2017 State of Malware Report TABLE of CONTENTS
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Data-Driven Grasp Synthesis - a Survey Jeannette Bohg, Member, IEEE, Antonio Morales, Member, IEEE, Tamim Asfour, Member, IEEE, Danica Kragic Member, IEEE
TRANSACTIONS ON ROBOTICS 1 Data-Driven Grasp Synthesis - A Survey Jeannette Bohg, Member, IEEE, Antonio Morales, Member, IEEE, Tamim Asfour, Member, IEEE, Danica Kragic Member, IEEE specific metric. This process is usually based on some existing Abstract—We review the work on data-driven grasp synthesis grasp experience that can be a heuristic or is generated in and the methodologies for sampling and ranking candidate simulation or on a real robot. Kamon et al. [5] refer to this grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown as the comparative and Shimoga [2] as the knowledge-based objects. This structure allows us to identify common object rep- approach. Here, a grasp is commonly parameterized by [6, 7]: resentations and perceptual processes that facilitate the employed • the grasping point on the object with which the tool center data-driven grasp synthesis technique. In the case of known point (TCP) should be aligned, objects, we concentrate on the approaches that are based on • the approach vector which describes the 3D angle that object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching the robot hand approaches the grasping point with, to a set of previously encountered objects. Finally, for the • the wrist orientation of the robotic hand and approaches dealing with unknown objects, the core part is the • an initial finger configuration extraction of specific features that are indicative of good grasps. Data-driven approaches differ in how the set of grasp candi- Our survey provides an overview of the different methodologies dates is sampled, how the grasp quality is estimated and how and discusses open problems in the area of robot grasping. -
Post-Mortem of a Zombie: Conficker Cleanup After Six Years Hadi Asghari, Michael Ciere, and Michel J.G
Post-Mortem of a Zombie: Conficker Cleanup After Six Years Hadi Asghari, Michael Ciere, and Michel J.G. van Eeten, Delft University of Technology https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/asghari This paper is included in the Proceedings of the 24th USENIX Security Symposium August 12–14, 2015 • Washington, D.C. ISBN 978-1-939133-11-3 Open access to the Proceedings of the 24th USENIX Security Symposium is sponsored by USENIX Post-Mortem of a Zombie: Conficker Cleanup After Six Years Hadi Asghari, Michael Ciere and Michel J.G. van Eeten Delft University of Technology Abstract more sophisticated C&C mechanisms that are increas- ingly resilient against takeover attempts [30]. Research on botnet mitigation has focused predomi- In pale contrast to this wealth of work stands the lim- nantly on methods to technically disrupt the command- ited research into the other side of botnet mitigation: and-control infrastructure. Much less is known about the cleanup of the infected machines of end users. Af- effectiveness of large-scale efforts to clean up infected ter a botnet is successfully sinkholed, the bots or zom- machines. We analyze longitudinal data from the sink- bies basically remain waiting for the attackers to find hole of Conficker, one the largest botnets ever seen, to as- a way to reconnect to them, update their binaries and sess the impact of what has been emerging as a best prac- move the machines out of the sinkhole. This happens tice: national anti-botnet initiatives that support large- with some regularity. The recent sinkholing attempt of scale cleanup of end user machines. -
The Botnet Chronicles a Journey to Infamy
The Botnet Chronicles A Journey to Infamy Trend Micro, Incorporated Rik Ferguson Senior Security Advisor A Trend Micro White Paper I November 2010 The Botnet Chronicles A Journey to Infamy CONTENTS A Prelude to Evolution ....................................................................................................................4 The Botnet Saga Begins .................................................................................................................5 The Birth of Organized Crime .........................................................................................................7 The Security War Rages On ........................................................................................................... 8 Lost in the White Noise................................................................................................................. 10 Where Do We Go from Here? .......................................................................................................... 11 References ...................................................................................................................................... 12 2 WHITE PAPER I THE BOTNET CHRONICLES: A JOURNEY TO INFAMY The Botnet Chronicles A Journey to Infamy The botnet time line below shows a rundown of the botnets discussed in this white paper. Clicking each botnet’s name in blue will bring you to the page where it is described in more detail. To go back to the time line below from each page, click the ~ at the end of the section. 3 WHITE -
Generation of an Iot Botnet Dataset in a Medium-Sized Iot Network
MedBIoT: Generation of an IoT Botnet Dataset in a Medium-sized IoT Network Alejandro Guerra-Manzanares a, Jorge Medina-Galindo, Hayretdin Bahsi b and Sven Nomm˜ c Department of Software Science, Tallinn University of Technology, Tallinn, Estonia falejandro.guerra, hayretdin.bahsi, [email protected], [email protected] Keywords: Botnet, Internet of Things, Dataset, Intrusion Detection, Anomaly Detection, IoT. Abstract: The exponential growth of the Internet of Things in conjunction with the traditional lack of security mecha- nisms and resource constraints associated with these devices have posed new risks and challenges to security in networks. IoT devices are compromised and used as amplification platforms by cyber-attackers, such as DDoS attacks. Machine learning-based intrusion detection systems aim to overcome network security lim- itations relying heavily on data quantity and quality. In the case of IoT networks these data are scarce and limited to small-sized networks. This research addresses this issue by providing a labelled behavioral IoT data set, which includes normal and actual botnet malicious network traffic, in a medium-sized IoT network infrastructure (83 IoT devices). Three prominent botnet malware are deployed and data from botnet infec- tion, propagation and communication with C&C stages are collected (Mirai, BashLite and Torii). Binary and multi-class machine learning classification models are run on the acquired data demonstrating the suitability and reliability of the generated data set for machine learning-based botnet detection IDS testing, design and deployment. The generated IoT behavioral data set is released publicly available as MedBIoT data set∗. 1 INTRODUCTION lam, 2017; Bosche et al., 2018; Pratt, 2019). -
Iptrust Botnet / Malware Dictionary This List Shows the Most Common Botnet and Malware Variants Tracked by Iptrust
ipTrust Botnet / Malware Dictionary This list shows the most common botnet and malware variants tracked by ipTrust. This is not intended to be an exhaustive list, since new threat intelligence is always being added into our global Reputation Engine. NAME DESCRIPTION Conficker A/B Conficker A/B is a downloader worm that is used to propagate additional malware. The original malware it was after was rogue AV - but the army's current focus is undefined. At this point it has no other purpose but to spread. Propagation methods include a Microsoft server service vulnerability (MS08-067) - weakly protected network shares - and removable devices like USB keys. Once on a machine, it will attach itself to current processes such as explorer.exe and search for other vulnerable machines across the network. Using a list of passwords and actively searching for legitimate usernames - the ... Mariposa Mariposa was first observed in May 2009 as an emerging botnet. Since then it has infected an ever- growing number of systems; currently, in the millions. Mariposa works by installing itself in a hidden location on the compromised system and injecting code into the critical process ͞ĞdžƉůŽƌĞƌ͘ĞdžĞ͘͟/ƚŝƐknown to affect all modern Windows versions, editing the registry to allow it to automatically start upon login. Additionally, there is a guard that prevents deletion while running, and it automatically restarts upon crash/restart of explorer.exe. In essence, Mariposa opens a backdoor on the compromised computer, which grants full shell access to ... Unknown A botnet is designated 'unknown' when it is first being tracked, or before it is given a publicly- known common name. -
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 ......................................................................................................................................... -
Building an Intrusion Detection System (IDS) to Neutralize Botnet Attacks
International Journal of Computer Applications Technology and Research Volume 4– Issue 2, 103 - 107, 2015, ISSN:- 2319–8656 Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Botnet Attacks R. Kannan A.V.Ramani Department of Computer Science Department of Computer Science Sri Ramakrishna Mission Vidyalaya Sri Ramakrishna Mission Vidyalaya College of Arts and Science College of Arts and Science Coimbatore ,Tamilnadu,India. Coimbatore ,Tamilnadu,India Abstract: Among the various forms of malware attacks such as Denial of service, Sniffer, Buffer overflows are the most dreaded threats to computer networks. These attacks are known as botnet attacks and self-propagating in nature and act as an agent or user interface to control the computers which they attack. In the process of controlling a malware, Bot header(s) use a program to control remote systems through internet with the help of zombie systems. Botnets are collection of compromised computers (Bots) which are remotely controlled by its originator (Bot-Master) under a common Command-and-Control (C&C) structure. A server commands to the bot and botnet and receives the reports from the bot. The bots use Trojan horses and subsequently communicate with a central server using IRC. Botnet employs different techniques like Honeypot, communication protocols (e.g. HTTP and DNS) to intrude in new systems in different stages of their lifecycle. Therefore, identifying the botnets has become very challenging; because the botnets are upgrading their methods periodically for affecting the networks. Here, the focus on addressing the botnet detection problem in an Enterprise Network This research introduces novel Solution to mitigate the malicious activities of Botnet attacks through the Principle of component analysis of each traffic data, measurement and countermeasure selection mechanism called Malware Hunter. -
Identifying and Defining Relationships: Techniques for Improving Student Systemic Thinking
AC 2011-897: IDENTIFYING AND DEFINING RELATIONSHIPS: TECH- NIQUES FOR IMPROVING STUDENT SYSTEMIC THINKING Cecelia M. Wigal, University of Tennessee, Chattanooga Cecelia M. Wigal received her Ph.D. in 1998 from Northwestern University and is presently a Professor of Engineering and Assistant Dean of the College of Engineering and Computer Science at the University of Tennessee at Chattanooga (UTC). Her primary areas of interest and expertise include complex process and system analysis, process improvement analysis, and information system analysis with respect to usability and effectiveness. Dr. Wigal is also interested in engineering education reform to address present and future student and national and international needs. c American Society for Engineering Education, 2011 Identifying and Defining Relationships: Techniques for Improving Student Systemic Thinking Abstract ABET, Inc. is looking for graduating undergraduate engineering students who are systems thinkers. However, genuine systems thinking is contrary to the traditional practice of using linear thinking to help solve design problems often used by students and many practitioners. Linear thinking has a tendency to compartmentalize solution options and minimize recognition of relationships between solutions and their elements. Systems thinking, however, has the ability to define the whole system, including its environment, objectives, and parts (subsystems), both static and dynamic, by their relationships. The work discussed here describes two means of introducing freshman engineering students to thinking systemically or holistically when understanding and defining problems. Specifically, the modeling techniques of Rich Pictures and an instructor generated modified IDEF0 model are discussed. These techniques have roles in many applications. In this case they are discussed in regards to their application to the design process. -
ITU Botnet Mitigation Toolkit Background Information
ITU Botnet Mitigation Toolkit Background Information ICT Applications and Cybersecurity Division Policies and Strategies Department ITU Telecommunication Development Sector January 2008 Acknowledgements Botnets (also called zombie armies or drone armies) are networks of compromised computers infected with viruses or malware to turn them into “zombies” or “robots” – computers that can be controlled without the owners’ knowledge. Criminals can use the collective computing power and connected bandwidth of these externally-controlled networks for malicious purposes and criminal activities, including, inter alia, generation of spam e-mails, launching of Distributed Denial of Service (DDoS) attacks, alteration or destruction of data, and identity theft. The threat from botnets is growing fast. The latest (2007) generation of botnets such as the Storm Worm uses particularly aggressive techniques such as fast-flux networks and striking back with DDoS attacks against security vendors trying to mitigate them. An underground economy has now sprung up around botnets, yielding significant revenues for authors of computer viruses, botnet controllers and criminals who commission this illegal activity by renting botnets. In response to this growing threat, ITU is developing a Botnet Mitigation Toolkit to assist in mitigating the problem of botnets. This document provides background information on the toolkit. The toolkit, developed by Mr. Suresh Ramasubramanian, draws on existing resources, identifies relevant local and international stakeholders, and -
Botnet Threat Tracking | Intelligence Services | Kaspersky
INTELLIGENCE SERVICES: BOTNET THREAT TRACKING Expert monitoring and notification services to identify botnets threatening your reputation and customers Many network attacks are organized using botnets. Such attacks might target casual Internet users, but often these threats are aimed at specific organizations and their online customers. Kaspersky Lab’s expert solution tracks the activity of botnets and provides real-time notifications of threats associated with specific enterprise brands. You can use this information to advise and inform your customers, security services providers and law enforcement about current threats. Protect your organization’s reputation and customers today with Kaspersky Lab’s Botnet Threats Notification Service. TAKE ACTION WITH REAL-TIME DELIVERABLES: The service provides a subscription to personalized email or JSON format notifications containing intelligence about matching brand names by tracking keywords in the botnets monitored by Kaspersky Lab. Notifications include: Targeted URL of the botnet — Bot malware is designed to wait until the user accesses the URL(s) of the targeted organization and then starts the attack rule. Botnet type — Understand exactly what malware threat is being employed by the cybercriminal to affect your customers. Examples include Zeus, SpyEye, and Citadel. Attack type — Identify what the cybercriminals are using the malware to do; for example, web data injection, keylogging, screen wipes or video capture. Attack rules — Know what different rules of web code injection are being used such as HTML requests (GET / POST), data of web page before injection, data of web page after injection. Command and Control (C&C) server address — Enables you to notify the Internet service provider of the offending server for faster dismantling of the threat. -
Top 5 Least Wanted Malware
Halloween Edition WHAT IS MALWARE? Malware is an abbreviated term meaning “malicious software.” This is software that is specically designed to gain access or damage a computer without the knowledge of the owner. TOP 5 LEAST WANTED MALWARE Ransomware Botnet POS Malware RAT Browser-based 5 5 Malware 5 5 5 $ $ $ 4 $ (malware that restricts access 4 ur computer while to yo y a ransom demanding you pa 4 (also known as a zombie army) to get access back) 4 (a malicious software expressly 4 written to steal customer payment data) (Remote Access Trojan) (also known as “Man in the browser”) 2 4 1 3 5 Remote Access Trojan or RAT A malware program that includes a 1 back door for administrative control over the target computer. RATs are usually downloaded invisibly with a user-requested program – such as a game – or sent as an email attachment. Botnet (also known as a zombie army) is a number of Internet computers that, although their owners are unaware of it, have been set up to forward transmissions (including spam or viruses) to other computers on the Internet. Browser-based malware (also known as “Man in the browser”) is a security attack where the perpetrator 3 installs a trojan horse on a victim's computer that's capable of modifying that user's web transactions as they occur in real time. Ransomware A form of computer malware that restricts access to your 4 computer or its information, while demanding you pay a ransom to get access back. $ POS malware $ $ A malicious software $ expressly written to steal customer payment data – especially credit card data – from retail checkout. -
Automated Malware Analysis Report For
ID: 330706 Sample Name: RFQ0270004296- PR0720001831-Grasp Trading Pvt. Ltd.doc Cookbook: defaultwindowsofficecookbook.jbs Time: 14:51:31 Date: 15/12/2020 Version: 31.0.0 Red Diamond Table of Contents Table of Contents 2 Analysis Report RFQ0270004296-PR0720001831-Grasp Trading Pvt. Ltd.doc 4 Overview 4 General Information 4 Detection 4 Signatures 4 Classification 4 Startup 4 Malware Configuration 4 Yara Overview 4 Sigma Overview 4 Signature Overview 4 AV Detection: 5 Mitre Att&ck Matrix 5 Behavior Graph 5 Screenshots 6 Thumbnails 6 Antivirus, Machine Learning and Genetic Malware Detection 7 Initial Sample 7 Dropped Files 7 Unpacked PE Files 7 Domains 7 URLs 7 Domains and IPs 9 Contacted Domains 9 URLs from Memory and Binaries 9 Contacted IPs 12 General Information 12 Simulations 13 Behavior and APIs 13 Joe Sandbox View / Context 13 IPs 14 Domains 14 ASN 14 JA3 Fingerprints 14 Dropped Files 14 Created / dropped Files 14 Static File Info 16 General 16 File Icon 17 Static RTF Info 17 Objects 17 Network Behavior 17 UDP Packets 17 Code Manipulations 18 Statistics 18 System Behavior 18 Analysis Process: WINWORD.EXE PID: 896 Parent PID: 792 18 General 18 File Activities 18 File Created 18 File Deleted 19 Registry Activities 19 Key Created 19 Key Value Created 19 Copyright null 2020 Page 2 of 23 Key Value Modified 21 Disassembly 23 Copyright null 2020 Page 3 of 23 Analysis Report RFQ0270004296-PR0720001831-Grasp …Trading Pvt. Ltd.doc Overview General Information Detection Signatures Classification Sample RFQ0270004296- Name: PR0720001831-Grasp Muullltttiii AAVV SSccaannnneerrr ddeettteecctttiiioonn fffoorrr ssuubbm… Trading Pvt.