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Technical and Legal Approaches to Unsolicited Electronic Mail, 35 USFL Rev
UIC School of Law UIC Law Open Access Repository UIC Law Open Access Faculty Scholarship 1-1-2001 Technical and Legal Approaches to Unsolicited Electronic Mail, 35 U.S.F. L. Rev. 325 (2001) David E. Sorkin John Marshall Law School, [email protected] Follow this and additional works at: https://repository.law.uic.edu/facpubs Part of the Computer Law Commons, Internet Law Commons, Marketing Law Commons, and the Privacy Law Commons Recommended Citation David E. Sorkin, Technical and Legal Approaches to Unsolicited Electronic Mail, 35 U.S.F. L. Rev. 325 (2001). https://repository.law.uic.edu/facpubs/160 This Article is brought to you for free and open access by UIC Law Open Access Repository. It has been accepted for inclusion in UIC Law Open Access Faculty Scholarship by an authorized administrator of UIC Law Open Access Repository. For more information, please contact [email protected]. Technical and Legal Approaches to Unsolicited Electronic Mailt By DAVID E. SORKIN* "Spamming" is truly the scourge of the Information Age. This problem has become so widespread that it has begun to burden our information infrastructure. Entire new networks have had to be constructed to deal with it, when resources would be far better spent on educational or commercial needs. United States Senator Conrad Burns (R-MT)1 UNSOLICITED ELECTRONIC MAIL, also called "spain," 2 causes or contributes to a wide variety of problems for network administrators, t Copyright © 2000 David E. Sorkin. * Assistant Professor of Law, Center for Information Technology and Privacy Law, The John Marshall Law School; Visiting Scholar (1999-2000), Center for Education and Research in Information Assurance and Security (CERIAS), Purdue University. -
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. -
Spambots: Creative Deception
PATTERNS 2020 : The Twelfth International Conference on Pervasive Patterns and Applications Spambots: Creative Deception Hayam Alamro Costas S. Iliopoulos Department of Informatics Department of Informatics King’s College London, UK King’s College London, UK Department of Information Systems email: costas.iliopoulos Princess Nourah bint Abdulrahman University @kcl.ac.uk Riyadh, KSA email: [email protected] Abstract—In this paper, we present our spambot overview on the intent of building mailing lists to send unsolicited or phishing creativity of the spammers, and the most important techniques emails. Furthermore, spammers can create fake accounts to that the spammer might use to deceive current spambot detection target specific websites or domain specific users and start send- tools to bypass detection. These techniques include performing a ing predefined designed actions which are known as scripts. series of malicious actions with variable time delays, repeating the Moreover, spammers can work on spreading malwares to steal same series of malicious actions multiple times, and interleaving other accounts or scan the web to obtain customers contact legitimate and malicious actions. In response, we define our problems to detect the aforementioned techniques in addition information to carry out credential stuffing attacks, which is to disguised and "don’t cares" actions. Our proposed algorithms mainly used to login to another unrelated service. In addition, to solve those problems are based on advanced data structures spambots can be designed to participate in deceiving users on that are able to detect malicious actions efficiently in linear time, online social networks through the spread of fake news, for and in an innovative way. -
Address Munging: the Practice of Disguising, Or Munging, an E-Mail Address to Prevent It Being Automatically Collected and Used
Address Munging: the practice of disguising, or munging, an e-mail address to prevent it being automatically collected and used as a target for people and organizations that send unsolicited bulk e-mail address. Adware: or advertising-supported software is any software package which automatically plays, displays, or downloads advertising material to a computer after the software is installed on it or while the application is being used. Some types of adware are also spyware and can be classified as privacy-invasive software. Adware is software designed to force pre-chosen ads to display on your system. Some adware is designed to be malicious and will pop up ads with such speed and frequency that they seem to be taking over everything, slowing down your system and tying up all of your system resources. When adware is coupled with spyware, it can be a frustrating ride, to say the least. Backdoor: in a computer system (or cryptosystem or algorithm) is a method of bypassing normal authentication, securing remote access to a computer, obtaining access to plaintext, and so on, while attempting to remain undetected. The backdoor may take the form of an installed program (e.g., Back Orifice), or could be a modification to an existing program or hardware device. A back door is a point of entry that circumvents normal security and can be used by a cracker to access a network or computer system. Usually back doors are created by system developers as shortcuts to speed access through security during the development stage and then are overlooked and never properly removed during final implementation. -
A Visual Analytics Toolkit for Social Spambot Labeling
© 2019 IEEE. This is the author’s version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: 10.1109/TVCG.2019.2934266 VASSL: A Visual Analytics Toolkit for Social Spambot Labeling Mosab Khayat, Morteza Karimzadeh, Jieqiong Zhao, David S. Ebert, Fellow, IEEE G A H B C E I D F Fig. 1. The default layout of the front-end of VASSL: A) the timeline view, B) the dimensionality reduction view, C) the user/tweet detail views, D) & E) the topics view (clustering / words), F) the feature explorer view, G) the general control panel, H) the labeling panel, and I) the control panels for all the views (the opened control panel in the figure is for topics clustering view). Abstract—Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool. -
Detection of Spambot Groups Through DNA-Inspired Behavioral Modeling
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 1 Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi Abstract—Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics. Index Terms—Spambot detection, social bots, online social networks, Twitter, behavioral modeling, digital DNA. -
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. -
Battling the Internet Water Army: Detection of Hidden Paid Posters
Battling the Internet Water Army: Detection of Hidden Paid Posters Cheng Chen Kui Wu Venkatesh Srinivasan Xudong Zhang Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science University of Victoria University of Victoria University of Victoria Peking University Victoria, BC, Canada Victoria, BC, Canada Victoria, BC, Canada Beijing, China Abstract—We initiate a systematic study to help distinguish on different online communities and websites. Companies are a special group of online users, called hidden paid posters, or always interested in effective strategies to attract public atten- termed “Internet water army” in China, from the legitimate tion towards their products. The idea of online paid posters ones. On the Internet, the paid posters represent a new type of online job opportunity. They get paid for posting comments is similar to word-of-mouth advertisement. If a company hires and new threads or articles on different online communities enough online users, it would be able to create hot and trending and websites for some hidden purposes, e.g., to influence the topics designed to gain popularity. Furthermore, the articles opinion of other people towards certain social events or business or comments from a group of paid posters are also likely markets. Though an interesting strategy in business marketing, to capture the attention of common users and influence their paid posters may create a significant negative effect on the online communities, since the information from paid posters is usually decision. In this way, online paid posters present a powerful not trustworthy. When two competitive companies hire paid and efficient strategy for companies. -
Understanding Local Health Department Twitter Followership
JOURNAL OF MEDICAL INTERNET RESEARCH Harris et al Original Paper Are Public Health Organizations Tweeting to the Choir? Understanding Local Health Department Twitter Followership Jenine K Harris1, PhD; Bechara Choucair2,3, MD; Ryan C Maier4, BA; Nina Jolani5, BA; Jay M Bernhardt6, MPH, PhD 1Brown School, Washington University in St Louis, St Louis, MO, United States 2Chicago Department of Public Health, Chicago, IL, United States 3Feinberg School of Medicine, Northwestern University, Chicago, IL, United States 4Center for Public Health Systems Science, Washington University in St Louis, St Louis, MO, United States 5National Association of County and City Health Officials, Washington DC, DC, United States 6Center for Digital Health and Wellness, College of Health and Human Performance, University of Florida, Gainesville, FL, United States Corresponding Author: Jenine K Harris, PhD Brown School Washington University in St Louis One Brookings Drive Campus Box 1196 St Louis, MO, 63130 United States Phone: 1 3149353522 Fax: 1 3149353756 Email: [email protected] Abstract Background: One of the essential services provided by the US local health departments is informing and educating constituents about health. Communication with constituents about public health issues and health risks is among the standards required of local health departments for accreditation. Past research found that only 61% of local health departments met standards for informing and educating constituents, suggesting a considerable gap between current practices and best practice. Objective: Social media platforms, such as Twitter, may aid local health departments in informing and educating their constituents by reaching large numbers of people with real-time messages at relatively low cost. Little is known about the followers of local health departments on Twitter. -
The History of Digital Spam
The History of Digital Spam Emilio Ferrara University of Southern California Information Sciences Institute Marina Del Rey, CA [email protected] ACM Reference Format: This broad definition will allow me to track, in an inclusive Emilio Ferrara. 2019. The History of Digital Spam. In Communications of manner, the evolution of digital spam across its most popular appli- the ACM, August 2019, Vol. 62 No. 8, Pages 82-91. ACM, New York, NY, USA, cations, starting from spam emails to modern-days spam. For each 9 pages. https://doi.org/10.1145/3299768 highlighted application domain, I will dive deep to understand the nuances of different digital spam strategies, including their intents Spam!: that’s what Lorrie Faith Cranor and Brian LaMacchia ex- and catalysts and, from a technical standpoint, how they are carried claimed in the title of a popular call-to-action article that appeared out and how they can be detected. twenty years ago on Communications of the ACM [10]. And yet, Wikipedia provides an extensive list of domains of application: despite the tremendous efforts of the research community over the last two decades to mitigate this problem, the sense of urgency ``While the most widely recognized form of spam is email spam, the term is applied to similar abuses in other media: instant remains unchanged, as emerging technologies have brought new messaging spam, Usenet newsgroup spam, Web search engine spam, dangerous forms of digital spam under the spotlight. Furthermore, spam in blogs, wiki spam, online classified ads spam, mobile when spam is carried out with the intent to deceive or influence phone messaging spam, Internet forum spam, junk fax at scale, it can alter the very fabric of society and our behavior. -
Supervised Machine Learning Bot Detection Techniques to Identify
SMU Data Science Review Volume 1 | Number 2 Article 5 2018 Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Phillip George Efthimion Southern Methodist University, [email protected] Scott aP yne Southern Methodist University, [email protected] Nicholas Proferes University of Kentucky, [email protected] Follow this and additional works at: https://scholar.smu.edu/datasciencereview Part of the Theory and Algorithms Commons Recommended Citation Efthimion, hiP llip George; Payne, Scott; and Proferes, Nicholas (2018) "Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots," SMU Data Science Review: Vol. 1 : No. 2 , Article 5. Available at: https://scholar.smu.edu/datasciencereview/vol1/iss2/5 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Efthimion et al.: Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Phillip G. Efthimion1, Scott Payne1, Nick Proferes2 1Master of Science in Data Science, Southern Methodist University 6425 Boaz Lane, Dallas, TX 75205 {pefthimion, mspayne}@smu.edu [email protected] Abstract. In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns. -
Glossary of Spam Terms
white paper Glossary of Spam terms The jargon of The spam indusTry table of Contents A Acceptable Use Policy (AUP) . 5 Alias . 5 Autoresponder . 5 B Ban on Spam . 5 Bayesian Filtering . 5 C CAN-SPAM . 5 Catch Rate . 5 CAUSe . 5 Challenge Response Authentication . 6 Checksum Database . 6 Click-through . 6 Content Filtering . 6 Crawler . 6 D Denial of Service (DoS) . 6 Dictionary Attack . 6 DNSBL . 6 e eC Directive . 7 e-mail Bomb . 7 exploits Block List (XBL) (from Spamhaus org). 7 F False Negative . 7 False Positive . 7 Filter Scripting . 7 Fingerprinting . 7 Flood . 7 h hacker . 8 header . 8 heuristic Filtering . 8 honeypot . 8 horizontal Spam . 8 i internet Death Penalty . 8 internet Service Provider (iSP) . 8 J Joe Job . 8 K Keyword Filtering . 9 Landing Page . 9 LDAP . 9 Listwashing . 9 M Machine-learning . 9 Mailing List . 9 Mainsleaze . 9 Malware . 9 Mung . 9 N Nigerian 419 Scam . 10 Nuke . 10 O Open Proxy . 10 Open Relay . 10 Opt-in . 10 Opt-out . 10 P Pagejacking . 10 Phishing . 10 POP3 . 11 Pump and Dump . 11 Q Quarantine . 11 R RBLs . 11 Reverse DNS . 11 ROKSO . 11 S SBL . 11 Scam . 11 Segmentation . 11 SMtP . 12 Spam . 12 Spambot . 12 Spamhaus . 12 Spamming . 12 Spamware . 12 SPewS . 12 Spider . 12 Spim . 12 Spoof . 12 Spyware . 12 t training Set . 13 trojan horse . 13 trusted Senders List . 13 U UCe . 13 w whack-A-Mole . 13 worm . 13 V Vertical Spam . 13 Z Zombie . 13 Glossary of Spam terms A acceptable use policy (AUP) A policy statement, made by an iSP, whereby the company outlines its rules and guidelines for use of the account .