Zambia and Spam
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Show Me the Money: Characterizing Spam-Advertised Revenue
Show Me the Money: Characterizing Spam-advertised Revenue Chris Kanich∗ Nicholas Weavery Damon McCoy∗ Tristan Halvorson∗ Christian Kreibichy Kirill Levchenko∗ Vern Paxsonyz Geoffrey M. Voelker∗ Stefan Savage∗ ∗ y Department of Computer Science and Engineering International Computer Science Institute University of California, San Diego Berkeley, CA z Computer Science Division University of California, Berkeley Abstract money at all [6]. This situation has the potential to distort Modern spam is ultimately driven by product sales: policy and investment decisions that are otherwise driven goods purchased by customers online. However, while by intuition rather than evidence. this model is easy to state in the abstract, our under- In this paper we make two contributions to improving standing of the concrete business environment—how this state of affairs using measurement-based methods to many orders, of what kind, from which customers, for estimate: how much—is poor at best. This situation is unsurpris- ing since such sellers typically operate under question- • Order volume. We describe a general technique— able legal footing, with “ground truth” data rarely avail- purchase pair—for estimating the number of orders able to the public. However, absent quantifiable empiri- received (and hence revenue) via on-line store order cal data, “guesstimates” operate unchecked and can dis- numbering. We use this approach to establish rough, tort both policy making and our choice of appropri- but well-founded, monthly order volume estimates ate interventions. In this paper, we describe two infer- for many of the leading “affiliate programs” selling ence techniques for peering inside the business opera- counterfeit pharmaceuticals and software. tions of spam-advertised enterprises: purchase pair and • Purchasing behavior. -
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. -
Email Phishing for IT Providers How Phishing Emails Have Changed and How to Protect Your IT Clients
Email Phishing for IT Providers How phishing emails have changed and how to protect your IT clients 1 © 2016 Calyptix Security Corporation. All rights reserved. I [email protected] I (800) 650 – 8930 (800) 650-8930 I [email protected] Contents Introduction ............................................................................................ 2 Phishing overview .................................................................................. 3 Trends in phishing emails ...................................................................... 6 Email phishing tactics .......................................................................... 11 Steps for MSP & VARS .......................................................................... 24 Advice for your clients .......................................................................... 29 Sources .................................................................................................. 35 1 © 2016 Calyptix Security Corporation. All rights reserved. I [email protected] I (800) 650 – 8930 Introduction There are only so many ways to break into a bank. You can march through the door. You can climb through a window. You can tunnel through the floor. There is the service entrance, the employee entrance, and access on the roof. Criminals who want to rob a bank will probably use an open route – such as a side door. It’s easier than breaking down a wall. Criminals who want to break into your network face a similar challenge. They need to enter. They can look for a weakness in your -
CUB Guide to Fighting Robocalls
CitizensUtilityBoard.org 1-800-669-5556 Guide to Fighting Robocalls February 2021 The latest news on the robocall fi ght Robocalls are prerecorded messages from computer-generat- ed dialers, and Illinois is one of the nation’s hardest hit states. In early 2021, for example, the state received more than 153 million robocalls (about 57 per second) in the span of just one month. That ranked Illinois eighth in the country for these calls, according to the robocall-blocking fi rm YouMail. While there are helpful robocalls (alerting you to school closings or when a prescription is ready), YouMail estimates about 42 percent of the calls in Illinois were scams and another 22 percent were simply marketing pitches. Unwanted robocalls are annoying, and costly. The Federal Com- munications Commission (FCC) put the price tag at $3 billion a year just from lost time, not even counting any fraud. TechRe- One in 10 Americans public put the total annual loss for consumers at $9.5 billion. are scammed each year, While policymakers are fi nally starting to act against illegal robo- calls, don’t wait for federal law to catch up. Use the simple tips in resulting in an annual loss of this guide to protect yourself from unwanted calls. The law The Telephone Robocall Abuse Criminal Enforcement and $9.5 billion Deterrence (TRACED) Act became federal law in 2019. The act increases penalties and requires phone companies to validate Source: TechRepublic, calls before they reach you. This is to combat “spoofi ng,” October 2019 when a robocaller uses your area code and/or prefi x to ap- pear as if someone locally—maybe a friend or neighbor—is Note: In December 2020, the FCC ordered that organiza- trying to reach you. -
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. -
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. -
A Survey of Learning-Based Techniques of Email Spam Filtering
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Unitn-eprints Research A SURVEY OF LEARNING-BASED TECHNIQUES OF EMAIL SPAM FILTERING Enrico Blanzieri and Anton Bryl January 2008 (Updated version) Technical Report # DIT-06-056 A Survey of Learning-Based Techniques of Email Spam Filtering Enrico Blanzieri, University of Trento, Italy, and Anton Bryl University of Trento, Italy, Create-Net, Italy [email protected] January 11, 2008 Abstract vertising pornography, pyramid schemes, etc. [68]. The total worldwide financial losses caused by spam Email spam is one of the major problems of the to- in 2005 were estimated by Ferris Research Analyzer day’s Internet, bringing financial damage to compa- Information Service at $50 billion [31]. nies and annoying individual users. Among the ap- Lately, Goodman et al. [39] presented an overview proaches developed to stop spam, filtering is an im- of the field of anti-spam protection, giving a brief portant and popular one. In this paper we give an history of spam and anti-spam and describing major overview of the state of the art of machine learn- directions of development. They are quite optimistic ing applications for spam filtering, and of the ways in their conclusions, indicating learning-based spam of evaluation and comparison of different filtering recognition, together with anti-spoofing technologies methods. We also provide a brief description of and economic approaches, as one of the measures other branches of anti-spam protection and discuss which together will probably lead to the final victory the use of various approaches in commercial and non- over email spammers in the near future. -
Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide -
Characterizing Robocalls Through Audio and Metadata Analysis
Who’s Calling? Characterizing Robocalls through Audio and Metadata Analysis Sathvik Prasad, Elijah Bouma-Sims, Athishay Kiran Mylappan, and Bradley Reaves, North Carolina State University https://www.usenix.org/conference/usenixsecurity20/presentation/prasad This paper is included in the Proceedings of the 29th USENIX Security Symposium. August 12–14, 2020 978-1-939133-17-5 Open access to the Proceedings of the 29th USENIX Security Symposium is sponsored by USENIX. Who’s Calling? Characterizing Robocalls through Audio and Metadata Analysis Sathvik Prasad Elijah Bouma-Sims North Carolina State University North Carolina State University [email protected] [email protected] Athishay Kiran Mylappan Bradley Reaves North Carolina State University North Carolina State University [email protected] [email protected] Abstract Despite the clear importance of the problem, much of what Unsolicited calls are one of the most prominent security is known about the unsolicited calling epidemic is anecdotal issues facing individuals today. Despite wide-spread anec- in nature. Despite early work on the problem [6–10], the re- dotal discussion of the problem, many important questions search community still lacks techniques that enable rigorous remain unanswered. In this paper, we present the first large- analysis of the scope of the problem and the factors that drive scale, longitudinal analysis of unsolicited calls to a honeypot it. There are several challenges that we seek to overcome. of up to 66,606 lines over 11 months. From call metadata we First, we note that most measurements to date of unsolicited characterize the long-term trends of unsolicited calls, develop volumes, trends, and motivations (e.g., sales, scams, etc.) have the first techniques to measure voicemail spam, wangiri at- been based on reports from end users. -
"Best Way to Stop Robocalls", and Pulls on Both My Own Experience in A
Dear FTC: The following is my submission on the "best way to stop robocalls", and pulls on both my own experience in anti-telemarketing-abuse and anti-spam activity and—more particularly—my personal experience in investigation of the "worst actors" and coordinating with the Kentucky Attorney-General's office to get some of the worse offenders shut down. A little bit of prelude/backstory is necessary to discuss my particular solution. Kentucky has had a particularly stringent do-not-call since the early 2000s even in comparison with the FTC's do-not-call regulations. Specifically, rather than being a mere civil offense, violation of telemarketing laws is a Class D felony in Kentucky (resulting from 1 to 5 years imprisonment). The majority of the issues that the Kentucky A/G (and myself, as a private actor) have had in stopping the worst offenders has been the following: • Much of the problem is both technical and regulatory--the major technical issue is that the majority of "bad actors" are now using SIP-based VoIP calling from "phish farms" overseas which may not even be located in a specific call center but may be "farmed out" to people's homes worldwide from a central location. (A good example of the latter is the "Telemarketing 1-2- 3/Total Home Systems" phish-farm located out of Guadalajara, Mexico which uses SIP-based "softphone" VoIP to conduct illegal telemarketing to Americans (and other countries) and which typically uses a client PC located in a worker's home to call from.) • Most illegal robocalling is conducted by "phish farms" which may or may not have a US actor at all, but whose primary operations do seem to be located overseas; sometimes US companies hire out, some are overseas, but usually there is some US presence if via the PBX used on the American end of a VoIP call center setup. -
Stopping Outgoing Spam
Stopping Outgoing Spam ∗ Joshua Goodman Robert Rounthwaite Microsoft Research Microsoft Anti-Spam Technology and Strategy One Microsoft Way Group Redmond, WA 98052 One Microsoft Way [email protected] Redmond, WA 98052 [email protected] ABSTRACT year. The vast majority of research on spam has focused on We analyze the problem of preventing outgoing spam. We helping the recipients of spam avoid receiving it [6, 13, 16] show that some conventional techniques for limiting outgo- (to cite a few). We instead focus on helping Email Service ing spam are likely to be ineffective. We show that while Providers (ESPs) prevent their users from sending spam. imposing per message costs would work, less annoying tech- ESPs include most consumer ISPs (e.g. AOL and MSN), niques also work. In particular, it is only necessary that free email systems (e.g. Hotmail and Yahoo), universities, the average cost to the spammer over the lifetime of an ac- etc. Stopping outbound spam at ESPs will not prevent all count exceed his profits, meaning that not every message outgoing spam, as many spammers own direct internet con- need be challenged. We develop three techniques, one based nectivity and cannot be forced to adopt these techniques, on additional HIP challenges, one based on computational but it will stop a substantial source of spam. ESPs want to challenges, and one based on paid subscriptions. Each sys- stop outgoing spam to lessen the load on their own servers, tem is designed to impose minimal costs on legitimate users, to prevent their systems from being blocked from sending while being too costly for spammers.