Vulnerabilities Under Attack
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Automatic Classifying of Mac OS X Samples
Automatic Classifying of Mac OS X Samples Spencer Hsieh, Pin Wu and Haoping Liu Trend Micro Inc., Taiwan TREND MICRO LEGAL DISCLAIMER The information provided herein is for general information Contents and educational purposes only. It is not intended and should not be construed to constitute legal advice. The information contained herein may not be applicable to all situations and may not reflect the most current situation. Nothing contained herein should be relied on or acted 4 upon without the benefit of legal advice based on the particular facts and circumstances presented and nothing Introduction herein should be construed otherwise. Trend Micro reserves the right to modify the contents of this document at any time without prior notice. Translations of any material into other languages are intended solely as a convenience. Translation accuracy 6 is not guaranteed nor implied. If any questions arise related to the accuracy of a translation, please refer to Mac OS X Samples Dataset the original language official version of the document. Any discrepancies or differences created in the translation are not binding and have no legal effect for compliance or enforcement purposes. 10 Although Trend Micro uses reasonable efforts to include accurate and up-to-date information herein, Trend Micro makes no warranties or representations of any kind as Classification of Mach-O Files to its accuracy, currency, or completeness. You agree that access to and use of and reliance on this document and the content thereof is at your own risk. Trend Micro disclaims all warranties of any kind, express or implied. 11 Neither Trend Micro nor any party involved in creating, producing, or delivering this document shall be liable for any consequence, loss, or damage, including direct, Malware Families indirect, special, consequential, loss of business profits, or special damages, whatsoever arising out of access to, use of, or inability to use, or in connection with the use of this document, or any errors or omissions in the content 15 thereof. -
Watch out for Fake Virus Alerts
State of West Virginia Cyber Security Tip ALERT West Virginia Office of Information Security and Controls – Jim Richards, WV Chief Information Security Officer WATCH OUT FOR FAKE VIRUS ALERTS Rogue security software, also known as "scareware," is software that appears to be beneficial from a security perspective (i.e. free virus scan) but provides limited or no security, generates erroneous or misleading alerts, or attempts to lure users into participating in fraudulent transactions. How does rogue security software get on my computer? Rogue security software designers create legitimate looking pop-up windows that advertise security update software. These windows might appear on your screen while you surf the web. The "updates" or "alerts" in the pop-up windows call for you to take some sort of action, such as clicking to install the software, accept recommended updates, or remove unwanted viruses or spyware. When you click, the rogue security software downloads to your computer. Rogue security software might also appear in the list of search results when you are searching for trustworthy antispyware software, so it is important to protect your computer. What does rogue security software do? Rogue security software might report a virus, even though your computer is actually clean. The software might also fail to report viruses when your computer is infected. Inversely, sometimes, when you download rogue security software, it will install a virus or other malicious software on your computer so that the software has something to detect. Some rogue security software might also: Lure you into a fraudulent transaction (for example, upgrading to a non-existent paid version of a program). -
Analyzing Android Adware
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 2018 Analyzing Android Adware Supraja Suresh San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Computer Sciences Commons Recommended Citation Suresh, Supraja, "Analyzing Android Adware" (2018). Master's Projects. 621. DOI: https://doi.org/10.31979/etd.7xqe-kdft https://scholarworks.sjsu.edu/etd_projects/621 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. Analyzing Android Adware A Project Presented to The Faculty of the Department of Computer Science San Jose State University In Partial Fulfillment of the Requirements for the Degree Master of Science by Supraja Suresh May 2018 ○c 2018 Supraja Suresh ALL RIGHTS RESERVED The Designated Project Committee Approves the Project Titled Analyzing Android Adware by Supraja Suresh APPROVED FOR THE DEPARTMENTS OF COMPUTER SCIENCE SAN JOSE STATE UNIVERSITY May 2018 Dr. Mark Stamp Department of Computer Science Dr. Katerina Potika Department of Computer Science Fabio Di Troia Department of Mathematics ABSTRACT Analyzing Android Adware by Supraja Suresh Most Android smartphone apps are free; in order to generate revenue, the app developers embed ad libraries so that advertisements are displayed when the app is being used. Billions of dollars are lost annually due to ad fraud. In this research, we propose a machine learning based scheme to detect Android adware based on static and dynamic features. -
A Systematic Empirical Analysis of Unwanted Software Abuse, Prevalence, Distribution, and Economics
UNIVERSIDAD POLITECNICA´ DE MADRID ESCUELA TECNICA´ SUPERIOR DE INGENIEROS INFORMATICOS´ A Systematic Empirical Analysis of Unwanted Software Abuse, Prevalence, Distribution, and Economics PH.D THESIS Platon Pantelis Kotzias Copyright c 2019 by Platon Pantelis Kotzias iv DEPARTAMENTAMENTO DE LENGUAJES Y SISTEMAS INFORMATICOS´ E INGENIERIA DE SOFTWARE ESCUELA TECNICA´ SUPERIOR DE INGENIEROS INFORMATICOS´ A Systematic Empirical Analysis of Unwanted Software Abuse, Prevalence, Distribution, and Economics SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF: Doctor of Philosophy in Software, Systems and Computing Author: Platon Pantelis Kotzias Advisor: Dr. Juan Caballero April 2019 Chair/Presidente: Marc Dasier, Professor and Department Head, EURECOM, France Secretary/Secretario: Dario Fiore, Assistant Research Professor, IMDEA Software Institute, Spain Member/Vocal: Narseo Vallina-Rodriguez, Assistant Research Professor, IMDEA Networks Institute, Spain Member/Vocal: Juan Tapiador, Associate Professor, Universidad Carlos III, Spain Member/Vocal: Igor Santos, Associate Research Professor, Universidad de Deusto, Spain Abstract of the Dissertation Potentially unwanted programs (PUP) are a category of undesirable software that, while not outright malicious, can pose significant risks to users’ security and privacy. There exist indications that PUP prominence has quickly increased over the last years, but the prevalence of PUP on both consumer and enterprise hosts remains unknown. Moreover, many important aspects of PUP such as distribution vectors, code signing abuse, and economics also remain unknown. In this thesis, we empirically and sys- tematically analyze in both breadth and depth PUP abuse, prevalence, distribution, and economics. We make the following four contributions. First, we perform a systematic study on the abuse of Windows Authenticode code signing by PUP and malware. -
Internet Infrastructure Review Vol.34
Internet Infrastructure Review Mar.2017 Vol. 34 Infrastructure Security Ursnif (Gozi) Anti-Analysis Techniques and Methods for Bypassing Them Technology Trends The Current State of Library OSes Internet Infrastructure Review March 2017 Vol.34 Executive Summary ............................................................................................................................ 3 1. Infrastructure Security .................................................................................................................. 4 1.1 Introduction ..................................................................................................................................... 4 1.2 Incident Summary ........................................................................................................................... 4 1.3 Incident Survey ...............................................................................................................................11 1.3.1 DDoS Attacks ...................................................................................................................................11 1.3.2 Malware Activities ......................................................................................................................... 13 1.3.3 SQL Injection Attacks ..................................................................................................................... 17 1.3.4 Website Alterations ....................................................................................................................... -
Evolution of Iot Attacks
Evolution of IoT Attacks By 2025, there will be 41.6 billion connected IoT devices, generating 79 zettabytes (ZB) of data (IDC). Every Internet-connected “thing,” from power grids to smart doorbells, is at risk of attack. BLUETOOTH INDUSTRIAL MEDICAL Throughout the last few decades, cyberattacks against IoT devices have become more sophisticated, more common, and unfortunately, much BOTNET GENERIC IOT SMART HOME more effective. However, the technology sector has fought back, developing and implementing new security technologies to prevent and CAR INFRASTRUCTURE WATCH/WEARABLE protect against cyberattacks. This infographic shows how the industry has evolved with new technologies, protocols, and processes to raise the bar on cybersecurity. STUXNET WATER BMW MIRAI FITBIT SAUDI PETROL SILEX LINUX DARK NEXUS VIRUS UTILITY CONNECTED BOTNET VULNERABILITY CHEMICAL MALWARE BOTNET SYSTEM DRIVE SYSTEM PLANT ATTACK (SCADA) PUERTO UNIVERSITY UKRAINIAN HAJIME AMNESIA AMAZON PHILIPS HUE RICO OF MICHIGAN POWER VIGILANTE BOTNET RING HACK LIGHTBULB SMART TRAFFIC GRID BOTNET METERS LIGHTS MEDTRONIC GERMAN TESLA CCTV PERSIRAI FANCY SWEYNTOOTH INSULIN STEEL MODEL S BOTNET BOTNET BEAR VS. FAMILY PUMPS MILL HACK REMOTE SPORTS HACK HACKABLE HEART BASHLITE FIAT CHRYSLER NYADROP SELF- REAPER THINKPHP TWO MILLION KAIJI MONITORS BOTNET REMOTE CONTROL UPDATING MALWARE BOTNET EXPLOITATION TAKEOVER MALWARE First Era | THE AGE OF EXPLORATION | 2005 - 2009 Second Era | THE AGE OF EXPLOITATION | 2011 - 2019 Third Era | THE AGE OF PROTECTION | 2020 Security is not a priority for early IoT/embedded devices. Most The number of connected devices is exploding, and cloud connectivity Connected devices are ubiquitous in every area of life, from cyberattacks are limited to malware and viruses impacting Windows- is becoming commonplace. -
Common Threats to Cyber Security Part 1 of 2
Common Threats to Cyber Security Part 1 of 2 Table of Contents Malware .......................................................................................................................................... 2 Viruses ............................................................................................................................................. 3 Worms ............................................................................................................................................. 4 Downloaders ................................................................................................................................... 6 Attack Scripts .................................................................................................................................. 8 Botnet ........................................................................................................................................... 10 IRCBotnet Example ....................................................................................................................... 12 Trojans (Backdoor) ........................................................................................................................ 14 Denial of Service ........................................................................................................................... 18 Rootkits ......................................................................................................................................... 20 Notices ......................................................................................................................................... -
English Arabic Technical Computing Dictionary
English Arabic Technical Computing Dictionary Arabeyes Arabisation Team http://wiki.arabeyes.org/Technical Dictionary Versin: 0.1.29-04-2007 April 29, 2007 This is a compilation of the Technical Computing Dictionary that is under development at Arabeyes, the Arabic UNIX project. The technical dictionary aims to to translate and standardise technical terms that are used in software. It is an effort to unify the terms used across all Open Source projects and to present the user with consistant and understandable interfaces. This work is licensed under the FreeBSD Documentation License, the text of which is available at the back of this document. Contributors are welcome, please consult the URL above or contact [email protected]. Q Ì ÉJ ªË@ éÒ¢@ Ñ«YË QK AK.Q« ¨ðQåÓ .« èQK ñ¢ ÕæK ø YË@ úæ®JË@ úGñAm '@ ñÓA®ÊË éj èYë . l×. @QK. éÔg. QK ú¯ éÊÒªJÖÏ@ éJ J®JË@ HAjÊ¢Ö Ï@ YJ kñKð éÔg. QK úÍ@ ñÓA®Ë@ ¬YîE .ºKñJ ËAK. éîD J.Ë@ ÐYjJÒÊË éÒj. Óð éÓñê®Ó H. ñAg éêk. @ð Õç'Y®JË ð á ÔgQÖÏ@ á K. H. PAJË@ øXA®JË ,H. ñAmÌ'@ . ¾JÖ Ï .ñÓA®Ë@ éK AîE ú ¯ èQ¯ñJÖÏ@ ð ZAKñÊË ø X @ ú G. ø Q¯ ékP ù ë ñÓA®Ë@ ékP . éJ K.QªËAK. ÕÎ @ [email protected] . úΫ ÈAB@ ð@ èC«@ à@ñJªË@ úÍ@ H. AëYË@ ZAg. QË@ ,á ÒëAÖÏ@ ɾK. I. kQK A Abortive release êm .× (ú¾J.) ¨A¢®K@ Abort Aêk . @ Abscissa ú æJ Absolute address Ê¢Ó à@ñ J« Absolute pathname Ê¢Ó PAÓ Õæ @ Absolute path Ê¢Ó PAÓ Absolute Ê¢Ó Abstract class XQm.× J Abstract data type XQm.× HA KAJ K. -
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. -
Rethinking Security
RETHINKING SECURITY Fighting Known, Unknown and Advanced Threats kaspersky.com/business “Merchants, he said, are either not running REAL DANGERS antivirus on the servers managing point- of-sale devices or they’re not being updated AND THE REPORTED regularly. The end result in Home Depot’s DEMISE OF ANTIVIRUS case could be the largest retail data breach in U.S. history, dwarfing even Target.” 1 Regardless of its size or industry, your business is in real danger of becoming a victim of ~ Pat Belcher of Invincea cybercrime. This fact is indisputable. Open a newspaper, log onto the Internet, watch TV news or listen to President Obama’s recent State of the Union address and you’ll hear about another widespread breach. You are not paranoid when you think that your financial data, corporate intelligence and reputation are at risk. They are and it’s getting worse. Somewhat more controversial, though, are opinions about the best methods to defend against these perils. The same news sources that deliver frightening stories about costly data breaches question whether or not anti-malware or antivirus (AV) is dead, as reported in these articles from PC World, The Wall Street Journal and Fortune magazine. Reports about the death by irrelevancy of anti-malware technology miss the point. Smart cybersecurity today must include advanced anti-malware at its core. It takes multiple layers of cutting edge technology to form the most effective line of cyberdefense. This eBook explores the features that make AV a critical component of an effective cybersecurity strategy to fight all hazards targeting businesses today — including known, unknown and advanced cyberthreats. -
A Poisoned Apple: the Analysis of Macos Malware Shlayer By: Minh D
A Poisoned Apple: The Analysis of macOS Malware Shlayer by: Minh D. Nguyen Abstract Historically, the Microsoft Windows operating system family, which currently runs on more than 70 percent of computers in the world,7 has been the main target for malware. However, with the growing popularity of Apple’s MacBook products, the macOS operating system has become a new platform for attackers to target the general computer users. According to the 2016/2017 Security Report of AV-TEST, the number of malware samples for macOS detected in 2016 has increased by an astonishing 370 percent compared to the same figure in 2015.3 In order to address the rising interest of attackers in the macOS operating system, this project provides an analysis of a newly discovered malware for macOS, Shlayer, to reveal a well- known tactic that attackers can utilize to infect machines running on any operating system, and discusses possible countermeasures for this strategy. I. Introduction macOS is often hailed as a more secure operating system compared to its counterpart Microsoft Windows.2 However, in reality, many attacking techniques targeting Windows machines can also be applied to macOS machines. The analysis of the new Shlayer malware, discovered by researchers of Intego in February 2018,1 will reveal a familiar strategy that attackers often utilize to target victim machines without regards of the operating system. With the worldwide growth of macOS usage, it is important to recognize this attacking method and understand that in many cases, the success of an attack does not depend on the security of the operating system but on the awareness of the user. -
Cyren's 2016 Cyberthreat Report
2016 CYBERTHREAT Report AUTOMATED THREAT INTELLIGENCE: The Key to Preventing, Mitigating, and Identifying Cyber Breaches Introduction .................................................................................................4 The Cloud Sandbox Array: A New Tool Against Cybercrime .....................6 The Benefits of Big Data .......................................................................... 12 2016 Predictions....................................................................................... 14 Malware Newsmakers of 2015 ................................................................ 16 The Criminal Power of the Unknown ...................................................... 22 2015 Statistics: Android, Phishing, Malware, Spam ............................... 26 Table of Contents Table CYREN 2016 CYBERTHREAT REPORT 3 INTRODUCTION Lior Kohavi Chief Technical Officer, CYREN, Inc. There is a false perception that sophisticated attacks are too difficult to prevent and the only alternative is detection. But detection is NOT the new prevention. Cybersecurity professionals must make it their mission to STOP attacks, not just become proficient at detecting them. It's no secret that cybercriminals are willing to spend a lot of time and money to obtain the information they desire. And, the risk that these criminals will be caught and convicted is relatively low. Despite well-publicized botnet takedowns, like that of Darknode this past July, researchers estimate that less than 1% of cybercrimes receive a corresponding conviction.