Towards Mitigating Unwanted Calls in Voice Over IP
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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. -
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. -
Voice Phishing Attacks
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 07 | July 2020 www.irjet.net p-ISSN: 2395-0072 Voice Phishing Attacks Ujjwal Saini Student BSC HONS (Computer Science) Hansraj College Delhi University --------------------------------------------------------------------------***------------------------------------------------------------------ Abstract - Voice Phishing also known as vishing is a type of criminal fraud in which a fraudster or a bad guy use some social engineering techniques to steal the personal and sensitive information of a person over telephone lines. This research paper gives a brief information about the term voice phishing what exactly it is, describes the modus operandi that is used by these fraudsters nowadays. This paper also includes some case studies or some examples that are common in present times that based on survey. This paper also brief you the protective measures that a user can take to safeguard his/her personal information 1. INTRODUCTION Voice Phishing/Vishing is a technique in which a scammer or an attacker uses fraudulent calls and trick the user to give their personal information. Basically vishing is new name to an older scam i.e. telephone frauds which includes some new techniques to steal information from a user. Vishing is similar to fishing in which a fisher catch fishes in their trap similarly in vishing attacker catch user to give their personal information. Vishing frequently involves a criminal pretending to represent a trusted institution, -
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. -
Vishing Countermeasures
Vishing (and “SMiShing”) Countermeasures Fraud Investigation & Education FIS www.fisglobal.com Vishing Countermeasures Vishing…What is it? Vishing also called (Voice Phishing) is the voice counterpart to the phishing scheme. Instead of being directed by an email to a website, the user is asked to make a telephone call. The call triggers a voice response system that asks for the user’s personal identifiable information to include: Plastic card number, Expiration date, CVV2/CVC2, and/or PIN number. To date, there have been two methods of this technique that have been identified. The first method is via “Email blast”. The email blast has the exact same concept of phishing email that includes false statements intended to create the impression that there is an immediate threat or risk to the financial account of the person who receives the email. Instead of Weblink, there is a number provided that instructs the person to call and provide their personal identifiable information. Example of a vishing email: 800.282.7629 [email protected] 2 © 2010 FIS. and its subsidiaries. Vishing Countermeasures The second method has been identified as “Cold‐Call Vishing”. With this method, the fraudsters use both a war dialer program with a VoIP (Voice over Internet Protocol) technology to cover a specific area code(s). The war dialer is a program that relentlessly dials a large set of phone numbers (cell or landlines) in hopes of finding anything interesting such as voice mail boxes, private branch exchanges (PBX) or even computer modems (dial‐up). VoIP is a technology that allows anyone to make a call using a broadband internet connection instead of a regular phone line. -
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. -
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. -
Technical Means to Combat Spam in the Voip Service
Section Four Technical Means to Combat Spam in the VoIP Service Spam refers in general to any unsolicited communication. Spam will also become one of the serious problems for multimedia communication in the near future. Spam in multimedia communication is referred to as SIP spam or SPIT (Spam over Internet Telephony), where SIP is used to manage the session between two end users. In this paper, the types of SIP spam are introduced and various pragmatic solutions applicable to combat the SIP spams are described including content filtering, white list, black list, and the reputation system. Finally, the detailed operation and principles for the authenticated identity in SIP header, which is a prerequisite for the solutions above, are also described. The possible solutions to combat the SIP spasm have been listed and the background technology to those solutions, an authenticated identity between the domains, is also introduced. Heung Youl Youm (PhD) Professor, Soonchunhyang University, South Korea Rapporteur, Q.9/SG17, ITU-T [email protected] 1 Introduction IP telephony is known as a technology that allows standard telephone voice signals to be compressed into data packets for transmission over the Internet or other IP network. The protocols used in carrying the voice signals over the IP networks are commonly referred to as Voice over IP (VoIP). The spam problem in email and instant messaging (IM) makes the email or the IM users to trust less of these tools and consequently reduce their usage. While the security mechanisms for the IP telephony are being studied, the spam problem in VoIP has not been studied extensively yet. -
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. -
Towards Online Spam Filtering in Social Networks
Towards Online Spam Filtering in Social Networks Hongyu Gao Yan Chen Kathy Lee† Northwestern University Northwestern University Northwestern University Evanston, IL, USA Evanston, IL, USA Evanston, IL, USA [email protected] [email protected] [email protected] Diana Palsetia† Alok Choudhary† Northwestern University Northwestern University Evanston, IL, USA Evanston, IL, USA [email protected] [email protected] Abstract idence shows that they can also be effective mechanisms for spreading attacks. Popular OSNs are increasingly be- Online social networks (OSNs) are extremely popular coming the target of phishing attacks launched from large among Internet users. Unfortunately, in the wrong hands, botnets [1, 3]. Two recent studies [9, 10] have confirmed they are also effective tools for executing spam campaigns. the existence of large-scale spam campaigns in Twitter and In this paper, we present an online spam filtering system that Facebook, respectively. Furthermore, the clickthrough rate can be deployed as a component of the OSN platform to in- of OSN spam is orders of magnitude higher than its email spect messages generated by users in real-time. We propose counterpart [10], indicating that users are more prone to to reconstruct spam messages into campaigns for classifica- trust spam messages from their friends in OSNs. tion rather than examine them individually. Although cam- The OSN spam problem has already received atten- paign identification has been used for offline spam analysis, tion from researchers. Meanwhile, email spam, a seem- we apply this technique to aid the online spam detection ingly very similar problem, has been extensively studied for problem with sufficiently low overhead. -
A Survey on Voip Security Attacks and Their Proposed Solutions
International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected], [email protected] Volume 2, Issue 3, March 2013 ISSN 2319 - 4847 A Survey on VoIP Security Attacks and their Proposed Solutions Jyoti Shukla1, Bhavana Sahni2 1Assistant Professor, Amity University 2M.tech Student, Amity University ABSTRACT VoIP is a communication protocol which is being widely used and voice calls are transmitted over an IP network such as internet instead of Public Switched Telephone Networks (PSTN). VoIP converts voice into digital signal that travels over the internet and the audio output device makes the digital to audio conversation at the receiver side. In today's time people are using these technologies in their daily life, for communication over the internet but they are not aware with the security issues on VoIP conversation. There are different types of security issues with VoIP conversation. The main focus on this paper is to introduce different security attacks and defense approaches for security attacks on VoIP conversation. Keywords: VoIP, Security Attacks, Defense Approaches. 1. INTRODUCTION Voice over internet protocol (VoIP) is routing of voice conversation over the internet or IP based network. The flow of voice data over the internet in the VoIP system firstly human voice must be converted into digitized form. Then it is compressed to save bandwidth and optionally encryption can also be used to protect the conversation from sniffing. Then the voice samples are inserted into data packets to be carried out over the IP networks. Real- time Transport Protocol which defines the standardized packet format for delivering the audio or video over the internet, RTP packet have header field to hold the data and it is needed to correctly re-assemble packet into voice signal on the other end.