Phishing Made Easy: Time to Rethink Your Prevention Strategy?
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NEWS: Fake Whatsapp App May Have Been Built to Spy on Iphone Users – What You Need to Know a Fake Version of the Whatsapp Mess
NEWS: Fake WhatsApp app may have been built to spy on iPhone users – what you need to know A fake version of the WhatsApp messaging app is suspected of being created by an Italian spyware company to snoop upon individuals and steal sensitive data. The bogus app, uncovered by cybersecurity researchers at Citizen Lab and journalists at Motherboard, appears to be linked to an Italian firm called Cy4gate which develops “lawful interception” technology. https://hotforsecurity.bitdefender.com/blog/fake-whatsapp-app-may-have-been-built-to-spy-on-iphone- users-what-you-need-to-know-25270.html SOC teams spend nearly a quarter of their day handling suspicious emails Security professionals know that responding to relentless, incoming streams of suspicious emails can be a labor-intensive task, but a new study shared exclusively with SC Media in advance indicates just how time- consuming it actually is. Researchers at email security firm Avanan claim to have authored the “first comprehensive research study” that quantifies the amount of time security operations center (SOC) employees spend preventing, responding to, and investigating emails that successfully bypassed default security and are flagged by end users or other reporting mechanisms. https://www.scmagazine.com/home/email-security/soc-teams-spend-nearly-a-quarter-of-their-day- handling-suspicious-emails/ How to motivate employees to take cybersecurity seriously How can we push employees / users to take cybersecurity to heart? Dr. Maria Bada, external behavioral scientist at AwareGO, has been working on the answer for years. After studying media psychology, focusing her Ph.D. on behavior change, and working towards the treatment of excessive internet use in children and adolescents, nearly ten years ago she opted to join Oxford University as a postdoctoral researcher on cyberculture and online behavior. -
Web Shell 101 Joe Schottman Infosecon 2018 Oct
Web Shell 101 Joe Schottman InfoSeCon 2018 Oct. 26, 2018 About Me Senior Security Analyst for BB&T Legal Stuff 2 How To Reach Me @JoeSchottman on Twitter [email protected] www.joeschottman.com Add me on LinkedIn Find me on local Slacks 3 Agenda What is a Web Shell? How do Web Shells work? How can you detect them? Not going to cover how to use them 4 Definitions for this talk 5 If you’re playing security conference bingo 6 First, a diversion Equifax hack ▪ ▪ ▪ 8 Equifax hack 9 Equifax hack 10 Equifax hack 11 “ 12 “ 13 What is a Web Shell? 14 A subset of malware that runs on web servers 15 Used by APT groups 16 But also script kiddies 17 Someone else’s code ▪ PHP ▪ JSP ▪ Perl ▪ Ruby ▪ Python ▪ Shell Scripts ▪ ASP 18 Mostly scripting languages 19 Designed to control your server via HTTP 20 Imagine an evil web application 21 Executes just like your web applications 22 Unless the attacker takes steps to avoid it... 23 Used for different purposes 24 Hidden in different ways 25 How do they get on systems? 26 Web Shells are not the initial attack 27 Why at least two problems? ▪ ▪ ▪ 28 Let’s consider where in the attack Web Shells are used ▪ ▪ 29 Cyber Kill Chain 30 ATT&CK Discovery Lateral movement Collection Exfiltration Command and control 31 ATT&CK 32 Time to engage incident response ▪ ▪ ▪ 33 A funny aside 34 Metasploit makes some Web Shells easy 35 Detecting Web Shells Strategies 38 39 You do get permission before doing research, right? VirusTotal 41 File integrity monitoring 42 In an ideal world.. -
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. -
Zambia and Spam
ZAMNET COMMUNICATION SYSTEMS LTD (ZAMBIA) Spam – The Zambian Experience Submission to ITU WSIS Thematic meeting on countering Spam By: Annabel S Kangombe – Maseko June 2004 Table of Contents 1.0 Introduction 1 1.1 What is spam? 1 1.2 The nature of Spam 1 1.3 Statistics 2 2.0 Technical view 4 2.1 Main Sources of Spam 4 2.1.1 Harvesting 4 2.1.2 Dictionary Attacks 4 2.1.3 Open Relays 4 2.1.4 Email databases 4 2.1.5 Inadequacies in the SMTP protocol 4 2.2 Effects of Spam 5 2.3 The fight against spam 5 2.3.1 Blacklists 6 2.3.2 White lists 6 2.3.3 Dial‐up Lists (DUL) 6 2.3.4 Spam filtering programs 6 2.4 Challenges of fighting spam 7 3.0 Legal Framework 9 3.1 Laws against spam in Zambia 9 3.2 International Regulations or Laws 9 3.2.1 US State Laws 9 3.2.2 The USA’s CAN‐SPAM Act 10 4.0 The Way forward 11 4.1 A global effort 11 4.2 Collaboration between ISPs 11 4.3 Strengthening Anti‐spam regulation 11 4.4 User education 11 4.5 Source authentication 12 4.6 Rewriting the Internet Mail Exchange protocol 12 1.0 Introduction I get to the office in the morning, walk to my desk and switch on the computer. One of the first things I do after checking the status of the network devices is to check my email. -
" Detecting Malicious Code in a Web Server "
UNIVERSITY OF PIRAEUS DEPARTMENT OF DIGITAL SYSTEMS Postgraduate Programme "TECHNO-ECONOMIC MANAGEMENT & SECURITY OF DIGITAL SYSTEMS" " Detecting malicious code in a web server " Thesis of Soleas Agisilaos Α.Μ. : MTE14024 Supervisor Professor: Dr. Christoforos Ntantogian Athens, 2016 Table of Contents Abstract ....................................................................................................................................................... 3 1.Introduction .............................................................................................................................................. 4 2.What is a web shell .................................................................................................................................. 5 2.1.Web shell examples ................................................................................................................... 6 2.2.Web shell prevention .............................................................................................................. 24 2.3.What is a backdoor ................................................................................................................. 26 2.4.Known backdoors .................................................................................................................... 31 2.5.Prevent from backdoors ......................................................................................................... 32 3. NeoPi analysis ........................................................................................................................................ -
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. -
Using Web Honeypots to Study the Attackers Behavior
i 2017 ENST 0065 EDITE - ED 130 Doctorat ParisTech THÈSE pour obtenir le grade de docteur délivré par TELECOM ParisTech Spécialité « Informatique » présentée et soutenue publiquement par Onur CATAKOGLU le 30 Novembre 2017 Using Web Honeypots to Study the Attackers Behavior Directeur de thèse : Davide BALZAROTTI T H Jury È William ROBERTSON, Northeastern University Rapporteur Magnus ALMGREN, Chalmers University of Technology Rapporteur S Yves ROUDIER, University of Nice Sophia Antipolis Examinateur Albert LEVI, Sabanci University Examinateur E Leyla BILGE, Symantec Research Labs Examinateur TELECOM ParisTech École de l’Institut Télécom - membre de ParisTech Using Web Honeypots to Study the Attackers Behavior Thesis Onur Catakoglu [email protected] École Doctorale Informatique, Télécommunication et Électronique, Paris ED 130 November 30, 2017 Advisor: Prof. Dr. Davide Balzarotti Examiners: EURECOM, Sophia-Antipolis Prof. Dr. Yves ROUDIER, University of Nice Sophia Antipolis Reviewers: Prof. Dr. William ROBERTSON, Prof. Dr. Albert LEVI, Northeastern University Sabanci University Prof. Dr. Magnus ALMGREN, Dr. Leyla BILGE, Chalmers University of Technology Symantec Research Labs Acknowledgements First and foremost I would like to express my gratitude to my supervisor, Davide Balzarotti. He was always welcoming whenever I need to ask questions, discuss any ideas and even when he was losing in our long table tennis matches. I am very thankful for his guidance throughout my PhD and I will always keep the mental image of him staring at me for various reasons as a motivation to move on when things will get tough in the future. I would also like to thank my reviewers for their constructive comments regarding this thesis. -
Backdoors in Software: a Cycle of Fear and Uncertainty
BACKDOORS IN SOFTWARE: A CYCLE OF FEAR AND UNCERTAINTY Leah Holden, Tufts University, Medford, MA Abstract: With the advent of cryptography and an increasingly online world, an antagonist to the security provided by encryption has emerged: backdoors. Backdoors are slated as insidious but often they aren’t intended to be. Backdoors can just be abused maintenance accounts, exploited vulnerabilities, or created by another agent via infecting host servers with malware. We cannot ignore that they may also be intentionally programmed into the source code of an application. Like with the cyber attrition problem, it may be difficult to determine the root cause of a backdoor and challenging to prove its very existence. These characteristics of backdoors ultimately lead to a state of being that I liken to a cycle of fear where governments or companies throw accusations around, the public roasts the accused, and other companies live in fear of being next. Notably missing from this cycle are any concrete solutions for preventing backdoors. A pervasive problem underlies this cycle: backdoors could be mitigated if software developers and their managers fully embraced the concept that no one should ever be able to bypass authentication. From the Clipper chip to the FBI and Apple’s standoff, we will examine requested, suspected, and proven backdoors up through the end of 2017. We will also look at the aftermath of these incidents and their effect on the relationship between software and government. Introduction: A backdoor has been defined as both “an undocumented portal that allows an administrator to enter the system to troubleshoot or do upkeep” and “ a secret portal that hackers and intelligence agencies used to gain illicit access” (Zetter). -
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. -
Veracode.Com
Joe Brady Senior Solutions Architect [email protected] 1 Detecting Software Austin OWASP Backdoors August 30, 2011 Joe Brady Senior Solutions Architect Software Security Simplified [email protected] About • Veracode provides automated, SaaS-based, application security assessment and remediation capabilities for Internal , external and 3rd party Applications . • Automated techniques include static binary analysis and dynamic analysis . • Founded in 2006, includes application security experts from L 0pht, @stake, Guardent, Symantec, VeriSign and SPI Dynamics/Hewlett Packard 3 Now is a good time to think about software backdoors • Unverified and untested software is everywhere • It’s in your computer, house, car, phone, TV, printer and even refrigerator • Most of that software was developed by people you don’t trust or don’t know very well • You clicked on that link someone sent you didn’t you? What we will cover today (three things to worry think about) • Application Backdoors ‣ Backdoors in the applications you own, are buying or have built ‣ Do you know where your source code was last night? • System Backdoors ‣ Vulnerabilities in the software you use everyday that can be used to implant a system backdoor ‣ E.g. Aurora (CVE-2010-0249) • Mobile Backdoors ‣ Your phone just might be spying on you Attacker Motivation • Most practical method of compromise for many systems ‣ Let the users install your backdoor on systems you have no access to ‣ Looks like legitimate software so may bypass AV • Retrieve and manipulate valuable private data ‣ Looks like legitimate application traffic so little risk of detection by IDS and DLP • For high value targets it becomes cost effective and more reliable.