Not So Secure! Research Report on End-Point Compromises and the Risk to Internet Banking
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Identifying Threats Associated with Man-In-The-Middle Attacks During Communication Between a Mobile Device and the Back End Server in Mobile Banking Applications
IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. IX (Mar-Apr. 2014), PP 35-42 www.iosrjournals.org Identifying Threats Associated With Man-In-The-Middle Attacks during Communication between a Mobile Device and the Back End Server in Mobile Banking Applications Anthony Luvanda1,*Dr Stephen Kimani1 Dr Micheal Kimwele1 1. School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, PO Box 62000-00200 Nairobi Kenya Abstract: Mobile banking, sometimes referred to as M-Banking, Mbanking or SMS Banking, is a term used for performing balance checks, account transactions, payments, credit applications and other banking transactions through a mobile device such as a mobile phone or Personal Digital Assistant (PDA). Mobile banking has until recently most often been performed via SMS or the Mobile Web. Apple's initial success with iPhone and the rapid growth of phones based on Google's Android (operating system) have led to increasing use of special client programs, called apps, downloaded to the mobile device hence increasing the number of banking applications that can be made available on mobile phones . This in turn has increased the popularity of mobile device use in regards to personal banking activities. Due to the characteristics of wireless medium, limited protection of the nodes, nature of connectivity and lack of centralized managing point, wireless networks tend to be highly vulnerable and more often than not they become subjects of attack. This paper proposes to identify potential threats associated with communication between a mobile device and the back end server in mobile banking applications. -
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. -
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 -
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. -
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. -
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 .................................................................................................................. -
Android Malware Category and Family Detection and Identification Using Machine Learning
Android Malware Category and Family Detection and Identification using Machine Learning Ahmed Hashem El Fiky1*, Ayman El Shenawy1, 2, Mohamed Ashraf Madkour1 1 Systems and Computer Engineering Dept., Faculty of Engineering, Al-Azhar University, Cairo, Egypt. 1 Systems and Computer Engineering Dept., Faculty of Engineering, Al-Azhar University, Cairo, Egypt. 2 Software Engineering and Information Technology, Faculty of Engineering and Technology, Egyptian Chinese University, Cairo, Egypt. [email protected] [email protected] [email protected] Abstract: Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applications, there is still a long way to go. As a result, there is a need to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Each Android malware family and category has a distinct objective. As a result, it has impacted every corporate area, including healthcare, banking, transportation, government, and e-commerce. In this paper, we presented two machine- learning approaches for Dynamic Analysis of Android Malware: one for detecting and identifying Android Malware Categories and the other for detecting and identifying Android Malware Families, which was accomplished by analyzing a massive malware dataset with 14 prominent malware categories and 180 prominent malware families of CCCS-CIC- AndMal2020 dataset on Dynamic Layers. Our approach achieves in Android Malware Category detection more than 96 % accurate and achieves in Android Malware Family detection more than 99% accurate. Our approach provides a method for high-accuracy Dynamic Analysis of Android Malware while also shortening the time required to analyze smartphone malware. -
Microsoft Security Intelligence Report
Microsoft Security Intelligence Report Volume 12 July through December, 2011 www.microsoft.com/sir Microsoft Security Intelligence Report This document is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet website references, may change without notice. You bear the risk of using it. Copyright © 2012 Microsoft Corporation. All rights reserved. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. JULY–DECEMBER 2011 i Authors Dennis Batchelder David Felstead Ken Malcolmson Tim Rains Microsoft Protection Bing Microsoft Trustworthy Microsoft Trustworthy Technologies Computing Computing Paul Henry Shah Bawany Wadeware LLC Nam Ng Frank Simorjay Microsoft Windows Safety Microsoft Trustworthy Microsoft Trustworthy Platform Nitin Kumar Goel Computing Computing Microsoft Security Joe Blackbird Response Center Mark Oram Holly Stewart Microsoft Malware Microsoft Trustworthy Microsoft Malware Protection Center Jeff Jones Computing Protection Center Microsoft Trustworthy Eve Blakemore Computing Daryl Pecelj Matt Thomlinson Microsoft Trustworthy Microsoft IT Information Microsoft Trustworthy Computing Jimmy Kuo Security and Risk Computing Microsoft Malware Management Joe Faulhaber Protection Center Scott Wu Microsoft Malware Dave Probert Microsoft Malware Protection Center Marc Lauricella Microsoft -
Mobile Banking Adeptness on Man-In-The-Middle and Man-In-The-Browser Attacks
IOSR Journal of Mobile Computing & Application (IOSR-JMCA) e-ISSN: 2394-0050, P-ISSN: 2394-0042.Volume 4, Issue 2 (Mar. - Apr. 2017), PP 13-19 www.iosrjournals.org Mobile Banking Adeptness on Man-In-The-Middle and Man-In-The-Browser Attacks P.S.Jagadeesh Kumar, Wenli Hu, Xianpei Li, Kundan Lal Abstract : A brand new category of perilous threat, asphyxiate the browser behavior in line of attack similar to that of Trojan horses. These immaculate varieties of Trojan horses can acclimatize the banking transaction activities, as they are muddled in browsers on top of mobile banking applications, and exhibit the user's transactions. Primarily, they are man-in-the-middle (MITM) attacks pedestal on users illuminating their testament on a deceptive website and man-in-the-browser (MITB) attacks that modify the exterior of indenture in the user’s browser. Repugnant to phishing attacks which depends upon similar but illusive websites, these malicious attacks cannot be professed by the user, as they are with genuine titivates, the user is desperately logged-in as standard, and consequently, falls prey in losing practical mobile banking utilities. Keywords: Man-In-The-Middle (MITM), Man-In-The-Browser (MITB), Mobile Banking, Phishing Attack I. Introduction Mobile conventional password based consumer substantiation has been a distinguished sanctuary predicament. Even a primordial and tenuously accomplished phishing attack can be effectual for an effortless password thievery. Attributable to accumulate of phishing and connected attacks, susceptible requests such as online banking gradually more desired for more safe and sound authentication substitutes. Their clarifications, typically drop into a collection called two-step verification. -
A Survey on Keylogger: a Malicious Attack
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 4, April 2015 A Survey on Keylogger: A malicious Attack NameHemita Pathak, Apurva Pawar, Balaji Patil are depicting working of keylogger. Section V suggests Abstract—Now a days, internet has become part of basic various prevention and detection methods. We enlist different needs for modern society. People are using internet for online applications of keylogger in section VI. Finally, we conclude banking and confidensial information sharing through email our paper in section VII. and chats on social networking sites. Intruters are using malwares to damage systems. Keylogger is one of the harmful II. DIFFERENT PASSWORD ATTACKS malwares. Keylogger tries to obtain private information through monitoring keystroke and communicating this data to For authentication of any system password is first and intruter with malicious intentions. And thus, keylogger is said to foremost step so, passwords play an important role in daily be a main threat for business and personal activities. To prevent life in various computing applications like ATM machines, from huge economical/personal loss, system needs to protect internet services, windows login, authentication in mobiles valuable information from Intruters. But before planning for etc. Intruders/hackers can make system vulnerable, can get prevention it is necessary to be aware of few things like key access of it and can also get valuable information of ours. In logger working. This paper gives a brief description of keylogger, it’s working, prevention detection of key logger and this section we enlisted some of possible password attacks. various applications of key logger. -
Mobile Malware and Spyware: Working Through the Bugs
Mobile Malware and Spyware: Working Through the Bugs Detective Cindy Murphy 608-267-8824 [email protected] The Mobile Malware Threat Year to Year Change in Number of • 155% increase in mobile Mobile Malware Samples malware from 2010 to 276259 2011 • 614% increase in mobile malware from March 2012 to March 2013 38689 28472 • Total samples across all 11138 platforms Feb 2010 Feb 2011 March 2012 March 2013 • Android represents for http://www.juniper.net/us/en/local/pdf/additional-resources/3rd- 92% all known mobile jnpr-mobile-threats-report-exec-summary.pdf malware infections Types of Mobile Malware & Potentially Unwanted Applications • Malware • Backdoor • Trojan • Worm • Ransomware • Potentially Unwanted Applications (PUA) • Adware • Trackware • Spyware Mobile Malware Infection Vectors • Third-party App Store repositories • Androids with outdated OS versions • Jailbroken iPhones • Unlocked Windows Phones • Malicious websites - “drive-by” download installation • Direct victim targeting through e-mail, SMS, and MMS • “Smishing” • Official App Stores • Emerging Methods • QR Codes • NFC Chips Signs & Symptoms of Mobile Malware Infection • Poor Battery Life • Dropped Calls and Call Disruption • Unusually Large Phone Bills • Data Plan Spikes • Performance Problems • Unexpected Device Behaviors • RISKY user behavior • Pirated apps for free • Porn surfing • “Free” Money Apps • AT RISK users OUCH!? • What are the implications of + hits on a case? • Can the presence of malware or spyware actually potentially help the investigation? Mobile