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The Madoff Investment Securities Fraud: Regulatory and Oversight Concerns and the Need for Reform Hearing Committee on Banking
S. HRG. 111–38 THE MADOFF INVESTMENT SECURITIES FRAUD: REGULATORY AND OVERSIGHT CONCERNS AND THE NEED FOR REFORM HEARING BEFORE THE COMMITTEE ON BANKING, HOUSING, AND URBAN AFFAIRS UNITED STATES SENATE ONE HUNDRED ELEVENTH CONGRESS FIRST SESSION ON HOW THE SECURITIES REGULATORY SYSTEM FAILED TO DETECT THE MADOFF INVESTMENT SECURITIES FRAUD, THE EXTENT TO WHICH SECURITIES INSURANCE WILL ASSIST DEFRAUDED VICTIMS, AND THE NEED FOR REFORM JANUARY 27, 2009 Printed for the use of the Committee on Banking, Housing, and Urban Affairs ( Available at: http://www.access.gpo.gov/congress/senate/senate05sh.html U.S. GOVERNMENT PRINTING OFFICE 50–465 PDF WASHINGTON : 2009 For sale by the Superintendent of Documents, U.S. Government Printing Office Internet: bookstore.gpo.gov Phone: toll free (866) 512–1800; DC area (202) 512–1800 Fax: (202) 512–2104 Mail: Stop IDCC, Washington, DC 20402–0001 VerDate Nov 24 2008 08:33 Jul 07, 2009 Jkt 048080 PO 00000 Frm 00001 Fmt 5011 Sfmt 5011 S:\DOCS\50465.TXT JASON COMMITTEE ON BANKING, HOUSING, AND URBAN AFFAIRS CHRISTOPHER J. DODD, Connecticut, Chairman TIM JOHNSON, South Dakota RICHARD C. SHELBY, Alabama JACK REED, Rhode Island ROBERT F. BENNETT, Utah CHARLES E. SCHUMER, New York JIM BUNNING, Kentucky EVAN BAYH, Indiana MIKE CRAPO, Idaho ROBERT MENENDEZ, New Jersey MEL MARTINEZ, Florida DANIEL K. AKAKA, Hawaii BOB CORKER, Tennessee SHERROD BROWN, Ohio JIM DEMINT, South Carolina JON TESTER, Montana DAVID VITTER, Louisiana HERB KOHL, Wisconsin MIKE JOHANNS, Nebraska MARK R. WARNER, Virginia KAY BAILEY HUTCHISON, Texas JEFF MERKLEY, Oregon MICHAEL F. BENNET, Colorado COLIN MCGINNIS, Acting Staff Director WILLIAM D. -
Mobile Telephony Threats in Asia Black Hat Asia 2017, Singapore
Mobile Telephony Threats in Asia Black Hat Asia 2017, Singapore Dr. Marco Balduzzi Dr. Payas Gupta Lion Gu Sr. Threat Researcher Data Scientist Sr. Threat Researcher Trend Micro Pindrop Trend Micro Joint work with Prof. Debin Gao (SMU) and Prof. Mustaque Ahamad (GaTech) Marco’s 9th BH Anniversary :-) 2 Click to play recording [removed] 3 Wangiri Fraud, Japan 4 Fake Officials Fraud, China 5 This is your Telco calling, UAE 6 Police Scam, Singapore 7 BringBackOurCash, Nigeria 8 Why is Happening? • Lack of users’ awareness • Users publicly disclose their mobile numbers • Expose themselves and the organization they work for! 9 Current Defeat Strategies • Telcos • Crowd sourced – FTC, fraud complaints – 800notes open datasets • Proprietary 10 10 Missing Caller's Details 11 No Actual Timestamps 12 Perception v/s Reality 13 Not all Fraudulent Calls are Reported • Compared both FTC and 800notes against each other for a certain set of numbers 14 Delay in Reporting Fraudulent Calls 15 Any Solution? 17 Using SIP Trunks IP-192.168.1.11 Tel. ext - 83345 IP-192.168.1.12 Call Call Manager/ PBX Switch Tel. ext - 83351 SIP Trunk Telephone Rules Destination no. Destination IP 192.168.1.10 Exchange Incoming call 88800 - 88899 192.168.1.10 Tel. Range - Incoming call 83345 192.168.1.11 88800 to 88899 Incoming call 83351 192.168.1.12 IP-192.168.1.13 Incoming call 88346 192.168.1.13 Tel. ext - 88346 Call Manager table Honeypot 18 Using GSM/VoIP Gateways 19 Mobile Telephony Honeypot 20 Mobile Telephony Honeypot 21 Example of Call Recording Example of SMS Recording • 确认了哈,位置还留起的 之前在等qq消息,我刚才电话问 了,给我转款吧。建 行四川分行第五支行5240 9438 1020 0709,户名:王玲。 (I have confirmed. -
Detecting Malicious Campaigns in Obfuscated Javascript with Scalable Behavioral Analysis
Detecting malicious campaigns in obfuscated JavaScript with scalable behavioral analysis 1st Oleksii Starov 2nd Yuchen Zhou 3rd Jun Wang Palo Alto Networks, Inc. Palo Alto Networks, Inc. Palo Alto Networks, Inc. [email protected] [email protected] [email protected] Abstract—Modern security crawlers and firewall solutions for malicious scripts [4], [10], [13]. However, proposed sys- have to analyze millions of websites on a daily basis, and tems usually require additional instrumentation and resources significantly more JavaScript samples. At the same time, fast that are unsuitable for near real-time analysis on a large static approaches, such as file signatures and hash matching, often are not enough to detect advanced malicious campaigns, population of URLs. Moreover, such approaches use machine i.e., obfuscated, packed, or randomized scripts. As such, low- learning models, which must be trained and calibrated to overhead yet efficient dynamic analysis is required. minimize false positives. In contrast, there is lack of research In the current paper we describe behavioral analysis after in terms of lightweight behavioral detection methods and indi- executing all the scripts on web pages, similarly to how real cators of dynamic script execution that conclusively determine browsers do. Then, we apply light “behavioral signatures” to the malicious behavior. To the best of our knowledge, we are the collected dynamic indicators, such as global variables declared during runtime, popup messages shown to the user, established first to present a systematic case-study on detecting modern WebSocket connections. Using this scalable method for a month, malicious campaigns with such scalable dynamic analysis. we enhanced the coverage of a commercial URL filtering product In this paper, we describe behavioral analysis that exe- by detecting 8,712 URLs with intrusive coin miners. -
Convergence of Wildlife Crime with Other Forms of Organised Crime May 2021 3
Convergence of wildlife crime with other forms of organised crime May 2021 www.wildlifejustice.org 3 Table of contents Acronyms ............................................................................................................. 4 Executive Summary ............................................................................................ 7 Introduction ....................................................................................................... 10 Brief review of wildlife crime convergence ................................................. 12 Illustrative case studies of convergence ...................................................... 15 Wildlife crime ............................................................................................. 16 Case study 1 ....................................................................................... 16 Case study 2 ....................................................................................... 20 Case study 3 ....................................................................................... 22 Case study 4 ....................................................................................... 24 Case study 5 ....................................................................................... 26 Case study 6 ....................................................................................... 28 Fisheries crime .......................................................................................... 30 Case study 7 ...................................................................................... -
Grado En Comercio
GRADO EN COMERCIO TRABAJO FIN DE GRADO ANÁLISIS DE LAS PRÁCTICAS FRAUDULENTAS EN LAS REDES VÍCTOR GALLEGO MORO VALLADOLID, SEPTIEMBRE 2020 UNIVERSIDAD DE VALLADOLID GRADO EN COMERCIO CURSO ACADÉMICO 2019/2020 TRABAJO FIN DE GRADO ANÁLISIS DE LAS PRÁCTICAS FRAUDULENTAS EN LAS REDES Trabajo presentado por: VÍCTOR GALLEGO MORO Firma: Tutor: OSCAR M. GONZÁLEZ RODRÍGUEZ Firma: Valladolid, septiembre 2020 ÍNDICE 1 Introducción. ...................................................................................................5 2 Marco teórico ..................................................................................................7 2.1 Scam. Concepto, medios y clasificación ...........................................................7 2.1.1 Medios utilizados por Scammers ................................................................................ 7 2.1.2 Tipos de Scam más frecuentes ................................................................................... 8 2.1.3 Formas de cobrar una estafa .................................................................................... 10 3 Funcionamiento de las organizaciones de Scam ...........................................12 3.1 Formando una organización criminal de estafas online ................................. 12 3.2 Estructura organizativa .................................................................................. 14 3.2.1 Organigrama ............................................................................................................ 14 3.2.2 Horarios.................................................................................................................. -
Presroent for STRICT STOCK MARKET CURB
A.'vmuea otAiLT oibcvxatium tar tki Moirth of April. 1M4 5,453 FOL. IfSL, NO. 198. (ClaMtiled O B PBfO lAX MANCHESTER, CONN., WEDNESDAY, MAY 16, 1934. (SIXTEEN PAGES) PRICE THREE SEEK CHANGES QUICK JUSTICE Lightship Sunk In Fog By Liner Olympic- FLIERS BAREY IN LAYOUT OF FOR KIDNAPERS; Seven Killed CROSS SEA, HIT PRESroENT FOR STRICT DEPOnOUARE THEYJETLIFE I R E L ^ R M STOCK MARKET CURB Hfeold Cat Down Corner at Gettle Captors WiD Be m Sabelli and Pond Miss Doath Chief Execatire Dechret Intersection and Also Re* San Qoentin Before Night by hches, Plane Cracking Sen. Reed Victorious a Emphatically for Regula moye Hedge on North fall to Start Senring Sen Dp I t Lahinch, Seashore In Pennsy Primaries tion of Exchange hy d » Main Street tences— Plead Gnilty. Town of Clare. Philadelphia, May 16.—(A P I- After Senator David A. Reed bad Federal Trade Commia- rolled up a majority of more than Plans for a radical change in the Los Angeles, May 16.—(A P I- Lahlnch, Irish Free State, May 16. 100,000 in two-thirds of the state. Depot square area to lessen the Three kidnapers of the wealthy —(AP)— ’The story of “missing Governor Gifford Plnchot today sion as Proposed by the lection of Main and North Main William (Settle will begin serving death by Inches” as they flew over conceded defeat in his effort to win the Republican nomination for Sen kraffie danger existing at the inter* life sentences for that crime before the Atlantic, fighting leaking gaso Senate. street were viewed by the Select nightfall. -
Telephone-Based Social Engineering Attacks: an Experiment Testing the Success and Time Decay of an Intervention
Telephone-based social engineering attacks: An experiment testing the success and time decay of an intervention Jan-Willem Bullee´ a, Lorena Montoya a, Marianne Junger a and Pieter H. Hartel a a University of Twente, Enschede, The Netherlands Abstract. The objective of this study is to evaluate the effectiveness of an infor- mation campaign to counter a social engineering attack via the telephone. Four dif- ferent offenders phoned 48 employees and made them believe that their PC was distributing spam emails. Targets were told that this situation could be solved by downloading and executing software from a website (i.e. an untrusted one). A total of 46.15 % of employees not exposed to the intervention followed the instructions of the offender. This was significantly different to those exposed to an intervention 1 week prior to the attack (9.1 %); however there was no effect for those exposed to an intervention 2 weeks prior to the attack (54.6 %). This research suggests that scam awareness-raising campaigns reduce vulnerability only in the short term. Keywords. Awareness, Decay, Scam, Social Engineering, Retention, Telephone, Time, Training 1. Introduction Since 2008 a scam has been carried out by employees claiming to belong to Microsoft’s technical department [1]. A phone call is received unexpectedly at home; the caller intro- duces himself and proceeds to inform the home owner that there is a virus on the PC or that the PC is distributing spam emails. To verify the claim from the caller, the victim is persuaded to open a remote desktop session to review some warnings in the system log files. -
Board of County Commissioners Agenda Thursday, February 13, 2020, 9:00Am Commission Chambers, Room B-11
BOARD OF COUNTY COMMISSIONERS AGENDA THURSDAY, FEBRUARY 13, 2020, 9:00AM COMMISSION CHAMBERS, ROOM B-11 I. PROCLAMATIONSIPRESENTA TIONS II. UNFINISHED BUSINESS Ill. CONSENT AGENDA l. Acknowledge receipt of Summons in Case No. 2020CV000080 Equity Bank vs. Hoard of Commissioners of Shawnee County. Kansas et. al.--County Clerk. 2. Con:-.idcr emergency vehicle pennit for Kyle Soldani for emergency fire and rescue response for Shawnee County / Dover Fire District #4-Emergem:y Management. 1 3. Consider acceptance of one permanent and two temporary easements for the SE 45 h Street- SE Berryton/ West Edge Road to SE East Edge Road project and authorization to pay property owners the agreed settlement amounts Public Works. 4. Consider <~uthorization and execution of the following contracts for treatment of noxious weeds with a recovery rate of 100 pcrccnl: (a) Contract C39-2020 with Aubum Tovmship (b) Contract C40-2020 with Kansas Dept. of Transportation (c) Contract C41-2020 with \tlission Township (d) Contract C42-2020 with Tecumseh Township (e) Contract C43-2020 with the Wakarusa Watershed Joint District #35 5. Consider approval of the following regarding the previously approved hiring of John Landon as the Noxious Weed Director: (a) Approval of the excerpt of minutes from the February 3, 2020 Board of County Commission Meeting as required by the Kansas Department of Agriculture-County Clerk. (h) E:o:ecuting the Kans<ts Dep<trtment of Agriculture's "Nomination fur County Weed Supervisor"-Public Works. IV. NEW BUSINESS A. COUNTY CLERK- Cynthia Beck I. Consider all voucher payments. 2. Consider correction orders. B. PUBLIC WORKS- Curt Niehaus I. -
Why We Should Partially Privatize the Barney Fifes at the SEC Mark Klock†
LESSONS LEARNED FROM BERNARD MADOFF: Why We Should Partially Privatize the Barney Fifes at the SEC Mark Klock† TABLE OF CONTENTS I. INTRODUCTION........................................................................................ 2 II. BACKGROUND: THE LARGEST PONZI SCHEME IN HISTORY ..................... 3 A. The Chronology of Public Revelations About Madoff .................... 3 B. The Chronology of the SEC’s Internal Investigations .................... 5 III. FINDINGS OF IINCOMPETENCE IN THE MADOFF EXAMINATIONS .............. 6 A. The 1992 Opportunity to Stop the Scam ......................................... 6 B. SEC Examinations and Non-Examinations from 2001 to 2008 .... 11 C. A Digression on Options and Madoff’s Claimed Hedge Strategy 22 IV. THE ROLE OF AUDITORS AND MADOFF'S AUDITOR ............................... 27 A. The Auditing Function in Capital Markets ................................... 27 B. Madoff’s Auditor David Friehling ................................................ 33 V. OTHER FINANCIAL SCANDALS OF THE LAST DECADE ........................... 37 VI. SECONDARY LIABILITY TO PROMOTE A CULTURE OF INTEGRITY .......... 42 VII. A CALL FOR MORE FINANCIAL LITERACY IN LEGAL EDUCATION ......... 47 VIII. CONCLUSION ........................................................................................ 51 † B.A., The Pennsylvania State University, 1978; Ph.D. in Economics, Boston College, 1983; J.D. (with honors), University of Maryland, 1988; admitted to the Maryland Bar, 1988; admitted to the District of Columbia Bar, 1989; -
Using Web-Scale Graph Analytics to Counter Technical Support Scams
Using Web-Scale Graph Analytics to Counter Technical Support Scams Jonathan Larson Bryan Tower Duane Hadfield Darren Edge Christopher White Microsoft AI & Research Microsoft AI & Research Digital Crimes Unit Microsoft AI & Research Microsoft AI & Research Microsoft Microsoft Microsoft Microsoft Microsoft Silverdale, WA, USA Silverdale, WA, USA Redmond, WA, USA Cambridge, UK Redmond, WA, USA [email protected] [email protected] [email protected] [email protected] [email protected] Abstract—Technical Support Scams delivered through Support Scams. While the design of the tool was based on the malicious webpages and linked advertisements are an endemic investigative tradecraft that contributed to 16 major enforcement problem on the web. To avoid detection, scammers systematically actions taken by the FTC against scam organizations and their vary the URLs, web page designs, and phone numbers of scam owners in 2016 [7], its subsequent deployment from 2016-2018 delivery. We describe a web-scale pipeline powered by Cloud AI has led to further actions by Indian law enforcement on Delhi- services that continuously detects and links evidence of such based call centers and helped to drive a global 5-percent decline scams. By integrating this evidence in a graph structure and in consumer exposure to ad-based Technical Support Scams [2]. exposing it for forensic analysis in a user interface, we enable investigators and law enforcement partners to track the evolving scope and sophistication of scam operations. This approach automates and scales investigative tradecraft that contributed to major enforcement actions by the FTC in 2016. From 2016-2018, it helped reduce consumer exposure to such scams by 5 percent. -
Consumer Protection Report: February 1 –28, 2014
CONSUMER PROTECTION REPORT: FEBRUARY 1 –28, 2014 This newsletter is the fourth of a monthly circulation that describes consumer protection activity announced by state attorneys general. This information was gathered solely from attorney general press releases. It makes no effort to prioritize or analyze the impact of any of these cases and initiatives. The following press releases are organized by state and multistate activity. In addition, certain Medicaid fraud cases that touch on consumer protection and advocacy initiatives have been included. If an office would like their activity to be included in subsequent newsletters, please notify [email protected]. To sign up for the monthly consumer protection report, please click on the link below and enter your contact information. Newsletter sign up: http://stateag.us4.list- manage.com/subscribe?u=9c3bb47bb6aba00473adb0c58&id=fdba3bae7b The National State Attorneys General Program at Columbia Law School is a legal research, education, and policy center that examines the implications of the jurisprudence of state attorneys general. Working closely with attorneys general, academics, and other members of the legal community, the program is active in the development and dissemination of legal information used by state prosecutors in carrying out their civil and criminal responsibilities. For more information about the National State Attorneys General Program and resources, please visit our website www.stateag.org. 1 TABLE OF CONTENTS CONSUMER PROTECTION CASES, SETTLEMENTS AND -
Dial One for Scam: a Large-Scale Analysis of Technical Support Scams
Dial One for Scam: A Large-Scale Analysis of Technical Support Scams Najmeh Miramirkhani Oleksii Starov Nick Nikiforakis Stony Brook University Stony Brook University Stony Brook University [email protected] [email protected] [email protected] Abstract—In technical support scams, cybercriminals attempt In this paper, we perform a three-pronged analysis of to convince users that their machines are infected with malware the increasingly serious problem of technical support scams. and are in need of their technical support. In this process, the First, we build a reliable, distributed crawling infrastructure victims are asked to provide scammers with remote access to their that can identify technical support scam pages and use it to machines, who will then “diagnose the problem”, before offering collect technical support scam pages from websites known their support services which typically cost hundreds of dollars. to participate in malvertising activities. By deploying this Despite their conceptual simplicity, technical support scams are responsible for yearly losses of tens of millions of dollars from infrastructure, in a period of 250 days, we discover 8,698 unique everyday users of the web. domain names involved in technical support scams, claiming that users are infected and urging them to call one of the In this paper, we report on the first systematic study of 1,581 collected phone numbers. To the best of our knowledge, technical support scams and the call centers hidden behind them. our system is the first one that can automatically discover We identify malvertising as a major culprit for exposing users hundreds of domains and numbers belonging to technical to technical support scams and use it to build an automated system capable of discovering, on a weekly basis, hundreds of support scammers every week, without relying on manual labor phone numbers and domains operated by scammers.