Messaging spam, sometimes called SPIM,[1][2][3] is a type of spam targeting users of instant messaging (IM) services, SMS, or private messages within websites.
Instant messaging systems, such as Telegram, WhatsApp, Twitter Direct Messaging, Kik, Skype and Snapchat are all targets for spammers.[4] Many IM services are publicly linked to social media platforms, which may include information on the user such as age, sex, location and interests. Advertisers and scammers can gather this information, sign on to the service, and send unsolicited messages which could contain scam links, pornographic material, malware or ransomware. With most services users can report and block spam accounts,[5] or set privacy settings so only contacts can contact them.
These bots target random usernames; this often results in minors receiving unsolicited pornographic images.
On Windows NT-based systemsedit
Example of Messenger Service spam from 2023.
In 2002, a number of spammers began abusing the Windows Messenger service, a function of Windows designed to allow administrators to send alerts to users' workstations (not to be confused with Windows Messenger or Windows Live Messenger, a free instant messaging application) in Microsoft's Windows NT-based operating systems. Messenger Service spam appears as normal dialog boxes containing the spammer's message. These messages are easily blocked by firewalls configured to block packets to the NetBIOS ports 135-139 and 445 as well as unsolicited [[User Datagram Protocol|UDP]] packets to ports above 1024.[8] Additionally, Windows XP Service Pack 2 disables the Messenger Service by default.
Messenger Service spammers frequently send messages to vulnerable Windows machines with a URL. The message promises the user to eradicate spam messages sent via the Messenger Service. The URL leads to a website where, for a fee, users are told how to disable the Messenger service. Though the Messenger is easily disabled for free by the user, this works because it creates a perceived need and then offers an immediate solution.[citation needed]
In an opinion based recommender system, an important concern is how to evaluate the user-generated reviews on the items. One of the purpose of this evaluation is to identify malicious or spam reviews. Poorly written reviews are considered helpless to the recommender system. However, even if a review is well generated, they can still be harmful to the recommender system by their biased prejudice to form an actual advertisement or slander towards a target item.
Current approach of spam detection methods includes analyzing the spam text and identifying the spam reviewers by their reviews and activities. For the first kind, a machine learning application on review text has been developed.[9] For the second kind, researchers use network motif analysis technique to identify spam reviewers by their recurring reviewing activity.[10]
Referencesedit
^"CNET: Spim, splog on the rise". News.com. Retrieved 2013-07-07.
^"Spam being rapidly outpaced by spim". New Scientist. 2004-03-26. Retrieved 2013-07-07.
^Spamfo: SPIM, your new spam October 21, 2007, at the Wayback Machine
^Agarwal, Shubham. "Scientists found more than 1,000 AI spam bots trying to scam people and steal their social media profiles — and regulators can't keep up". Business Insider. Retrieved 2023-10-17.
^Kurt Knutsson, CyberGuy Report (2023-09-22). "How to protect yourself from social media scammers". Fox News. Retrieved 2023-10-17.
^"Tired of spam calls? Block promotional calls in some easy steps on Android, iOS phones". The Economic Times. 2023-06-02. ISSN 0013-0389. Retrieved 2023-10-17.
^"Porn Spam Gets a Kik Out of You". Tom's Guide. 2014-06-05. Retrieved 2017-08-18.
^"Messenger Service window that contains an Internet advertisement appears". Microsoft. Retrieved 2023-12-01.
^Li, Fangtao, et al. "Learning to identify review spam." IJCAI Proceedings-International Joint Conference on Artificial Intelligence. Vol. 22. No. 3. 2011.
^O'Callaghan, Derek, et al. "Network analysis of recurring YouTube spam campaigns." arXiv preprint arXiv:1201.3783 (2012).
February 15, 2024
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Messaging spam sometimes called SPIM 1 2 3 is a type of spam targeting users of instant messaging IM services SMS or private messages within websites Contents 1 Instant messaging applications 2 Countermeasures 3 Pornographic IM spambots 4 On Windows NT based systems 5 In opinion based recommender systems 6 ReferencesInstant messaging applications editThis section does not cite any sources Please help improve this section by adding citations to reliable sources Unsourced material may be challenged and removed October 2022 Learn how and when to remove this template message nbsp Messaging spam on Telegram Instant messaging systems such as Telegram WhatsApp Twitter Direct Messaging Kik Skype and Snapchat are all targets for spammers 4 Many IM services are publicly linked to social media platforms which may include information on the user such as age sex location and interests Advertisers and scammers can gather this information sign on to the service and send unsolicited messages which could contain scam links pornographic material malware or ransomware With most services users can report and block spam accounts 5 or set privacy settings so only contacts can contact them Countermeasures editThis section does not cite any sources Please help improve this section by adding citations to reliable sources Unsourced material may be challenged and removed October 2022 Learn how and when to remove this template message Many users choose to receive IMs only from people already on their contact list In corporate settings spam over IM is blocked by IM spam blockers 6 like those from Actiance ScanSafe and Symantec IM providers like Kik have a report user button which sends a chatlog to the IM administrators who can then take action Pornographic IM spambots editSpam bots often sign on to popular messaging services like Kik 7 or Skype to spread pornographic images Often if the user responds they receive a URL inviting them to a private livestream that will ask them to enter credit card details for age verification These bots target random usernames this often results in minors receiving unsolicited pornographic images On Windows NT based systems edit nbsp Example of Messenger Service spam from 2023 In 2002 a number of spammers began abusing the Windows Messenger service a function of Windows designed to allow administrators to send alerts to users workstations not to be confused with Windows Messenger or Windows Live Messenger a free instant messaging application in Microsoft s Windows NT based operating systems Messenger Service spam appears as normal dialog boxes containing the spammer s message These messages are easily blocked by firewalls configured to block packets to the NetBIOS ports 135 139 and 445 as well as unsolicited User Datagram Protocol UDP packets to ports above 1024 8 Additionally Windows XP Service Pack 2 disables the Messenger Service by default Messenger Service spammers frequently send messages to vulnerable Windows machines with a URL The message promises the user to eradicate spam messages sent via the Messenger Service The URL leads to a website where for a fee users are told how to disable the Messenger service Though the Messenger is easily disabled for free by the user this works because it creates a perceived need and then offers an immediate solution citation needed In opinion based recommender systems editSee also Recommender systemIn an opinion based recommender system an important concern is how to evaluate the user generated reviews on the items One of the purpose of this evaluation is to identify malicious or spam reviews Poorly written reviews are considered helpless to the recommender system However even if a review is well generated they can still be harmful to the recommender system by their biased prejudice to form an actual advertisement or slander towards a target item Current approach of spam detection methods includes analyzing the spam text and identifying the spam reviewers by their reviews and activities For the first kind a machine learning application on review text has been developed 9 For the second kind researchers use network motif analysis technique to identify spam reviewers by their recurring reviewing activity 10 References edit CNET Spim splog on the rise News com Retrieved 2013 07 07 Spam being rapidly outpaced by spim New Scientist 2004 03 26 Retrieved 2013 07 07 Spamfo SPIM your new spam Archived October 21 2007 at the Wayback Machine Agarwal Shubham Scientists found more than 1 000 AI spam bots trying to scam people and steal their social media profiles and regulators can t keep up Business Insider Retrieved 2023 10 17 Kurt Knutsson CyberGuy Report 2023 09 22 How to protect yourself from social media scammers Fox News Retrieved 2023 10 17 Tired of spam calls Block promotional calls in some easy steps on Android iOS phones The Economic Times 2023 06 02 ISSN 0013 0389 Retrieved 2023 10 17 Porn Spam Gets a Kik Out of You Tom s Guide 2014 06 05 Retrieved 2017 08 18 Messenger Service window that contains an Internet advertisement appears Microsoft Retrieved 2023 12 01 Li Fangtao et al Learning to identify review spam IJCAI Proceedings International Joint Conference on Artificial Intelligence Vol 22 No 3 2011 O Callaghan Derek et al Network analysis of recurring YouTube spam campaigns arXiv preprint arXiv 1201 3783 2012 Retrieved from https en wikipedia org w index php title Messaging spam amp oldid 1204845095, wikipedia, wiki, book, books, library,
