Mobile and Ubiquitous Malware
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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. -
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. -
Symbian Worm Yxes: Towards Mobile Botnets?
J Comput Virol (2012) 8:117–131 DOI 10.1007/s11416-012-0163-2 ORIGINAL PAPER Symbian worm Yxes: towards mobile botnets? Axelle Apvrille Received: 20 December 2010 / Accepted: 3 April 2012 / Published online: 1 May 2012 © Springer-Verlag France 2012 Abstract In 2009, a new Symbian malware named the costs and inconveniences caused by the famous Comm- SymbOS/Yxes was detected and quickly hit the headlines Warrior worm, with 115,000 infected phones [14] and over as one of the first malware for Symbian OS 9 and above 450,000 messages [16] sent prove the matter cannot be under- all as the foretaste of a mobile botnet. Yet, detailed analy- estimated. sis of the malware were still missing. This paper addresses At the beginning of 2009, a new Symbian malware was this issue and details how the malware silently connects to detected [13]. It installed on recent and widely-deployed the Internet, installs new malware or spreads to other victims. Symbian OS 9 phones. The malware looked perfectly legiti- Each of these points are illustrated with commented assembly mate and there was no hint it might be a malware apart from code taken from the malware or re-generated Symbian API the fact it created no application icon. As it was typically con- calls. Besides those implementation aspects, the paper also nected to the word “sexy” (sexy.sisx as package name, “Sexy provides a global overview of Yxes’s behaviour. It explains View”, “Sexy Girls”, “Sexy Space” as application name etc), how malicious remote servers participate in the configuration Fortinet christened it Yxes, i.e. -
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. -
Not So Secure! Research Report on End-Point Compromises and the Risk to Internet Banking
Not so secure! Research report on end-point compromises and the risk to Internet banking Special research report. August 2016 Internet Banking has grown by leaps and bounds in India. India had over 400 million Internet users by the beginning of 2016 as per a report by IAMAI and IMRB. This is the highest number of Internet banking users in any country. While having a high number of Internet banking users indicates healthy adoption of IT and reduced costs for banks, it also means that this population is vulnerable to cyberthreats which can put their hard-earned money at risk. One of the biggest addressable risks which users face is man-in-the-browser and end-point compromise attacks. “Virtual keyboards are ineffective in preventing the theft of end-user credentials and banks need to look at end point security more holistically. End-point compromise is an attack in which the end user’s computer and, in many cases these days, a browser is compromised using a simple program—generally a browser plugin or browser extension. The program is designed to steal the Internet banking credentials of the end users. Our security research team looked at the Internet banking websites of 26 banks in India. We found that over 21 banks have inadequate mechanisms to detect or prevent attacks which could compromise the end user credentials. This finding has significant implications as the end user is generally gullible, and many users can be made to download a browser extension/plugin which is capable of capturing banking credentials. Mechanisms which are currently prevalent, such as the virtual keyboard, which were long considered a protection against such attacks, were found to be inadequate to prevent these attacks. -
Contemporary Report
. .......... Mobile Phone and Infrastructure Vulnerabilities Summary and Analysis of Disclosed Issues 2005-2006 Prasanna Kannan Nesret Krasniqi The Thesis comprises 30 ECTS credits and is a compulsory part in the Master of Science with a Major in Master’s Programme in Electrical Engineering with specialisation in Communication and Signalprocessing, 120 ECTS credits Nr 9/2007 Mobile Phone and Infrastructure Vulnerabilities 2005 & 2006 Mobile Phone and Infrastructure Vulnerabilities – summary and analysis of disclosed issues 2005 - 2006 Prasanna Kannan Nesret Krasniqi Master thesis Series and Number Communication and Signal Processing 9/2007 University College of Borås School of Engineering SE-501 90 BORÅS Telephone +46 033 435 4640 Examiner: Jim Arlebrink Supervisor: Lennart Damm Client: OnePutt Solutions, Uppsala, Sweden. Date: 2007-11-08 Keywords: Mobile vulnerabilities , symbian viruses 1 Mobile Phone and Infrastructure Vulnerabilities 2005 & 2006 TABLE OF CONTENTS Abstract………………………………………………………………………………………………………………………10 Introduction……………………………………………………………………………………………………………..........11 About this report…………………………………………………………………………………………………………….12 What is a mobile phone virus and how does it spread? .............. ……………………………………………………………………114 List of vulnerabilities addressed…………………………………………………………………………………………….13 Statistics diagrams for 2005 and 2006………………………………………………………………………………………14 Overview of vulnerability families…………………………………………………………………………………………..16 Analysis………………………………………………………………………………………………………………………26 1. Skulls.D vulnerability -
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 -
Yet Another Symbian Vulnerability Update
Edith Cowan University Research Online Australian Information Security Management Conference Conferences, Symposia and Campus Events 11-30-2010 Yet Another Symbian Vulnerability Update Nizam Uddin Bhuiyan Edith Cowan University Follow this and additional works at: https://ro.ecu.edu.au/ism Part of the Information Security Commons DOI: 10.4225/75/57b523eccd8ae 8th Australian Information Security Mangement Conference, Edith Cowan University, Perth Western Australia, 30th November 2010 This Conference Proceeding is posted at Research Online. https://ro.ecu.edu.au/ism/101 Proceedings of the 8th Australian Information Security Management Conference Yet Another Symbian Vulnerability Update Nizam Uddin Bhuiyan School of Computer and Security Science Edith Cowan University Perth, Western Australia [email protected] Abstract The more the mobile devices are approaching to advance their security, the numbers of vulnerabilities are also becoming more astonishing. The number of mobile phones including smart phones is rising vertically, and so has the amount of malware activity. This report documents the latest threats in Symbian mobile industry and analyses the consequence. In addition, it will suggest the possible solution that may help individuals to protect their device & ultimately maintain the privacy. Keywords Symbian, Nokia vulnerability, Nokia threats, Mobile threat, Symbian malware, Symbian virus, Cell security, Consequences and counter measure for mobile threats. BACKGROUND The popularity of mobile has reached to sky high regardless whether you receive a blank SMS frequently or your mobile keeps sending a SMS to strangers persistently without your knowledge. According to Goode Intelligence 2009 Mobile Security Survey, “In the last couple of years, the percentage of mobile messaging traffic (SMS/MMS/e-mail) that is defined as spam or malware has risen from approximately 2% to between 20% and 30% of total traffic” (Francis, 2010).