Malware – What Is It? What Is the Difference Between Malware and Spam? How Do You Protect Yourself from Both? Malware
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Trojan Vs Rat Vs Rootkit Mayuri More1, Rajeshwari Gundla2, Siddharth Nanda3 1U.G
IJRECE VOL. 7 ISSUE 2 (APRIL- JUNE 2019) ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) Trojan Vs Rat Vs Rootkit Mayuri More1, Rajeshwari Gundla2, Siddharth Nanda3 1U.G. Student, 2 Senior Faculty, 3Senior Faculty SOE, ADYPU, Lohegaon, Pune, Maharashtra, India1 IT, iNurture, Bengaluru, India2,3 Abstract - Malicious Software is Malware is a dangerous of RATs completely and prevent confidential data being software which harms computer systems. With the increase leaked. So Dan Jiang and Kazumasa Omote researchers in technology in today’s days, malwares are also increasing. have proposed an approach to detect RAT in the early stage This paper is based on Malware. We have discussed [10]. TROJAN, RAT, ROOTKIT in detail. Further, we have discussed the adverse effects of malware on the system as III. CLASSIFICATION well as society. Then we have listed some trusted tools to Rootkit vs Trojan vs Rat detect and remove malware. Rootkit - A rootkit is a malicious software that permits a legitimate user to have confidential access to a system and Keywords - Malware, Trojan, RAT, Rootkit, System, privileged areas of its software. A rootkit possibly contains Computer, Anti-malware a large number of malicious means for example banking credential stealers, keyloggers, antivirus disablers, password I. INTRODUCTION stealers and bots for DDoS attacks. This software stays Nowadays, this world is full of technology, but with the hidden in the computer and allocates the remote access of advantages of technology comes its disadvantages like the computer to the attacker[2]. hacking, corrupting the systems, stealing of data etc. These Types of Rootkit: malpractices are possible because of malware and viruses 1. -
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. -
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. -
Adware-Searchsuite
McAfee Labs Threat Advisory Adware-SearchSuite June 22, 2018 McAfee Labs periodically publishes Threat Advisories to provide customers with a detailed analysis of prevalent malware. This Threat Advisory contains behavioral information, characteristics and symptoms that may be used to mitigate or discover this threat, and suggestions for mitigation in addition to the coverage provided by the DATs. To receive a notification when a Threat Advisory is published by McAfee Labs, select to receive “Malware and Threat Reports” at the following URL: https://www.mcafee.com/enterprise/en-us/sns/preferences/sns-form.html Summary Detailed information about the threat, its propagation, characteristics and mitigation are in the following sections: Infection and Propagation Vectors Mitigation Characteristics and Symptoms Restart Mechanism McAfee Foundstone Services The Threat Intelligence Library contains the date that the above signatures were most recently updated. Please review the above mentioned Threat Library for the most up to date coverage information. Infection and Propagation Vectors Adware-SearchSuite is a "potentially unwanted program" (PUP). PUPs are any piece of software that a reasonably security- or privacy-minded computer user may want to be informed of and, in some cases, remove. PUPs are often made by a legitimate corporate entity for some beneficial purpose, but they alter the security state of the computer on which they are installed, or the privacy posture of the user of the system, such that most users will want to be aware of them. Mitigation Mitigating the threat at multiple levels like file, registry and URL could be achieved at various layers of McAfee products. Browse the product guidelines available here (click Knowledge Center, and select Product Documentation from the Support Content list) to mitigate the threats based on the behavior described in the Characteristics and symptoms section. -
Crimeware on the Net
Crimeware on the Net The “Behind the scenes” of the new web economy Iftach Ian Amit Director, Security Research – Finjan BlackHat Europe, Amsterdam 2008 Who Am I ? (iamit) • Iftach Ian Amit – In Hebrew it makes more sense… • Director Security Research @ Finjan • Various security consulting/integration gigs in the past – R&D – IT • A helping hand when needed… (IAF) 2 BlackHat Europe – Amsterdam 2008 Today’s Agenda • Terminology • Past vs. Present – 10,000 feet view • Business Impact • Key Characteristics – what does it look like? – Anti-Forensics techniques – Propagation methods • What is the motive (what are they looking for)? • Tying it all up – what does it look like when successful (video). • Anything in it for us to learn from? – Looking forward on extrusion testing methodologies 3 BlackHat Europe – Amsterdam 2008 Some Terminology • Crimeware – what we refer to most malware these days is actually crimeware – malware with specific goals for making $$$ for the attackers. • Attackers – not to be confused with malicious code writers, security researchers, hackers, crackers, etc… These guys are the Gordon Gecko‟s of the web security field. The buy low, and capitalize on the investment. • Smart (often mislead) guys write the crimeware and get paid to do so. 4 BlackHat Europe – Amsterdam 2008 How Do Cybercriminals Steal Business Data? Criminals’ activity in the cyberspace Federal Prosecutor: “Cybercrime Is Funding Organized Crime” 5 BlackHat Europe – Amsterdam 2008 The Business Impact Of Crimeware Criminals target sensitive business data -
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. -
SMM Rootkits
SMM Rootkits: A New Breed of OS Independent Malware Shawn Embleton Sherri Sparks Cliff Zou University of Central Florida University of Central Florida University of Central Florida [email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION The emergence of hardware virtualization technology has led to A rootkit consists of a set of programs that work to subvert the development of OS independent malware such as the Virtual control of an Operating System from its legitimate users [16]. If Machine based rootkits (VMBRs). In this paper, we draw one were asked to classify viruses and worms by a single defining attention to a different but related threat that exists on many characteristic, the first word to come to mind would probably be commodity systems in operation today: The System Management replication. In contrast, the single defining characteristic of a Mode based rootkit (SMBR). System Management Mode (SMM) rootkit is stealth. Viruses reproduce, but rootkits hide. They hide is a relatively obscure mode on Intel processors used for low-level by compromising the communication conduit between an hardware control. It has its own private memory space and Operating System and its users. Secondary to hiding themselves, execution environment which is generally invisible to code rootkits are generally capable of gathering and manipulating running outside (e.g., the Operating System). Furthermore, SMM information on the target machine. They may, for example, log a code is completely non-preemptible, lacks any concept of victim user’s keystrokes to obtain passwords or manipulate the privilege level, and is immune to memory protection mechanisms. -
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 ......................................................................................................................................... -
Trojans and Malware on the Internet an Update
Attitude Adjustment: Trojans and Malware on the Internet An Update Sarah Gordon and David Chess IBM Thomas J. Watson Research Center Yorktown Heights, NY Abstract This paper continues our examination of Trojan horses on the Internet; their prevalence, technical structure and impact. It explores the type and scope of threats encountered on the Internet - throughout history until today. It examines user attitudes and considers ways in which those attitudes can actively affect your organization’s vulnerability to Trojanizations of various types. It discusses the status of hostile active content on the Internet, including threats from Java and ActiveX, and re-examines the impact of these types of threats to Internet users in the real world. Observations related to the role of the antivirus industry in solving the problem are considered. Throughout the paper, technical and policy based strategies for minimizing the risk of damage from various types of Trojan horses on the Internet are presented This paper represents an update and summary of our research from Where There's Smoke There's Mirrors: The Truth About Trojan Horses on the Internet, presented at the Eighth International Virus Bulletin Conference in Munich Germany, October 1998, and Attitude Adjustment: Trojans and Malware on the Internet, presented at the European Institute for Computer Antivirus Research in Aalborg, Denmark, March 1999. Significant portions of those works are included here in original form. Descriptors: fidonet, internet, password stealing trojan, trojanized system, trojanized application, user behavior, java, activex, security policy, trojan horse, computer virus Attitude Adjustment: Trojans and Malware on the Internet Trojans On the Internet… Ever since the city of Troy was sacked by way of the apparently innocuous but ultimately deadly Trojan horse, the term has been used to talk about something that appears to be beneficial, but which hides an attack within. -
Rootkits, Part 1 of 3: the Growing Threat
White Paper | April 2006 Rootkits, Part 1 of 3: The Growing Threat www.mcafee.com White Paper | 2006 Page Table of Contents Key Findings 3 Abstract 3 A Brief History of Stealth Malware (a.k.a. Rootkits) 3 Rootkits, Malware, and Controversy 4 Rootkit Technology Trends 5 Works Cited 7 www.mcafee.com White Paper | 2006 Page Key Findings netstat, ls, and passwd. Because these same tools could be used by an attacker to hide any trace of intrusion, the term 1. In just three short years, the use of stealth techniques in rootkit became associated with stealth. When these same malicious software (malware) has grown by more than strategies were applied to the Windows environment, the 600 percent. rootkit name transferred with them. Today, rootkit is a term . From 000 to 005, rootkit complexity grew by more commonly used to describe malware—such as Trojans, than 400 percent, and year-over-year, Q1 2005 to 2006, worms, and viruses—that actively conceals its existence complexity has grown by more than 900 percent. and actions from users and other system processes. The share of Linux-based techniques has gone from The practice of hiding malware from the prying eyes of a high of roughly 71 percent of all malware stealth users and security products dates back to the very first PC 1 components in 001 to a negligible number in virus, Brain, which was released in 1986. Brain escaped 005, while the number of Windows®-based stealth detection by intercepting PC boot-sector interrogations and components has increased by ,00 percent in the same redirecting the read operations to elsewhere on the disk. -
An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers
applied sciences Article An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers Riaz Ullah Khan 1,* , Xiaosong Zhang 1, Rajesh Kumar 1 , Abubakar Sharif 1, Noorbakhsh Amiri Golilarz 1 and Mamoun Alazab 2 1 Center of Cyber Security, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; [email protected] (X.Z.); [email protected] (R.K.); [email protected] (A.S.); [email protected] (N.A.G.) 2 College of Engineering, IT and Environment, Charles Darwin University, Casuarina 0810, Australia; [email protected] * Correspondence: [email protected]; Tel.: +86-155-2076-3595 Received: 19 March 2019; Accepted: 24 April 2019; Published: 11 June 2019 Abstract: In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. -
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.