Examples of Spamming on the Internet
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Schwab's Fraud Encyclopedia
Fraud Encyclopedia Updated June 2018 Contents Introduction . 3 Scams . 26 How to use this Fraud Encyclopedia . 3 1 . Properties . 28 2. Romance/marriage/sweetheart/catfishing . 28 Email account takeover . 4 3 . Investments/goods/services . .. 29 1 . Emotion . 7 4 . Prizes/lotteries . 29 2 . Unavailability . 7 5 . IRS . 30 3 . Fee inquiries . 8 6 . Payments . 30 4 . Attachments . 9 Other cybercrime techniques . 31 5 . International wires . 10 1 . Malware . 33 6 . Language cues . 10 2 . Wi-Fi connection interception . 34 7 . Business email compromise . 11 3 . Data breaches . 35 Client impersonation and identity theft . 12 4 . Credential replay incident (CRI) . 37 1 . Social engineering . 14 5 . Account online compromise/takeover . 37 2. Shoulder surfing . 14 6 . Distributed denial of service (DDoS) attack . 38 3. Spoofing . 15 Your fraud checklist . 39 4 . Call forwarding and porting . 16 Email scrutiny . 39 5 . New account fraud . 16 Verbally confirming client requests . 40 Identical or first-party disbursements . 17 Safe cyber practices . 41 1 . MoneyLink fraud . 19 What to do if fraud is suspected . 42 2 . Wire fraud . .. 19 Schwab Advisor Center® alerts . 43 3 . Check fraud . 20 4 . Transfer of account (TOA) fraud . 20 Phishing . 21 1 . Spear phishing . 23 2 . Whaling . .. 24 3 . Clone phishing . 24 4 . Social media phishing . 25 CONTENTS | 2 Introduction With advances in technology, we are more interconnected than ever . But we’re also more vulnerable . Criminals can exploit the connectivity of our world and use it to their advantage—acting anonymously How to use this to perpetrate fraud in a variety of ways . Fraud Encyclopedia Knowledge and awareness can help you protect your firm and clients and guard against cybercrime. -
Spamming Botnets: Signatures and Characteristics
Spamming Botnets: Signatures and Characteristics Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten+,IvanOsipkov+ Microsoft Research, Silicon Valley +Microsoft Corporation {yxie,fangyu,kachan,rina,ghulten,ivano}@microsoft.com ABSTRACT botnet infection and their associated control process [4, 17, 6], little In this paper, we focus on characterizing spamming botnets by effort has been devoted to understanding the aggregate behaviors of leveraging both spam payload and spam server traffic properties. botnets from the perspective of large email servers that are popular Towards this goal, we developed a spam signature generation frame- targets of botnet spam attacks. work called AutoRE to detect botnet-based spam emails and botnet An important goal of this paper is to perform a large scale analy- membership. AutoRE does not require pre-classified training data sis of spamming botnet characteristics and identify trends that can or white lists. Moreover, it outputs high quality regular expression benefit future botnet detection and defense mechanisms. In our signatures that can detect botnet spam with a low false positive rate. analysis, we make use of an email dataset collected from a large Using a three-month sample of emails from Hotmail, AutoRE suc- email service provider, namely, MSN Hotmail. Our study not only cessfully identified 7,721 botnet-based spam campaigns together detects botnet membership across the Internet, but also tracks the with 340,050 unique botnet host IP addresses. sending behavior and the associated email content patterns that are Our in-depth analysis of the identified botnets revealed several directly observable from an email service provider. Information interesting findings regarding the degree of email obfuscation, prop- pertaining to botnet membership can be used to prevent future ne- erties of botnet IP addresses, sending patterns, and their correlation farious activities such as phishing and DDoS attacks. -
A Fast Unsupervised Social Spam Detection Method for Trending Topics
Information Quality in Online Social Networks: A Fast Unsupervised Social Spam Detection Method for Trending Topics Mahdi Washha1, Dania Shilleh2, Yara Ghawadrah2, Reem Jazi2 and Florence Sedes1 1IRIT Laboratory, University of Toulouse, Toulouse, France 2Department of Electrical and Computer Engineering, Birzeit University, Ramallah, Palestine Keywords: Twitter, Social Networks, Spam. Abstract: Online social networks (OSNs) provide data valuable for a tremendous range of applications such as search engines and recommendation systems. However, the easy-to-use interactive interfaces and the low barriers of publications have exposed various information quality (IQ) problems, decreasing the quality of user-generated content (UGC) in such networks. The existence of a particular kind of ill-intentioned users, so-called social spammers, imposes challenges to maintain an acceptable level of information quality. Social spammers sim- ply misuse all services provided by social networks to post spam contents in an automated way. As a natural reaction, various detection methods have been designed, which inspect individual posts or accounts for the existence of spam. These methods have a major limitation in exploiting the supervised learning approach in which ground truth datasets are required at building model time. Moreover, the account-based detection met- hods are not practical for processing ”crawled” large collections of social posts, requiring months to process such collections. Post-level detection methods also have another drawback in adapting the dynamic behavior of spammers robustly, because of the weakness of the features of this level in discriminating among spam and non-spam tweets. Hence, in this paper, we introduce a design of an unsupervised learning approach dedicated for detecting spam accounts (or users) existing in large collections of Twitter trending topics. -
Zambia and Spam
ZAMNET COMMUNICATION SYSTEMS LTD (ZAMBIA) Spam – The Zambian Experience Submission to ITU WSIS Thematic meeting on countering Spam By: Annabel S Kangombe – Maseko June 2004 Table of Contents 1.0 Introduction 1 1.1 What is spam? 1 1.2 The nature of Spam 1 1.3 Statistics 2 2.0 Technical view 4 2.1 Main Sources of Spam 4 2.1.1 Harvesting 4 2.1.2 Dictionary Attacks 4 2.1.3 Open Relays 4 2.1.4 Email databases 4 2.1.5 Inadequacies in the SMTP protocol 4 2.2 Effects of Spam 5 2.3 The fight against spam 5 2.3.1 Blacklists 6 2.3.2 White lists 6 2.3.3 Dial‐up Lists (DUL) 6 2.3.4 Spam filtering programs 6 2.4 Challenges of fighting spam 7 3.0 Legal Framework 9 3.1 Laws against spam in Zambia 9 3.2 International Regulations or Laws 9 3.2.1 US State Laws 9 3.2.2 The USA’s CAN‐SPAM Act 10 4.0 The Way forward 11 4.1 A global effort 11 4.2 Collaboration between ISPs 11 4.3 Strengthening Anti‐spam regulation 11 4.4 User education 11 4.5 Source authentication 12 4.6 Rewriting the Internet Mail Exchange protocol 12 1.0 Introduction I get to the office in the morning, walk to my desk and switch on the computer. One of the first things I do after checking the status of the network devices is to check my email. -
Analysis and Detection of Low Quality Information in Social Networks
ANALYSIS AND DETECTION OF LOW QUALITY INFORMATION IN SOCIAL NETWORKS A Thesis Presented to The Academic Faculty by De Wang In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computer Science at the College of Computing Georgia Institute of Technology August 2014 Copyright c 2014 by De Wang ANALYSIS AND DETECTION OF LOW QUALITY INFORMATION IN SOCIAL NETWORKS Approved by: Professor Dr. Calton Pu, Advisor Professor Dr. Edward R. Omiecinski School of Computer Science School of Computer Science at the College of Computing at the College of Computing Georgia Institute of Technology Georgia Institute of Technology Professor Dr. Ling Liu Professor Dr. Kang Li School of Computer Science Department of Computer Science at the College of Computing University of Georgia Georgia Institute of Technology Professor Dr. Shamkant B. Navathe Date Approved: April, 21 2014 School of Computer Science at the College of Computing Georgia Institute of Technology To my family and all those who have supported me. iii ACKNOWLEDGEMENTS When I was a little boy, my parents always told me to study hard and learn more in school. But I never thought that I would study abroad and earn a Ph.D. degree at United States at that time. The journey to obtain a Ph.D. is quite long and full of peaks and valleys. Fortunately, I have received great helps and guidances from many people through the journey, which is also the source of motivation for me to move forward. First and foremost I should give thanks to my advisor, Prof. Calton Pu. -
Towards Mitigating Unwanted Calls in Voice Over IP
FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO Towards Mitigating Unwanted Calls in Voice Over IP Muhammad Ajmal Azad Programa Doutoral em Engenharia Electrotécnica e de Computadores Supervisor: Ricardo Santos Morla June 2016 c Muhammad Ajmal Azad, 2016 Towards Mitigating Unwanted Calls in Voice Over IP Muhammad Ajmal Azad Programa Doutoral em Engenharia Electrotécnica e de Computadores June 2016 I Dedicate This Thesis To My Parents and Wife For their endless love, support and encouragement. i Acknowledgments First and foremost, I would like to express my special gratitude and thanks to my advisor, Professor Dr. Ricardo Santos Morla for his continuous support, supervision and time. His suggestions, advice and criticism on my work have helped me a lot from finding a problem, design a solution and analyzing the solution. I am forever grateful to Dr. Morla for mentoring and helping me throughout the course of my doctoral research.. I would like to thanks my friends Dr. Arif Ur Rahman and Dr. Farhan Riaz for helping in understanding various aspects of research at the start of my Ph.D, Asif Mohammad for helping me in coding with Java, and Bilal Hussain for constructive debate other than academic research and continuous encouragements in the last three years. Of course acknowledgments are incomplete without thanking my parents, family members and loved ones. I am very thankful to my parents for spending on my education despite limited resources. They taught me about hard work, make me to study whenever I run away, encourage me to achieve the goals, self-respect and always encourage me for doing what i want. -
Enisa Etl2020
EN From January 2019 to April 2020 Spam ENISA Threat Landscape Overview The first spam message was sent in 1978 by a marketing manager to 393 people via ARPANET. It was an advertising campaign for a new product from the company he worked for, the Digital Equipment Corporation. For those first 393 spammed people it was as annoying as it would be today, regardless of the novelty of the idea.1 Receiving spam is an inconvenience, but it may also create an opportunity for a malicious actor to steal personal information or install malware.2 Spam consists of sending unsolicited messages in bulk. It is considered a cybersecurity threat when used as an attack vector to distribute or enable other threats. Another noteworthy aspect is how spam may sometimes be confused or misclassified as a phishing campaign. The main difference between the two is the fact that phishing is a targeted action using social engineering tactics, actively aiming to steal users’ data. In contrast spam is a tactic for sending unsolicited e-mails to a bulk list. Phishing campaigns can use spam tactics to distribute messages while spam can link the user to a compromised website to install malware and steal personal data. Spam campaigns, during these last 41 years have taken advantage of many popular global social and sports events such as UEFA Europa League Final, US Open, among others. Even so, nothing compared with the spam activity seen this year with the COVID-19 pandemic.8 2 __Findings 85%_of all e-mails exchanged in April 2019 were spam, a 15-month high1 14_million -
Locating Spambots on the Internet
BOTMAGNIFIER: Locating Spambots on the Internet Gianluca Stringhinix, Thorsten Holzz, Brett Stone-Grossx, Christopher Kruegelx, and Giovanni Vignax xUniversity of California, Santa Barbara z Ruhr-University Bochum fgianluca,bstone,chris,[email protected] [email protected] Abstract the world-wide email traffic [20], and a lucrative busi- Unsolicited bulk email (spam) is used by cyber- ness has emerged around them [12]. The content of spam criminals to lure users into scams and to spread mal- emails lures users into scams, promises to sell cheap ware infections. Most of these unwanted messages are goods and pharmaceutical products, and spreads mali- sent by spam botnets, which are networks of compro- cious software by distributing links to websites that per- mised machines under the control of a single (malicious) form drive-by download attacks [24]. entity. Often, these botnets are rented out to particular Recent studies indicate that, nowadays, about 85% of groups to carry out spam campaigns, in which similar the overall spam traffic on the Internet is sent with the mail messages are sent to a large group of Internet users help of spamming botnets [20,36]. Botnets are networks in a short amount of time. Tracking the bot-infected hosts of compromised machines under the direction of a sin- that participate in spam campaigns, and attributing these gle entity, the so-called botmaster. While different bot- hosts to spam botnets that are active on the Internet, are nets serve different, nefarious goals, one important pur- challenging but important tasks. In particular, this infor- pose of botnets is the distribution of spam emails. -
Towards Detecting Compromised Accounts on Social Networks
Towards Detecting Compromised Accounts on Social Networks Manuel Egeley, Gianluca Stringhinix, Christopher Kruegelz, and Giovanni Vignaz yBoston University xUniversity College London zUC Santa Barbara [email protected], [email protected], fchris,[email protected] Abstract Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable – they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons. 1 Introduction phishing web sites [2]. Such traditional attacks are best carried out through a large population of compromised accounts Online social networks, such as Facebook and Twitter, have belonging to regular social network account users. Recent become one of the main media to stay in touch with the rest incidents, however, demonstrate that attackers can cause havoc of the world. -
The History of Spam Timeline of Events and Notable Occurrences in the Advance of Spam
The History of Spam Timeline of events and notable occurrences in the advance of spam July 2014 The History of Spam The growth of unsolicited e-mail imposes increasing costs on networks and causes considerable aggravation on the part of e-mail recipients. The history of spam is one that is closely tied to the history and evolution of the Internet itself. 1971 RFC 733: Mail Specifications 1978 First email spam was sent out to users of ARPANET – it was an ad for a presentation by Digital Equipment Corporation (DEC) 1984 Domain Name System (DNS) introduced 1986 Eric Thomas develops first commercial mailing list program called LISTSERV 1988 First know email Chain letter sent 1988 “Spamming” starts as prank by participants in multi-user dungeon games by MUDers (Multi User Dungeon) to fill rivals accounts with unwanted electronic junk mail. 1990 ARPANET terminates 1993 First use of the term spam was for a post from USENET by Richard Depew to news.admin.policy, which was the result of a bug in a software program that caused 200 messages to go out to the news group. The term “spam” itself was thought to have come from the spam skit by Monty Python's Flying Circus. In the sketch, a restaurant serves all its food with lots of spam, and the waitress repeats the word several times in describing how much spam is in the items. When she does this, a group of Vikings in the corner start a song: "Spam, spam, spam, spam, spam, spam, spam, spam, lovely spam! Wonderful spam!" Until told to shut up. -
Adversarial Web Search by Carlos Castillo and Brian D
Foundations and TrendsR in Information Retrieval Vol. 4, No. 5 (2010) 377–486 c 2011 C. Castillo and B. D. Davison DOI: 10.1561/1500000021 Adversarial Web Search By Carlos Castillo and Brian D. Davison Contents 1 Introduction 379 1.1 Search Engine Spam 380 1.2 Activists, Marketers, Optimizers, and Spammers 381 1.3 The Battleground for Search Engine Rankings 383 1.4 Previous Surveys and Taxonomies 384 1.5 This Survey 385 2 Overview of Search Engine Spam Detection 387 2.1 Editorial Assessment of Spam 387 2.2 Feature Extraction 390 2.3 Learning Schemes 394 2.4 Evaluation 397 2.5 Conclusions 400 3 Dealing with Content Spam and Plagiarized Content 401 3.1 Background 402 3.2 Types of Content Spamming 405 3.3 Content Spam Detection Methods 405 3.4 Malicious Mirroring and Near-Duplicates 408 3.5 Cloaking and Redirection 409 3.6 E-mail Spam Detection 413 3.7 Conclusions 413 4 Curbing Nepotistic Linking 415 4.1 Link-Based Ranking 416 4.2 Link Bombs 418 4.3 Link Farms 419 4.4 Link Farm Detection 421 4.5 Beyond Detection 424 4.6 Combining Links and Text 426 4.7 Conclusions 429 5 Propagating Trust and Distrust 430 5.1 Trust as a Directed Graph 430 5.2 Positive and Negative Trust 432 5.3 Propagating Trust: TrustRank and Variants 433 5.4 Propagating Distrust: BadRank and Variants 434 5.5 Considering In-Links as well as Out-Links 436 5.6 Considering Authorship as well as Contents 436 5.7 Propagating Trust in Other Settings 437 5.8 Utilizing Trust 438 5.9 Conclusions 438 6 Detecting Spam in Usage Data 439 6.1 Usage Analysis for Ranking 440 6.2 Spamming Usage Signals 441 6.3 Usage Analysis to Detect Spam 444 6.4 Conclusions 446 7 Fighting Spam in User-Generated Content 447 7.1 User-Generated Content Platforms 448 7.2 Splogs 449 7.3 Publicly-Writable Pages 451 7.4 Social Networks and Social Media Sites 455 7.5 Conclusions 459 8 Discussion 460 8.1 The (Ongoing) Struggle Between Search Engines and Spammers 460 8.2 Outlook 463 8.3 Research Resources 464 8.4 Conclusions 467 Acknowledgments 468 References 469 Foundations and TrendsR in Information Retrieval Vol. -
Detecting Social Spam Campaigns on Twitter
Detecting Social Spam Campaigns on Twitter Zi Chu1, Indra Widjaja2, and Haining Wang1 1 Department of Computer Science, The College of William and Mary, Williamsburg, VA 23187, USA {zichu,hnw}@cs.wm.edu 2 Bell Laboratories, Alcatel-Lucent, Murray Hill, NJ 07974, USA [email protected] Abstract. The popularity of Twitter greatly depends on the quality and integrity of contents contributed by users. Unfortunately, Twitter has at- tracted spammers to post spam content which pollutes the community. Social spamming is more successful than traditional methods such as email spamming by using social relationship between users. Detecting spam is the first and very critical step in the battle of fighting spam. Conventional detection methods check individual messages or accounts for the existence of spam. Our work takes the collective perspective, and focuses on detecting spam campaigns that manipulate multiple accounts to spread spam on Twitter. Complementary to conventional detection methods, our work brings efficiency and robustness. More specifically, we design an automatic classification system based on machine learning, and apply multiple features for classifying spam campaigns. Our experi- mental evaluation demonstrates the efficacy of the proposed classification system. Keywords: Spam Detection, Anomaly Detection, Machine Learning, Twitter 1 Introduction With the tremendous popularity of online social networks (OSNs), spammers have exploited them for spreading spam messages. Social spamming is more successful than traditional methods such as email spamming by taking advantage of social relationship between users. One important reason is that OSNs help build intrinsic trust relationship between cyber friends even though they may not know each other in reality.