Consultation Paper on Spam, Unsolicited Calls and Obnoxious
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A Rule Based Approach for Spam Detection
A RULE BASED APPROACH FOR SPAM DETECTION Thesis submitted in partial fulfillment of the requirements for the award of degree of Master of Engineering In Computer Science & Engineering By: Ravinder Kamboj (Roll No. 800832030) Under the supervision of: Dr. V.P Singh Mrs. Sanmeet Bhatia Assistant Professor Assistant Professor Computer Science & Engineering Department of SMCA COMPUTER SCIENCE AND ENGINEERING DEPARTMENT THAPAR UNIVERSITY PATIALA – 147004 JULY- 2010 i ii Abstract Spam is defined as a junk Email or unsolicited Email. Spam has increased tremendously in the last few years. Today more than 85% of e-mails that are received by e-mail users are spam. The cost of spam can be measured in lost human time, lost server time and loss of valuable mail. Spammers use various techniques like spam via botnet, localization of spam and image spam. According to the mail delivery process anti-spam measures for Email Spam can be divided in to two parts, based on Emails envelop and Email data. Black listing, grey listing and white listing techniques can be applied on the Email envelop to detect spam. Techniques based on the data part of Email like heuristic techniques and Statistical techniques can be used to combat spam. Bayesian filters as part of statistical technique divides the income message in to words called tokens and checks their probability of occurrence in spam e-mails and ham e-mails. Two types of approaches can be followed for the detection of spam e-mails one is learning approach other is rule based approach. Learning approach required a large dataset of spam e-mails and ham e-mails is required for the training of spam filter; this approach has good time characteristics filter can be retrained quickly for new Spam. -
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 Survey on Spam Detection Techniques
ISSN (Online) : 2278-1021 ISSN (Print) : 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 12, December 2014 A survey on spam detection techniques Anjali Sharma1, Manisha 2, Dr.Manisha 3 , Dr.Rekha Jain 4 1,2,3,4 Bansthali Vidyapith, Jaipur Campus, India Abstract: Today e-mails have become one of the most popular and economical forms of communication for Internet users. Thus due to its popularity, the e-mail is going to be misused. One such misuse is the posting of unwelcome, unwanted e-mails known as spam or junk e-mails [1]. E-mail spam has various consequences. It reduces productivity, takes extra space in mail boxes, extra time, extend software damaging viruses, and materials that contains potentially harmful information for Internet users, destroy stability of mail servers, and as a result users spend lots of time for sorting incoming mail and deleting unwanted correspondence. So there is a need of spam detection so that its consequences can be reduced [2]. In this paper, we present various spam detection techniques. Keywords: Spam, Spam detection techniques, Email classification I. INTRODUCTION Spam refers to unsolicited commercial email. Also known firewalls; therefore, it is an especially useful way for as junk mail, spam floods Internet users’ electronic spammers. It targets the users when they join any chat mailboxes. These junk mails can contain various types of room to find new friends. It spoils enjoy of people and messages such as pornography, commercial advertising, waste their time also. doubtful product, viruses or quasi legal services [3]. -
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. -
Image Spam Detection: Problem and Existing Solution
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072 Image Spam Detection: Problem and Existing Solution Anis Ismail1, Shadi Khawandi2, Firas Abdallah3 1,2,3Faculty of Technology, Lebanese University, Lebanon ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Today very important means of communication messaging spam, Internet forum spam, junk fax is the e-mail that allows people all over the world to transmissions, and file sharing network spam [1]. People communicate, share data, and perform business. Yet there is who create electronic spam are called spammers [2]. nothing worse than an inbox full of spam; i.e., information The generally accepted version for source of spam is that it crafted to be delivered to a large number of recipients against their wishes. In this paper, we present a numerous anti-spam comes from the Monty Python song, "Spam spam spam spam, methods and solutions that have been proposed and deployed, spam spam spam spam, lovely spam, wonderful spam…" Like but they are not effective because most mail servers rely on the song, spam is an endless repetition of worthless text. blacklists and rules engine leaving a big part on the user to Another thought maintains that it comes from the computer identify the spam, while others rely on filters that might carry group lab at the University of Southern California who gave high false positive rate. it the name because it has many of the same characteristics as the lunchmeat Spam that is nobody wants it or ever asks Key Words: E-mail, Spam, anti-spam, mail server, filter. -
History of Spam
1 Max W. Mosing1 THE UPS AND DOWNS IN THE HISTORY OF EU-SPAM-REGULATIONS AND THEIR PRACTICAL IMPACT “Two years from now, spam will be solved. I promise a spam-free world by 2006” (Bill Gates; January 2004) Table of content: 1. (Legal) History of Spam................................................................................................................................................................................... 1 1.1. „Monty Python“and „Canter and Seigel“............................................................................................................................................ 2 1.2. Definition of Spam from a Legal Perspective ...................................................................................................................................... 2 1.3. Harm of Spam and– if the Internet community wants to ban spam – why is spam so successful?........................................... 3 1.4. Legal Framework and legal background of spam .............................................................................................................................. 4 2. “Spam-Regulations” in EU-Directives .......................................................................................................................................................... 6 2.1. Directive 97/66/EC concerning the processing of personal data and the protection of privacy in the telecommunications sector – ISDN-Directive and National Laws.............................................................................................................................................. -
Asian Anti-Spam Guide 1
Asian Anti-Spam Guide 1 © MediaBUZZ Pte Ltd January 2009 Asian Anti-SpamHighlights Guide 2 • Combating the latest inbound threat: Spam and dark traffic, Pg. 13 • Secure Email Policy Best Practices, Pg. 17 • The Continuous Hurdle of Spam, Pg. 29 • Asian Anti Spam Acts, Pg. 42 Contents: • Email Spam: A Rising Tide 4 • What everyone should know about spam and privacy 7 • Scary Email Issues of 2008 12 • Combating the latest inbound threat: Spam and dark 13 • Proofpoint survey viewed spam as an increasing threat 16 • Secure Email Policy Best Practices 17 • Filtering Out Spam and Scams 24 • The Resurgence of Spam 26 • 2008 Q1 Security Threat landscape 27 • The Continuous Hurdle of Spam 29 • Spam Filters are Adaptive 30 • Liberating the inbox: How to make email safe and pro- 31 ductive again • Guarantee a clear opportunity to opt out 33 • The Great Balancing Act: Juggling Collaboration and 34 Authentication in Government IT Networks • The Not So Secret Cost of Spam 35 • How to Avoid Spam 36 • How to ensure your e-mails are not classified as spam 37 • Blue Coat’s Top Security Trends for 2008 38 • The Underground Economy 40 • Losing Email is No Longer Inevitable 42 • Localized malware gains ground 44 • Cyber-crime shows no signs of abating 45 MEDIABUZZ PTE LTD • Asian Anti-Spam Acts 47 ASIAN ANTI-SPAM GUIDE © MediaBUZZ Pte Ltd January 2009 Asian Anti-SpamHighlights Guide 3 • Frost & Sullivan: Do not underestimate spam, Pg. 65 • Unifying email security is key, Pg. 71 • The many threats of network security, Pg. 76 • The UTM story, Pg. -
Internet Security
In the News Articles in the news from the past month • “Security shockers: 75% of US bank websites Internet Security have flaws” • “Blank robbers swipe 3,000 ‘fraud-proof’ UK passports” • “Korean load sharks feed on hacked data” • “Worms spread via spam on Facebook and Nan Niu ([email protected]) MySpace” CSC309 -- Fall 2008 • “Beloved websites riddled with crimeware” • “Google gives GMail always-on encryption” http://www.theregister.co.uk 2 New Targets of 2007 Scenario 1 • Cyber criminals and cyber spies have • The Chief Information Security Officer shifted their focus again of a medium sized, but sensitive, federal – Facing real improvements in system and agency learned that his computer was network security sending data to computers in China. • The attackers now have two new targets • He had been the victim of a new type of spear phishing attack highlighted in this – users who are easily misled year’s Top 20. – custom-built applications • Once they got inside, the attackers had • Next, 4 exploits scenarios… freedom of action to use his personal • Reported by SANS (SysAdmin, Audit, Network, computer as a tunnel into his agency’s Security), http://www.sans.org systems. 3 4 Scenario 2 Scenario 3 • Hundreds of senior federal officials and business • A hospital’s website was compromised executives visited a political think-tank website that had been infected and caused their computers to because a Web developer made a become zombies. programming error. • Keystroke loggers, placed on their computers by the • Sensitive patient records were taken. criminals (or nation-state), captured their user names and passwords when their stock trading accounts and • When the criminals proved they had the their employers computers, and sent the data to data, the hospital had to choose between computers in different countries. -
Design of SMS Commanded-And-Controlled and P2P-Structured Mobile Botnets
Design of SMS Commanded-and-Controlled and P2P-Structured Mobile Botnets Yuanyuan Zeng, Kang G. Shin, Xin Hu The University of Michigan, Ann Arbor, MI 48109-2121, U.S.A. fgracez, kgshin, [email protected] Abstract—Botnets have become one of the most serious security is usually capable of only one or two functions. Although the threats to the Internet and personal computer (PC) users. number of mobile malware families and their variants has been Although botnets have not yet caused major outbreaks in mobile growing steadily over the recent years, their functionalities networks, with the rapidly-growing popularity of smartphones such as Apple’s iPhone and Android-based phones that store have remained simple until recently. more personal data and gain more capabilities than earlier- SymbOS.Exy.A trojan [2] was discovered in February 2009 generation handsets, botnets are expected to move towards this and its variant SymbOS.Exy.C resurfaced in July 2009. This mobile domain. Since SMS is ubiquitous to every phone and can mobile worm, which is said to have “botnet-esque” behavior delay message delivery for offline phones, it is a suitable medium patterns, differs from other mobile malware because after for command and control (C&C). In this paper, we describe how a mobile botnet can be built by utilizing SMS messages infection, it connects back to a malicious HTTP server and for C&C, and how different P2P structures can be exploited reports information of the device and its user. The Ikee.B for mobile botnets. Our simulation results demonstrate that a worm [3] targets jailbroken iPhones, and has behavior similar modified Kademlia—a structured architecture—is a better choice to SymbOS.Exy. -
Anti-Spam Methods
INTRODUCTION Spamming is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. Most spam is commercial advertising, often for dubious products, get-rich-quick schemes, or quasi- legal services. Spam costs the sender very little to send -- most of the costs are paid for by the recipient or the carriers rather than by the sender. There are two main types of spam, and they have different effects on Internet users. Cancellable Usenet spam is a single message sent to 20 or more Usenet newsgroups. (Through long experience, Usenet users have found that any message posted to so many newsgroups is often not relevant to most or all of them.) Usenet spam is aimed at "lurkers", people who read newsgroups but rarely or never post and give their address away. Usenet spam robs users of the utility of the newsgroups by overwhelming them with a barrage of advertising or other irrelevant posts. Furthermore, Usenet spam subverts the ability of system administrators and owners to manage the topics they accept on their systems. Email spam targets individual users with direct mail messages. Email spam lists are often created by scanning Usenet postings, stealing Internet mailing lists, or searching the Web for addresses. Email spams typically cost users money out-of-pocket to receive. Many people - anyone with measured phone service - read or receive their mail while the meter is running, so to speak. Spam costs them additional money. On top of that, it costs money for ISPs and online services to transmit spam, and these costs are transmitted directly to subscribers. -
Spam (Spam 2.0) Through Web Usage
Digital Ecosystems and Business Intelligence Institute Addressing the New Generation of Spam (Spam 2.0) Through Web Usage Models Pedram Hayati This thesis is presented for the Degree of Doctor of Philosophy of Curtin University July 2011 I Abstract Abstract New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications. The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method. This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering. This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. -
Statistics Survey 357528 'Survey on Internet Attitudes'
Quick statistics Survey 357528 'Survey on Internet Attitudes' Results Survey 357528 Number of records in this query: 1000 Total records in survey: 1000 Percentage of total: 100.00% page 1 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QA1 I agree to these terms and want to participate in the survey. I confirm that I am 16 years of age or over. Answer Count Percentage Yes (A1) 1000 100.00% No answer 0 0.00% page 2 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QB1a(SQ001) How often do you go online and for which of the following activities (for private purposes)? [a. Check my email] Answer Count Percentage Several times a day (A1) 893 89.30% Daily (A2) 92 9.20% Weekly (A3) 10 1.00% Monthly (A4) 2 0.20% Less than monthly (A5) 0 0.00% Never (A6) 3 0.30% No answer 0 0.00% page 3 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QB1a(SQ002) How often do you go online and for which of the following activities (for private purposes)? [b. Use instant messaging (e.g. WhatsApp, Facebook Messenger)] Answer Count Percentage Several times a day (A1) 577 57.70% Daily (A2) 165 16.50% Weekly (A3) 87 8.70% Monthly (A4) 28 2.80% Less than monthly (A5) 33 3.30% Never (A6) 110 11.00% No answer 0 0.00% page 4 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QB1a(SQ003) How often do you go online and for which of the following activities (for private purposes)? [c.