Identifying Threats Associated with Man-In-The-Middle Attacks During Communication Between a Mobile Device and the Back End Server in Mobile Banking Applications
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100169 V2 BCG Mobile Malware Infographic
KNOW THY With mobile usage at an all time high, malware specifically designed for smartphones has become more prevalent and sophisticated. MOBILE ENEMY. We’re here to help. Mobile malware can take on many different forms: POTENTIALLY UNWANTED SOFTWARE (PUS) The Basics How PUS Starts Signs of a PUS Attack • Often poses as • Users allow permission • Sudden increase in junk antivirus software because attack poses as SMS texts antivirus software • Similar to adware • Data stolen from your or spyware contacts list and shared with third parties • Millions of variations already exist RANSOMWARE The Basics Complete Anonymity Ransomware & Fear • Advanced cryptographic • Assailants demand • Most aren’t likely to report Accept threats that hold untraceable ransom ransomware acquired from les hostage payment (Bitcoin) embarrassing sources (ie. porn) • Ransom is due within • Attackers use Tor network a strict time limit to hide destination • Often payment doesn’t mean before les become of payment the bad guys uphold their permanently inaccessible end of the bargain • .onion addresses often used in ransom demands How Ransomware Starts • Installing risky mobile apps from insecure websites INFORMATION LEAKAGE Every Move is Monitored IMEI Identifier Broadcast Personal Privacy Threats Within Mobile Network • Often results from app • Can lead to cloned • Utilize GPS satellite designers who don’t phones where service systems to create digital encrypt or do it wrong is hijacked “breadcrumbs” showing activity • Reveal where people live, work, socialize, etc. using social networking options TOP TWO INFECTION VECTORS MIXING BUSINESS WITH PLEASURE Users now have one device for everything— #1 Porn #2Suspicious chances of personal use impacting business networks at 36% WebAd networks/large networks are higher than ever. -
Recent Developments in Cybersecurity Melanie J
American University Business Law Review Volume 2 | Issue 2 Article 1 2013 Fiddling on the Roof: Recent Developments in Cybersecurity Melanie J. Teplinsky Follow this and additional works at: http://digitalcommons.wcl.american.edu/aublr Part of the Law Commons Recommended Citation Teplinsky, Melanie J. "Fiddling on the Roof: Recent Developments in Cybersecurity." American University Business Law Review 2, no. 2 (2013): 225-322. This Article is brought to you for free and open access by the Washington College of Law Journals & Law Reviews at Digital Commons @ American University Washington College of Law. It has been accepted for inclusion in American University Business Law Review by an authorized administrator of Digital Commons @ American University Washington College of Law. For more information, please contact [email protected]. ARTICLES FIDDLING ON THE ROOF: RECENT DEVELOPMENTS IN CYBERSECURITY MELANIE J. TEPLINSKY* TABLE OF CONTENTS Introduction .......................................... ..... 227 I. The Promise and Peril of Cyberspace .............. ........ 227 II. Self-Regulation and the Challenge of Critical Infrastructure ......... 232 III. The Changing Face of Cybersecurity: Technology Trends ............ 233 A. Mobile Technology ......................... 233 B. Cloud Computing ........................... ...... 237 C. Social Networking ................................. 241 IV. The Changing Face of Cybersecurity: Cyberthreat Trends ............ 244 A. Cybercrime ................................. ..... 249 1. Costs of Cybercrime -
MALWARE ACTIVITY DETECTION THROUGH BROWSER EXTENSION Jayanth Betha1, Prakash Andavolu2, Mariyala V V Gupta3 Department of Computer Science and Engineering, St
MALWARE ACTIVITY DETECTION THROUGH BROWSER EXTENSION Jayanth Betha1, Prakash Andavolu2, Mariyala V V Gupta3 Department of Computer Science and Engineering, St. Martin’s Engineering College, India. Abstract stretch out beyond the contaminated PC. A The drastic growth in Internet users and Malware infection may aim to damage the files Digital assets worldwide has created on storage devices; however, a keylogger is opportunities for cybercriminals to break utilized to take individual data, such as email into systems. The massive increase in ID's or passwords. While there are numerous malware and cybercrime is seen all over the approaches used to secure against keyloggers, globe; people have become more dependent this study mainly focuses on the detection of on the web environment. Malware (Malicious keyloggers. This research report will provide an Software), is a software that opens the door in-depth study on types of secretly monitoring for cybercriminals to access sensitive malware (keyloggers). A browser based solution information from the computing devices. In (browser extension) is needed to detect and alert the current technology-dependent world, the user about the keyloggers. attacks upon sensitive user information have In today’s technology-dependent world, continued to grow over time steadily. A attacks upon sensitive user information have common threat to data security is Malware. continued to evolve steadily over time. A Symantec discovered more than 430 million common threat to data security is Malware. new unique pieces of malware in 2015, up 36 According to (Symantec’s 2016 Internet percent from the year before. The objective of Security Threat Report. (n.d.). -
Mobile Malware
CS 155 Spring 2016 Mobile Malware John Mitchell Outline • Mobile malware • Identifying malware – Detect at app store rather than on platform • Classification study of mobile web apps – Entire Google Play market as of 2014 – 85% of approx 1 million apps use web interface • Target fragmentation in Android – Out-of-date Apps may disable more recent security platform patches Malware Trends W Based on FairPlay vulnerability • Requires malware on user PC, installation of malicious app in App Store • Continues to work after app removed from store • Silently installs app on phone Android malware 2015 Current Android Malware Description AccuTrack This application turns an Android smartphone into a GPS tracker. Ackposts This Trojan steals contact information from the compromised device and uploads them to a remote server. Acnetdoor This Trojan opens a backdoor on the infected device and sends the IP address to a remote server. Adsms This is a Trojan which is allowed to send SMS messages. The distribution channel ... is through a SMS message containing the download link. Airpush/StopSMS Airpush is a very aggresive Ad-Network. … BankBot This malware tries to steal users’ confidential information and money from bank and mobile accounts associated with infected devices. http://forensics.spreitzenbarth.de/android-malware/ Trends 2014-15 Android free antivirus apps … 1. Comodo Security & Antivirus 2. CM Security Antivirus AppLock 3. 360 Security - Antivirus Boost 4. Sophos Free Antivirus and Security 5. Malwarebytes Anti- Malware 6. Bitdefender Antivirus -
Compromised Connections
COMPROMISED CONNECTIONS OVERCOMING PRIVACY CHALLENGES OF THE MOBILE INTERNET The Universal Declaration of Human Rights, the International Covenant on Civil and Political Rights, and many other international and regional treaties recognize privacy as a fundamental human right. Privacy A WORLD OF INFORMATION underpins key values such as freedom of expression, freedom of association, and freedom of speech, IN YOUR MOBILE PHONE and it is one of the most important, nuanced and complex fundamental rights of contemporary age. For those of us who care deeply about privacy, safety and security, not only for ourselves but also for our development partners and their missions, we need to think of mobile phones as primary computers As mobile phones have transformed from clunky handheld calling devices to nifty touch-screen rather than just calling devices. We need to keep in mind that, as the storage, functionality, and smartphones loaded with apps and supported by cloud access, the networks these phones rely on capability of mobiles increase, so do the risks to users. have become ubiquitous, ferrying vast amounts of data across invisible spectrums and reaching the Can we address these hidden costs to our digital connections? Fortunately, yes! We recommend: most remote corners of the world. • Adopting device, data, network and application safety measures From a technical point-of-view, today’s phones are actually more like compact mobile computers. They are packed with digital intelligence and capable of processing many of the tasks previously confined -
Mobile Financial Fraud April 2013
White Paper: Mobile Financial Fraud April 2013 Mobile Threats and the Underground Marketplace Principal Investigator and Corresponding Author Jart Armin Contributing Researchers Andrey Komarov, Mila Parkour, Raoul Chiesa, Bryn Thompson, Will Rogofsky Panel & Review Dr. Ray Genoe (UCD), Robert McArdle (Trend Micro), Dave Piscitello (ICANN), Foy Shiver (APWG), Edgardo Montes de Oca (Montimage), Peter Cassidy (APWG) APWG Mobile Fraud web site http://ecrimeresearch.org/wirelessdevice/Fraud/ Table of Contents Abstract ..................................................................................................................................... 2 Introduction and Starting Position ........................................................................................ 2 A Global Overview .................................................................................................................. 3 Vulnerabilities Overview ....................................................................................................... 3 The Underground Mobile Market ....................................................................................... 13 Mobile DNS & Traffic ........................................................................................................... 15 iBots & the Pocket Botnet ..................................................................................................... 18 Mobile Intrusion ................................................................................................................... -
Mobile Malware Security Challenges and Cloud-Based Detection
Mobile Malware Security Challeges and Cloud-Based Detection Nicholas Penning, Michael Hoffman, Jason Nikolai, Yong Wang College of Business and Information Systems Dakota State University Madison, SD 57042 {nfpenning, mjhoffman13054, janikolai}@pluto.dsu.edu, [email protected] Abstract— Mobile malware has gained significant ground since the techniques, privilege escalation, remote control, financial dawning of smartphones and handheld devices. TrendLabs charge, and information collection, etc. The previous stated estimated that there were 718,000 malicious and high risk Android techniques provide a malicious attacker with a variety of options apps in the second quarter of 2013. Mobile malware malicious to utilize a compromised mobile device. infections arise through various techniques such as installing repackaged legitimate apps with malware, updating current apps Many mobile malware prevention techniques are ported from that piggy back malicious variants, or even a drive-by download. desktop or laptop computers. However, due to the uniqueness of The infections themselves will perform at least one or multiple of smartphones [6], such as multiple-entrance open system, the following techniques, privilege escalation, remote control, platform-oriented, central data management, vulnerability to financial charge, and information collection, etc. This paper theft and lost, etc., challenges are also encountered when porting summarizes mobile malware threats and attacks, cybercriminal existing anti-malware techniques to mobile devices. These motivations behind malware, existing prevention methods and challenges include, inefficient security solutions, limitations of their limitations, and challenges encountered when preventing signature-based mobile malware detection, lax control of third malware on mobile devices. The paper further proposes a cloud- party app stores, and uneducated or careless users, etc. -
APT and Cybercriminal Targeting of HCS June 9, 2020 Agenda
APT and Cybercriminal Targeting of HCS June 9, 2020 Agenda • Executive Summary Slides Key: • APT Group Objectives Non-Technical: managerial, strategic • APT Groups Targeting Health Sector and high-level (general audience) • Activity Timeline Technical: Tactical / IOCs; requiring • TTPs in-depth knowledge (sysadmins, IRT) • Malware • Vulnerabilities • Recommendations and Mitigations TLP: WHITE, ID#202006091030 2 Executive Summary • APT groups steal data, disrupt operations, and destroy infrastructure. Unlike most cybercriminals, APT attackers pursue their objectives over longer periods of time. They adapt to cyber defenses and frequently retarget the same victim. • Common HPH targets include: • Healthcare Biotechnology Medical devices • Pharmaceuticals Healthcare information technology • Scientific research • HPH organizations who have been victim of APT attacks have suffered: • Reputational harm Disruption to operations • Financial losses PII/PHI and proprietary data theft • HC3 recommends several mitigations and controls to counter APT threats. TLP: WHITE, ID#202006091030 3 APT Group Objectives • Motivations of APT Groups which target the health sector include: • Competitive advantage • Theft of proprietary data/intellectual capital such as technology, manufacturing processes, partnership agreements, business plans, pricing documents, test results, scientific research, communications, and contact lists to unfairly advance economically. • Intelligence gathering • Groups target individuals and connected associates to further social engineering -
Mobile Malware Network View Kevin Mcnamee : Alcatel-Lucent Agenda
Mobile Malware Network View Kevin McNamee : Alcatel-Lucent Agenda • Introduction • How the data is collected • Lies, Damn Lies and Statistics • Windows PC Malware • Android Malware • Network Impact • Examples of malware • DDOS Monitoring the Mobile Network • Monitor GTP-C traffic MOBILE NETWORK SECURITY ANALYTICS - Maps IMSI, APN & EMEI to IP address - Associates infection with a specific device or user Forensic Analysis • Monitor GTP-U traffic Alert - Malware C&C Aggregation & Analysis - Exploits Malware - DDOS Detection Sensor - Hacking 10GE or GE NodeB SGSN RAN Alternate Tap Recommended Alternate tap RNC (Iu-PS and S1-u) Tap (Gn and choice (Gi and Internet S5/8) SGi) GGSN/PGW eNodeB SGW 3 Monitoring the Mobile Network • Analytics Provides MOBILE NETWORK SECURITY ANALYTICS - Raw security alerts - Trigger packets - Infection history by device Forensic Analysis - Infection history by malware Alert • Reports Aggregation & Analysis - Most active malware Malware Detection - Network impact Sensor - Infection rates 10GE or GE NodeB SGSN RAN Alternate Tap Recommended Alternate tap RNC (Iu-PS and S1-u) Tap (Gn and choice (Gi and Internet S5/8) SGi) GGSN/PGW eNodeB SGW 4 Detection Rules Development Process MALWARE TRAFFIC SAMPLES POLICY MALWARE ZERO DAY RULES TRAFFIC BEHAVIORAL VIRUS VAULT SANDBOX DEVELOPMENT LIBRARY RULES LIBRARY RULES • 120K+ ANALYZED PER DAY • 30M+ Active samples RULE ACTIVATION RULES REPOSITORY DEPLOYMENT-SPECIFIC RULE SETS FEEDBACK FROM FIELD QUALITY TESTS TESTING FIELD TESTING IN LIVE NETWORKS 5 Infection Rate • C&C detection measures actual infections as seen from the network • Problem is growing (by 25% in 2014) • LTE device 5 times more likely to be infected 6 Estimates of Mobile Malware infection However… rates vary wildly - ALU, Lookout & Google report 0.4% to 0.6% - Verizon Breach Report quoted 0.03% - Damballa quoted 0.0064% at RSA. -
Kaspersky Security Bulletin 2020. Statistics Kaspersky Security Bulletin 2020
Kaspersky Security Bulletin 2020. Statistics Kaspersky Security Bulletin 2020. Statistics Contents Figures of the year 3 Financial threats 4 Number of users attacked by banking malware 4 Attack geography 5 Top 10 financial malware families 6 Ransomware programs 7 Number of users attacked by ransomware Trojans 7 Attack geography 8 Miners 10 Number of users attacked by miners 10 Attack geography 11 Vulnerable applications used by cybercriminals during cyber attacks 12 Attacks on macOS 14 Threat geography 15 IoT attacks 17 IoT threat statistics 17 Threats loaded into traps 19 Attacks via web resources 20 Countries that are sources of web-based attacks: 20 Countries where users faced the greatest risk of online infection 21 Top 20 malicious programs most actively used in online attacks 22 Local threats 24 Top 20 malicious objects detected on user computers 24 Countries where users faced the highest risk of local infection 25 2 Kaspersky Security Bulletin 2020. Statistics Figures of the year • During the year, 10.18% of Internet user computers worldwide experienced at least one Malware-class attack. • Kaspersky solutions blocked 666,809,967 attacks launched from online resources in various countries across the world. • 173,335,902 unique URLs were recognized as malicious by Web Anti-Virus. • Our Web Anti-Virus blocked 33,412,568 unique malicious objects. • Ransomware attacks were defeated on the computers of 549,301 unique users. • During the reporting period, miners attacked 1,523,148 unique users. • Attempted infections by malware designed to steal money via online access to bank accounts were logged on the devices of 668,619 users. -
MOBILE MALWARE EVOLUTION 2016 Contents the Year in Figures
MOBILE MALWARE EVOLUTION 2016 Contents The year in figures ......................................................................................................................................... 2 Trends of the year .......................................................................................................................................... 2 Malicious programs using super-user rights ............................................................................................. 3 Cybercriminals continue their use of Google Play..................................................................................... 4 Bypassing Android’s protection mechanisms ............................................................................................ 6 Mobile ransomware................................................................................................................................... 7 A glance into the Dark Web. Contribution from INTERPOL’s Global Complex for Innovation. ..................... 8 Marketplaces ................................................................................................................................................. 8 Vendor shops, forums and social media ....................................................................................................... 9 Statistics ....................................................................................................................................................... 10 Geography of mobile threats .................................................................................................................. -
Detecting Botnets Using File System Indicators
Detecting botnets using file system indicators Master's thesis University of Twente Author: Committee members: Peter Wagenaar Prof. Dr. Pieter H. Hartel Dr. Damiano Bolzoni Frank Bernaards LLM (NHTCU) December 12, 2012 Abstract Botnets, large groups of networked zombie computers under centralised control, are recognised as one of the major threats on the internet. There is a lot of research towards ways of detecting botnets, in particular towards detecting Command and Control servers. Most of the research is focused on trying to detect the commands that these servers send to the bots over the network. For this research, we have looked at botnets from a botmaster's perspective. First, we characterise several botnet enhancing techniques using three aspects: resilience, stealth and churn. We see that these enhancements are usually employed in the network communications between the C&C and the bots. This leads us to our second contribution: we propose a new botnet detection method based on the way C&C's are present on the file system. We define a set of file system based indicators and use them to search for C&C's in images of hard disks. We investigate how the aspects resilience, stealth and churn apply to each of the indicators and discuss countermeasures botmasters could take to evade detection. We validate our method by applying it to a test dataset of 94 disk images, 16 of which contain C&C installations, and show that low false positive and false negative ratio's can be achieved. Approaching the botnet detection problem from this angle is novel, which provides a basis for further research.