Cyber Risk and Insurance Terminology 1 • Adware – Adware Refers
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A Honeypot for Arbitrary Malware on USB Storage Devices
A Honeypot for Arbitrary Malware on USB Storage Devices Sebastian Poeplau Jan Gassen University of Bonn Elmar Gerhards-Padilla Institute of Computer Science 4 Fraunhofer FKIE Friedrich-Ebert-Allee 144 Cyber Defense research group 53113 Bonn Friedrich-Ebert-Allee 144 53113 Bonn Abstract—Malware is a serious threat for modern information be infected usually by pretending to be vulnerable computers technology. It is therefore vital to be able to detect and analyze or services. Most commonly, honeypots are run on dedicated such malicious software in order to develop contermeasures. systems, i. e. machines that are maintained exclusively for the Honeypots are a tool supporting that task—they collect malware samples for analysis. Unfortunately, existing honeypots concen- purpose of capturing malware. Honeypots enable researchers trate on malware that spreads over networks, thus missing any and anti-virus companies to analyze new types of malware and malware that does not use a network for propagation. to develop countermeasures, and they are vital in generating A popular network-independent technique for malware to signatures of previously unknown malware. Even though there spread is copying itself to USB flash drives. In this article we are various different honeypot concepts, they all face one basic present Ghost, a new kind of honeypot for such USB malware. problem: How to trick malware into infecting a honeypot It detects malware by simulating a removable device in software, thereby tricking malware into copying itself to the virtual device. machine? We explain the concept in detail and evaluate it using samples Many of the employed concepts make use of the fact that of wide-spread malware. -
Malvertising - a Rising Threat to the Online Ecosystem
2016 Proceedings of the Conference on Information Systems Applied Research ISSN: 2167-1508 Las Vegas, Nevada USA v9 n4266 __________________________________________________________________________________________________________________________ Malvertising - A Rising Threat To The Online Ecosystem Catherine Dwyer [email protected] Ameet Kanguri [email protected] Seidenberg School of Computer Science & Information Systems Pace University New York, New York, USA Abstract Online advertising is a multi-billion dollar industry that supports web content providers around the globe. A sophisticated technology known as real time bidding (RTB) dominates the advertising landscape, connecting advertisers with specific online customers of interest. With RTB, when web visitors connect to a site, advertising networks are notified of space available on that site along with what can be gleaned about the visitor. These combinations of space and visitor are auctioned, and the winning bid’s ad content is served to the web visitor. The entire process, from a visitor landing on a publisher’s page to ads being auctioned, selected and served, takes 200 milliseconds, the time needed to snap your fingers. This tightly choreographed interaction is a technical marvel, but one with built in risks. The just-in-time collaboration between ever changing technology providers gives an opening to malicious actors, who through devious means, use ad networks to deliver malware rather than ads. Delivering malware as an ad is called malvertising, and its presence on otherwise credible sites is dangerous, undermining the business models of trustworthy publishers and legitimate online advertisers. The purpose of this paper is to introduce malvertising, describe its relationship with online advertising, and identify the risks RTB and malvertising bring to the online ecosystem. -
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. -
An API Honeypot for Ddos and XSS Analysis
An API Honeypot for DDoS and XSS Analysis G Leaden, Marcus Zimmermann, Casimer DeCusatis, Fellow, IEEE, and Alan G. Labouseur Marist College School of Computer Science and Mathematics Poughkeepsie, NY 12601 fG.Leaden1, Marcus.Zimmermann1, Casimer.DeCusatis, [email protected] Abstract—Honeypots are servers or systems built to mimic II. BACKGROUND critical parts of a network, distracting attackers while logging their information to develop attack profiles. This paper discusses Not long after making G-Star Studio available online, we the design and implementation of a honeypot disguised as a REp- observed a number of unauthorized connection attempts to resentational State Transfer (REST) Application Programming its Application Programming Interface (API). These attacks Interface (API). We discuss the motivation for this work, design specifically targeted G-star Studio’s REpresentational State features of the honeypot, and experimental performance results Transfer (REST) API. under various traffic conditions. We also present analyses of both a distributed denial of service (DDoS) attack and a cross-site A REST API is among the most commonly used API scripting (XSS) malware insertion attempt against this honeypot. architectures today [6]. There are many examples of recent attacks against REST APIs, including well-publicized attacks on the Nissan Leaf smart car [7] and the U.S. Internal Revenue Service database. Existing APIs do not always follow security I. INTRODUCTION best practices, and developers might be lulled into a false sense of security by believing that their API will not be an attack The number and severity of cyber attacks has grown signif- target. In an effort to improve the security of our SecureCloud icantly in recent years [1], [2]. -
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. -
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 ......................................................................................................................................... -
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. -
Malware to Crimeware
I have surveyed over a decade of advances in delivery of malware. Over this daVid dittRich period, attackers have shifted to using complex, multi-phase attacks based on malware to crimeware: subtle social engineering tactics, advanced how far have they cryptographic techniques to defeat takeover gone, and how do and analysis, and highly targeted attacks we catch up? that are intended to fly below the radar of current technical defenses. I will show how Dave Dittrich is an affiliate information malicious technology combined with social security researcher in the University of manipulation is used against us and con- Washington’s Applied Physics Laboratory. He focuses on advanced malware threats and clude that this understanding might even the ethical and legal framework for respond- ing to computer network attacks. help us design our own combination of [email protected] technical and social mechanisms to better protect us. And ye shall know the truth, and the truth shall make you free. The late 1990s saw the advent of distributed and John 8:32 coordinated computer network attack tools, which were primarily used for the electronic equivalent of fist fighting in the streets. It only took a few years for criminal activity—extortion, click fraud, denial of service for competitive advantage—to appear, followed by mass theft of personal and financial data through quieter, yet still widespread and auto- mated, keystroke logging. Despite what law-abid- ing citizens would desire, crime does pay, and pay well. Today, the financial gain from criminal enter- prise allows investment of large sums of money in developing tools and operational capabilities that are increasingly sophisticated and highly targeted. -
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 Review on Honeypot-Based Botnet Detection Models for Smart Factory
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 6, 2020 A Review on Honeypot-based Botnet Detection Models for Smart Factory Lee Seungjin1, Azween Abdullah2, NZ Jhanjhi3 School of Computer Science and Engineering (SCE) Taylor‟s University Subang Jaya, Selangor, Malaysia Abstract—Since the Swiss Davos Forum in January 2017, the production line and is expected to change from mass most searched keywords related to the Fourth Revolutionary customization to flexible production systems through Industry are AI technology, big data, and IoT. In particular, the modularization. It is possible to save energy by changing from manufacturing industry seeks to advance information and a person-centered working environment to an ICT-oriented communication technology (ICT) to build a smart factory that one, and it is expected that the productivity of the integrates the management of production processes, safety, manufacturing industry will increase [2], Various possibilities procurement, and logistics services. Such smart factories can for the transition to smart factories are recognized. It is effectively solve the problem of frequent occurrences of accidents predicted that it will be able to monitor and control and high fault rates. An increasing number of cases happening in manufacturing sites via virtual space, making it easier to smart factories due to botnet DDoS attacks have been reported in manage factories. It will enhance competitiveness in quality recent times. Hence, the Internet of Thing security is of paramount importance in this emerging field of network security and cost [3]. Smart factories are closely linked to data by improvement. In response to the cyberattacks, smart factory application of the latest ICT technologies such as AI, security needs to gain its defending ability against botnet.