http://repository.iiitd.edu.in/xmlui/handle/123456789/65 Sat, 04 Apr 2026 07:18:42 GMT 2026-04-04T07:18:42Z http://repository.iiitd.edu.in/xmlui/handle/123456789/110 PhishAri : automatic realtime phishing detection on Twitter Aggarwal, Anupama; Kumaraguru, Ponnurangam (Advisor) With the advent of online social media, phishers have started using social networks like Twitter, Facebook, and Foursquare to spread phishing scams. Twitter is an immensely popular micro- blogging network where people post short messages of 140 characters called tweets. It has over 100 million active users who post about 200 million tweets everyday. Phishers have started using Twitter as a medium to spread phishing because of this vast information dissemination. Due to constraints of limited text space in social systems like Twitter, phishers have begun to use URL shortener services. In this study, we rst provide an overview of phishing attacks for this new scenario. One of our main conclusions was that phishers use URL shorteners not only for reducing space but also to hide their identity. We also observed that social media websites like Facebook, Habbo, Orkut are competing with e-commerce services like PayPal, eBay in terms of tra c and focus of phishers. 1 Further, it is di cult to detect phishing on Twitter unlike emails because of the quick spread of phishing links in the network, short size of the content, and use of URL obfuscation to shorten the URL. We developed a technique, PhishAri, 2 which detects phishing on Twitter in realtime. We use Twitter speci c features along with URL features to detect whether a tweet posted with a URL is phishing or not. Some of the Twitter speci c features we used are tweet content and its characteristics like length, hashtags, and mentions. Other Twitter features used are the characteristics of the Twitter user posting the tweet such as age of the account, number of tweets, and the follower-followee ratio. These TTwitterwitter speci c features coupled with URL based features proved to be a strong mechanism to detect phishing tweets. We used machine learning classi cation techniques and detected phishing tweets with an accuracy of 92.52%. We deployed our system for end-users by providing an easy to use Chrome browser extension. The extension works in realtime and classi es a tweet as phishing or safe. In this research, we showed that we were able to detect phishing tweets at zero hour with high accuracy which is much faster than public blacklists and as well as Twitter's own defense mechanism to detect malicious content. We also performed a quick user evaluation of PhishAri in a laboratory study to evaluate the usability and e ectiveness of PhishAri and showed that users like and nd it convenient to use PhishAri in real-world. Currently, there are 74 active users of PhishAri chrome extension. To the best of our knowledge, this is the rst realtime, comprehensive and usable system to detect phishing on Twitter. Fri, 11 Oct 2013 11:58:11 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/110 2013-10-11T11:58:11Z http://repository.iiitd.edu.in/xmlui/handle/123456789/77 WhACKY! - what anyone could know about you from twitter Correa, Denzil; Sureka, Ashish (Advisor) Twitter is a popular micro-blogging website which allows users to post 140-character limit mes- sages called tweets. Twitter users (also called Twitterers) post activity messages about their daily lives, opinions on current events and news, and even have conversations with other users. In addition, Twitterers also share various other information like photographs, videos and visited locations hosted on other external services like Flickr, YouTube and Foursquare. Therefore, tweets contain variety of information obtained from a combination of multiple sources. We demonstrate a cheap and elegant solution { WhACKY! { to harness this multi-source informa- tion to link Twitter pro les across other external services. In particular, we exploit activity feed sharing patterns to map Twitter pro les to their corresponding external service accounts using publicly available APIs. We illustrate a proof-of-concept by mapping 69,496 Twitter pro les to at least one of the ve popular external services : Flickr (photo-sharing service), Foursquare (location-based service), YouTube (video-sharing service), Facebook (a popular social network) and LastFM (music-sharing service). We evaluate our solution against a commercial social iden- tity mapping service { FlipTop { and demonstrate the e ciency of our approach. WhACKY! guarantees that the mapped pro les are 100% true-positive and helps quantify the unintended leakage of Personally Identi able Information (PII) attributes. During the process, WhACKY! is also able to detect duplicate Twitter pro les connected to multiple external services.We de- velop a web application based on WhACKY!1 for perusal by Twitterers which can help them better understand unintended leakage of their PII. Wed, 06 Feb 2013 10:35:23 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/77 2013-02-06T10:35:23Z http://repository.iiitd.edu.in/xmlui/handle/123456789/70 Robust and traffic analysis resistant cloud file system Gupta, Madhvi; Nagaraja, Shishir (Advisor) With improved technology, every user now generates huge amount of data that requires ever increasing amount of space to store it. It is not economical for the users to purchase new storage device every time. As a remedy to this problem di erent service provider o ers storage space that can be utilized by the users. Cloud computing also provides a similar functionality to the users for storing their data over the Internet. The invention of cloud computing has ful lled the need for extra storage space but at the same time it has also lead to increased concerns regarding security of data stored over the Internet. The responsibility of the security of this huge amount of data lies with the service provider, but there may arise a situation where user may not trust the service provider due to the sensitivity of data. So there is a need of a technique which provide users a level of trust and security. In Document security solution (DSSol), we have tried to deal with such a situation, where we provide protection against adversary which can also be the service provider itself. We have used the Steganographic le system along with encryption to protect the document uploaded over the Google Docs from any security breach. The design encrypts the data les to be uploaded and divides them in equal sized chunks which gets uploaded along with some additional dummy chunks to the Goggle Docs. This provides unobservability, tra c analysis resistance and thereby providing protection against attacks possible due to tra c analysis and usage patterns. Wed, 17 Oct 2012 06:31:32 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/70 2012-10-17T06:31:32Z http://repository.iiitd.edu.in/xmlui/handle/123456789/66 Rebound attachs on GRφSTL Kochar, Komal; Sanadhya, Somitra Kumar (Advisor) Cryptographic hash Functions are widely used for a wide range of applications such as au- thentication of information, digital signatures and protection of pass-phrases. In the last few years, the cryptanalysis of hash functions has gained much importance within the cryp- tographic community. In 2004 a series of attacks by Wang et al. [19, 20] have exposed security vulnerabilities in the design of the most widely deployed SHA-1 hash function. As a result, the US National Institute for Standards and Technology (NIST) recommended the replacement of SHA-1 by the SHA-2 hash function family and in 2008, they announced a call for the design of a new SHA-3 hashing algorithm. On October 31, 2008, the “SHA-3 competition”, organised by the National Institute of Standards and Technology (NIST), was launched [17]. 64 algorithms were submitted, out of which, 51 were accepted for the first round of the competition. On July 24, 2009, 14 candidates were chosen by NIST to advance to the second round of the competition. One of the candidates accepted for the second round is called Grφstl [11], developed by Praveen Gauravaram, Lars R. Knudsen and Krystian Matusiewicz. Grφstl further advanced to the final round along with BLAKE [2], JH, Keccak [3], Skein [10] and became one of the top 5 proposals for SHA-3. The report breifly specifies the Grφstl family of cryptographic hash algorithms, one of the top 5 finalists of the SHA-3 hash function competition and a well known attack named Rebound Attack on Grφstl. The rebound attack is a freedom degrees utilization technique that was first proposed by Mendel et al. in [15] as an analysis of round-reduced Grφstl and Whirlpool [18]. The main idea of the rebound attack is to use the available degrees of freedom in a collision attack to effeciently bypass the low probability parts of a truncated differential trail. The rebound attack consists of an inbound phase with a match-in-the-middle part to exploit the available degrees of freedom, followed by a subsequent probabilistic outbound phase. Report discusses available rebound attacks on reduced rounds of Grφstl-256. The report first describes a simple method to utilize the available freedom degrees. The original idea of rebound is then applied to reduced rounds of Grφstl- 256. Report describes attack on 4 rounds of Grφstl-256. It further explains same rebound technique applied on 5 and 6 rounds Grφstl-256. The new technique Super Sbox Cryptanalysis [12] introduced by Thomas Peyrin and Henri Gilbert is explained in the report alongwith its application on 7 rounds of Grφstl-256. Wed, 25 Jul 2012 12:22:05 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/66 2012-07-25T12:22:05Z