidt St. Cloud State University Follow this and additional works at: https://commons.erau.edu/jdfsl Part of the Computer Engineering Commons, Computer Law Commons, Electrical and Computer Engineering Commons, Forensic Science and Technology Commons, and the Information Security Commons Recommended Citation Warkentin, Merrill; Bekkering, Ernst; and Schmidt, Mark B. (2008) "Steganography: Forensic, Security, and Legal Issues," Journal of Digital Forensics, Security and Law: Vol. 3 : No. 2 , Article 2. DOI: https://doi.org/10.15394/jdfsl.2008.1039 Available at: https://commons.erau.edu/jdfsl/vol3/iss2/2 This Article is brought to you for free and open access by the Journals at Scholarly Commons. It has been accepted for inclusion in Journal of Digital Forensics, Security and Law by an authorized administrator of (c)ADFSL Scholarly Commons. For more information, please contact [email protected]. Journal of Digital Forensics, Security and Law, Vol. 3(2) Steganography: Forensic, Security, and Legal Issues Merrill Warkentin Mississippi State University [email protected] Ernst Bekkering Northeastern State University [email protected] Mark B. Schmidt St. Cloud State University [email protected] ABSTRACT Steganography has long been regarded as a tool used for illicit and destructive purposes such as crime and warfare. Currently, digital tools are widely available to ordinary computer users also. Steganography software allows both illicit and legitimate users to hide messages so that they will not be detected in transit. -
Statistical Pattern Recognition for Audio Forensics -- Empirical
Statistical Pattern Recognition for Audio Forensics { Empirical Investigations on the Application Scenarios Audio Steganalysis and Microphone Forensics Dissertation zur Erlangung des akademischen Grades Doktoringenieur (Dr.-Ing.) angenommen durch die Fakult¨atf¨urInformatik der Otto-von-Guericke-Universit¨atMagdeburg von: Dipl.-Inform. Christian Kr¨atzer geboren am 12. Dezember 1978 in Halberstadt Gutachter: Prof. Dr. Jana Dittmann, Otto-von-Guericke Universit¨atMagdeburg, Magdeburg, Germany Prof. Dr. Stefan Katzenbeisser, Technische Universit¨atDarmstadt, Darmstadt, Germany Prof. Dr. Scott A. Craver, Binghamton University, Binghamton, NY, United States of America Magdeburg, Germany 23. Mai 2013 Abstract Media forensics is a quickly growing research field struggling to gain the same acceptance as traditional forensic investigation methods. Media forensics tasks such as proving image manipulations on digital images or audio manipulations on digital sound files are as relevant today as they were for their analogue counterparts some decades ago. The difference is that tools like Photoshop now allow a large number of people to manipulate digital media objects with a processing speed far beyond anything imaginable in times when image, audio or video manipulation of analogue media was a hardware- and labour-intensive task requiring skill and experience. Currently, there exists a large number of research prototypes but only a small number of accepted tools that are capable of answering specific questions regularly arising in court cases, such as the origin (source authenticity) of an image or recording, or the integrity of a media file. The reason for this discrepancy between the number of research prototypes and the number of solutions accepted in court has to be sought in the very nature of most judicial systems: Judges have to distinguish between valid bases for evidence and expert testimonies on the one hand, and `junk science' on the other hand.