Discovering Spam On Twitter

Twitter generates the majority of its revenue from advertising. Third parties pay to have their products advertised on Twitter through: tweets, accounts and trends. However, spammers can use Sybil accounts (fake accounts) [21] to advertise and avoid paying for it. Sybil accounts are highly active on Twitter performing advertising campaigns to serve their clients [5]. They aggressively try to reach a large audience to maximize their influence. These accounts have similar behavior if controlled by the same master. Most of their spam tweets include a shortened URL to trick users into clicking on it. Also, since they share resources with each other, they tend to tweet similar trending topics to attract a larger audience. However, some Sybil accounts do not spam aggressively to avoid being detected [22], rendering it difficult for traditional spam detectors to be effective in detecting low spamming Sybil accounts. In this paper, I investigate additional criteria to measure the similarity between accounts on Twitter. I propose an algorithm to define the correlation among accounts by investigating their tweeting habits and content. Given known labeled accounts by spam detectors, this approach can detect hidden accounts that are closely related to labeled accounts but are not detected by traditional spam detection approaches

SISTEM PREDIKSI SPAM ACCOUNT PADA MEDIA SOSIAL TWITTER DENGAN MENGGUNAKAN ALGORITMA C4.5

Spam merupakan masalah yang sulit dihindari di era digital saat ini. Spam dapat digambarkan sebagai sesuatu hal yang tidak diminta dengan melakukan tindakan secara berulang yang berdampak negatif bagi pengguna lain termasuk berbagai bentuk interaksi dan perilaku akun otomatis serta upaya untuk menyesatkan atau menipu pengguna. Spam dapat berupa pesan yang dikirimkan melalui email, pesan instan, blog dan bahkan dalam media sosial. Twitter merupakan salah satu media sosial yang sangat popular dalam menyebarkan informasi maupun media diskusi. Twitter memiliki 115 juta pengguna aktif dengan 58 juta tweet yang dikirim setiap harinya. Hal ini dimanfaatkan para spammer untuk menjadikan twitter sebagai media untuk melakukan kegiatan spamming. Sebanyak 9,3% tweet yang dikirimkan di media social twitter merupakan spam. Penelitian terkait tentang mendeteksi spam di Twitter telah banyak dilakukan dan mendapatkan hasil dengan akurasi yang cukup memuaskan. Pada penelitian ini, hasil akurasi dari klasifikasi akun spam dengan menggunakan algoritma C4.5 mendapatkan akurasi model sebesar 94,42% dan akurasi klasifijasi data uji sebesar 95%. Kata kunci : Spam account, Klasifikasi, Decision Tree , C4.5, Twitter Nowadays, spam is a difficult problem to avoid . Spam can be described as something not asked and do repetitive action that negatively affects other users including various forms of interaction and behavior of automatic account as well as an attempt to mislead or deceive the user. Spam can be sent via email, instant messaging, blogs and even in social media. Twitter is one of popular social media to spreading information and media discussion. Twitter has 115 million active users with 58 million tweets sent every day. This situation is exploited by spammers to make twitter as a medium for spamming activities. 9.3% of tweet posted on the Twitter is spam. Related research on detecting spam on Twitter has been done and get accuracy result that is quite satisfactory. In this study, the results of the classification accuracy of spam accounts using the C4.5 algorithm get 94.42% model's accuracy and 95% accuracy of test data classification. Keywords : : Spam account, Classification, Decision Tree , C4.5, Twitte

SPAMMER DETECTION BASED ON ACCOUNT, TWEET, AND COMMUNITY ACTIVITY ON TWITTER

Spammers are the activities of users who abuse Twitter to spread spam. Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. The collaboration makes it difficult to detect spammers' accounts. This research proposed the development of feature extraction based on hashtags and community activities for the detection of spammer accounts on Twitter. Hashtags are used by spammers to increase popularity. Community activities are used as features for the detection of spammers so as to give weight to the activities of spammers contained in a community. The experimental result shows that the proposed method got the best performance in accuracy, recall, precision and g-means with are 90.55%, 88.04%, 3.18%, and 16.74%, respectively.  The accuracy and g-mean of the proposed method can surpassed previous method with 4.23% and 14.43%. This shows that the proposed method can overcome the problem of detecting spammer on Twitter with better performance compared to state of the art

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science Track, Perth, Australia, 3-7 April, 2017