Global Threat Report 2007
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A the Hacker
A The Hacker Madame Curie once said “En science, nous devons nous int´eresser aux choses, non aux personnes [In science, we should be interested in things, not in people].” Things, however, have since changed, and today we have to be interested not just in the facts of computer security and crime, but in the people who perpetrate these acts. Hence this discussion of hackers. Over the centuries, the term “hacker” has referred to various activities. We are familiar with usages such as “a carpenter hacking wood with an ax” and “a butcher hacking meat with a cleaver,” but it seems that the modern, computer-related form of this term originated in the many pranks and practi- cal jokes perpetrated by students at MIT in the 1960s. As an example of the many meanings assigned to this term, see [Schneier 04] which, among much other information, explains why Galileo was a hacker but Aristotle wasn’t. A hack is a person lacking talent or ability, as in a “hack writer.” Hack as a verb is used in contexts such as “hack the media,” “hack your brain,” and “hack your reputation.” Recently, it has also come to mean either a kludge, or the opposite of a kludge, as in a clever or elegant solution to a difficult problem. A hack also means a simple but often inelegant solution or technique. The following tentative definitions are quoted from the jargon file ([jargon 04], edited by Eric S. Raymond): 1. A person who enjoys exploring the details of programmable systems and how to stretch their capabilities, as opposed to most users, who prefer to learn only the minimum necessary. -
Botection: Bot Detection by Building Markov Chain Models of Bots Network Behavior Bushra A
BOTection: Bot Detection by Building Markov Chain Models of Bots Network Behavior Bushra A. Alahmadi Enrico Mariconti Riccardo Spolaor University of Oxford, UK University College London, UK University of Oxford, UK [email protected] [email protected] [email protected] Gianluca Stringhini Ivan Martinovic Boston University, USA University of Oxford, UK [email protected] [email protected] ABSTRACT through DDoS (e.g. DDoS on Estonia [22]), email spam (e.g. Geodo), Botnets continue to be a threat to organizations, thus various ma- ClickFraud (e.g. ClickBot), and spreading malware (e.g. Zeus). 10,263 chine learning-based botnet detectors have been proposed. How- malware botnet controllers (C&C) were blocked by Spamhaus Mal- ever, the capability of such systems in detecting new or unseen ware Labs in 2018 alone, an 8% increase from the number of botnet 1 botnets is crucial to ensure its robustness against the rapid evo- C&Cs seen in 2017. Cybercriminals are actively monetizing bot- lution of botnets. Moreover, it prolongs the effectiveness of the nets to launch attacks, which are evolving significantly and require system in detecting bots, avoiding frequent and time-consuming more effective detection mechanisms capable of detecting those classifier re-training. We present BOTection, a privacy-preserving which are new or unseen. bot detection system that models the bot network flow behavior Botnets rely heavily on network communications to infect new as a Markov Chain. The Markov Chains state transitions capture victims (propagation), to communicate with the C&C server, or the bots’ network behavior using high-level flow features as states, to perform their operational task (e.g. -
Symantec Report on Rogue Security Software July 08 – June 09
REPORT: SYMANTEC ENTERPRISE SECURITY SYMANTEC REPORT: Symantec Report on Rogue Security Software July 08 – June 09 Published October 2009 Confidence in a connected world. White Paper: Symantec Enterprise Security Symantec Report on Rogue Security Software July 08 – June 09 Contents Introduction . 1 Overview of Rogue Security Software. 2 Risks . 4 Advertising methods . 7 Installation techniques . 9 Legal actions and noteworthy scam convictions . 14 Prevalence of Rogue Security Software . 17 Top reported rogue security software. 17 Additional noteworthy rogue security software samples . 25 Top rogue security software by region . 28 Top rogue security software installation methods . 29 Top rogue security software advertising methods . 30 Analysis of Rogue Security Software Distribution . 32 Analysis of Rogue Security Software Servers . 36 Appendix A: Protection and Mitigation. 45 Appendix B: Methodologies. 48 Credits . 50 Symantec Report on Rogue Security Software July 08 – June 09 Introduction The Symantec Report on Rogue Security Software is an in-depth analysis of rogue security software programs. This includes an overview of how these programs work and how they affect users, including their risk implications, various distribution methods, and innovative attack vectors. It includes a brief discussion of some of the more noteworthy scams, as well as an analysis of the prevalence of rogue security software globally. It also includes a discussion on a number of servers that Symantec observed hosting these misleading applications. Except where otherwise noted, the period of observation for this report was from July 1, 2008, to June 30, 2009. Symantec has established some of the most comprehensive sources of Internet threat data in the world through the Symantec™ Global Intelligence Network. -
2015 Threat Report Provides a Comprehensive Overview of the Cyber Threat Landscape Facing Both Companies and Individuals
THREAT REPORT 2015 AT A GLANCE 2015 HIGHLIGHTS A few of the major events in 2015 concerning security issues. 08 07/15: Hacking Team 07/15: Bugs prompt 02/15: Europol joint breached, data Ford, Range Rover, 08/15: Google patches op takes down Ramnit released online Prius, Chrysler recalls Android Stagefright botnet flaw 09/15: XcodeGhost 07/15: Android 07/15: FBI Darkode tainted apps prompts Stagefright flaw 08/15: Amazon, ENFORCEMENT bazaar shutdown ATTACKS AppStore cleanup VULNERABILITY reported SECURITYPRODUCT Chrome drop Flash ads TOP MALWARE BREACHING THE MEET THE DUKES FAMILIES WALLED GARDEN The Dukes are a well- 12 18 resourced, highly 20 Njw0rm was the most In late 2015, the Apple App prominent new malware family in 2015. Store saw a string of incidents where dedicated and organized developers had used compromised tools cyberespionage group believed to be to unwittingly create apps with malicious working for the Russian Federation since behavior. The apps were able to bypass at least 2008 to collect intelligence in Njw0rm Apple’s review procedures to gain entry support of foreign and security policy decision-making. Angler into the store, and from there into an ordinary user’s iOS device. Gamarue THE CHAIN OF THE CHAIN OF Dorkbot COMPROMISE COMPROMISE: 23 The Stages 28 The Chain of Compromise Nuclear is a user-centric model that illustrates Kilim how cyber attacks combine different Ippedo techniques and resources to compromise Dridex devices and networks. It is defined by 4 main phases: Inception, Intrusion, WormLink Infection, and Invasion. INCEPTION Redirectors wreak havoc on US, Europe (p.28) INTRUSION AnglerEK dominates Flash (p.29) INFECTION The rise of rypto-ransomware (p.31) THREATS BY REGION Europe was particularly affected by the Angler exploit kit. -
Power-Law Properties in Indonesia Internet Traffic. Why Do We Care About It
by Bisyron Wahyudi Muhammad Salahuddien Amount of malicious traffic circulating on the Internet is increasing significantly. Increasing complexity and rapid change in hosts and networks technology suggests that there will be new vulnerabilities. Attackers have interest in identifying networks and hosts to expose vulnerabilities : . Network scans . Worms . Trojans . Botnet Complicated methods of attacks make difficult to identify the real attacks : It is not simple as filtering out the traffic from some sources Security is implemented like an “add on” module for the Internet. Understanding nature behavior of malicious sources and targeted ports is important to minimize the damage by build strong specific security rules and counter measures Help the cyber security policy-making process, and to raise public awareness Questions : . Do malicious sources generate the attacks uniformly ? . Is there any pattern specific i.e. recurrence event ? . Is there any correlation between the number of some attacks over specific time ? Many systems and phenomena (events) are distributed according to a “power law” When one quantity (say y) depends on another (say x) raised to some power, we say that y is described by a power law A power law applies to a system when: . large is rare and . small is common Collection of System logs from Networked Intrusion Detection System (IDS) The NIDS contains 11 sensors installed in different core networks in Indonesian ISP (NAP) Period : January, 2012 - September, 2012 . Available fields : ▪ Event Message, Timestamp, Dest. IP, Source IP, Attacks Classification, Priority, Protocol, Dest. Port/ICMP code, Source Port/ICMP type, Sensors ID Two quantities x and y are related by a power law if y is proportional to x(-c) for a constant c y = .x(-c) If x and y are related by a power law, then the graph of log(y) versus log(x) is a straight line log(y) = -c.log(x) + log() The slope of the log-log plot is the power exponent c Destination Port Distribution . -
1.Computer Virus Reported (1) Summary for This Quarter
Attachment 1 1.Computer Virus Reported (1) Summary for this Quarter The number of the cases reported for viruses*1 in the first quarter of 2013 decreased from that of the fourth quarter of 2012 (See Figure 1-1). As for the number of the viruses detected*2 in the first quarter of 2013, W32/Mydoom accounted for three-fourths of the total (See Figure 1-2). Compared to the fourth quarter of 2012, however, both W32/Mydoom and W32/Netsky showed a decreasing trend. When we looked into the cases reported for W32/Netsky, we found that in most of those cases, the virus code had been corrupted, for which the virus was unable to carry out its infection activity. So, it is unlikely that the number of cases involving this virus will increase significantly in the future As for W32/IRCbot, it has greatly decreased from the level of the fourth quarter of 2012. W32/IRCbot carries out infection activities by exploiting vulnerabilities within Windows or programs, and is often used as a foothold for carrying out "Targeted Attack". It is likely that that there has been a shift to attacks not using this virus. XM/Mailcab is a mass-mailing type virus that exploits mailer's address book and distributes copies of itself. By carelessly opening this type of email attachment, the user's computer is infected and if the number of such users increases, so will the number of the cases reported. As for the number of the malicious programs detected in the first quarter of 2013, Bancos, which steals IDs/Passwords for Internet banking, Backdoor, which sets up a back door on the target PC, and Webkit, which guides Internet users to a maliciously-crafted Website to infect with another virus, were detected in large numbers. -
Detecting Botnets Using File System Indicators
Detecting botnets using file system indicators Master's thesis University of Twente Author: Committee members: Peter Wagenaar Prof. Dr. Pieter H. Hartel Dr. Damiano Bolzoni Frank Bernaards LLM (NHTCU) December 12, 2012 Abstract Botnets, large groups of networked zombie computers under centralised control, are recognised as one of the major threats on the internet. There is a lot of research towards ways of detecting botnets, in particular towards detecting Command and Control servers. Most of the research is focused on trying to detect the commands that these servers send to the bots over the network. For this research, we have looked at botnets from a botmaster's perspective. First, we characterise several botnet enhancing techniques using three aspects: resilience, stealth and churn. We see that these enhancements are usually employed in the network communications between the C&C and the bots. This leads us to our second contribution: we propose a new botnet detection method based on the way C&C's are present on the file system. We define a set of file system based indicators and use them to search for C&C's in images of hard disks. We investigate how the aspects resilience, stealth and churn apply to each of the indicators and discuss countermeasures botmasters could take to evade detection. We validate our method by applying it to a test dataset of 94 disk images, 16 of which contain C&C installations, and show that low false positive and false negative ratio's can be achieved. Approaching the botnet detection problem from this angle is novel, which provides a basis for further research. -
Transition Analysis of Cyber Attacks Based on Long-Term Observation—
2-3 nicterReport —TransitionAnalysisofCyberAttacksBasedon Long-termObservation— NAKAZATO Junji and OHTAKA Kazuhiro In this report, we provide a statistical data concerning cyber attacks and malwares based on a long-term network monitoring on the nicter. Especially, we show a continuous observation report of Conficker, which is a pandemic malware since November 2008. In addition, we report a transition analysis of the scale of botnet activities. Keywords Incident analysis, Darknet, Network monitoring, Malware analysis 1 Introduction leverages the traffic as detected by the four black hole sensors placed on different network We have been monitoring the IP address environments as shown by Fig. 1. space that is reachable and unused on the ● Sensor I : Structure where live nets and Internet (i.e. darknets) on a large-scale to darknets coexist in a class B understand the overall impact inflicted by network infectious activities including malware. This ● Sensor II : Structure where only darknets report analyzes the darknet traffic that has exist in a class B network been monitored and accumulated over six ● Sensor III : Structure where a /24 subnet years by an incident analysis center named in a class B network is a dark- *1 the nicter[1][2] to provide changing trends of net cyber attacks and fluctuation of attacker host ● Sensor IV : Structure where live nets and activities as obtained by long-term monitor- darknets coexist in a class B ing. In particular, we focus on Conficker, a network worm that has triggered large-scale infections The traffic obtained by these four sensors since November 2008, and report its impact on is analyzed by different analysis engines[3][4] the Internet and its current activities. -
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. -
System Center Endpoint Protection for Mac
System Center Endpoint Protection for Mac Installation Manual and User Guide Contents Context menu 19 System Center Endpoint Protection 3 System requirements 3 Advanced user 20 Import and export settings 20 Installation 4 Import settings 20 Typical installation 4 Export settings 20 Proxy server setup 20 Custom installation 4 Removable media blocking 20 Uninstallation 5 21 Beginners guide 6 Glossary Types of infiltrations 21 User interface 6 Viruses 21 Checking operation of the system 6 Worms 21 What to do if the program does not work properly 7 Trojan horses 21 Work with System Center Endpoint Adware 22 Spyware 22 Protection 8 Potentially unsafe applications 22 Antivirus and antispyware protection 8 Potentially unwanted applications 22 Real-time file system protection 8 Real-time Protection setup 8 Scan on (Event triggered scanning) 8 Advanced scan options 8 Exclusions from scanning 8 When to modify Real-time protection configuration 9 Checking Real-time protection 9 What to do if Real-time protection does not work 9 On-demand computer scan 10 Type of scan 10 Smart scan 10 Custom scan 11 Scan targets 11 Scan profiles 11 Engine parameters setup 12 Objects 12 Options 12 Cleaning 13 Extensions 13 Limits 13 Others 13 An infiltration is detected 14 Updating the program 14 Update setup 15 How to create update tasks 15 Upgrading to a new build 15 Scheduler 16 Purpose of scheduling tasks 16 Creating new tasks 16 Creating user-defined task 17 Quarantine 17 Quarantining files 17 Restoring from Quarantine 17 Log files 18 Log maintenance 18 Log filtering 18 User interface 18 Alerts and notifications 19 Alerts and notifications advanced setup 19 Privileges 19 System Center Endpoint Protection As the popularity of Unix-based operating systems increases, malware authors are developing more threats to target Mac users. -
Effective Malicious Features Extraction and Classification for Incident Handling Systems
EFFECTIVE MALICIOUS FEATURES EXTRACTION AND CLASSIFICATION FOR INCIDENT HANDLING SYSTEMS CHO CHO SAN UNIVERSITY OF COMPUTER STUDIES, YANGON OCTOBER, 2019 Effective Malicious Features Extraction and Classification for Incident Handling Systems Cho Cho San University of Computer Studies, Yangon A thesis submitted to the University of Computer Studies, Yangon in partial fulfillment of the requirements for the degree of Doctor of Philosophy October, 2019 Statement of Originality I hereby certify that the work embodied in this thesis is the result of original research and has not been submitted for a higher degree to any other University or Institution. …..…………………………… .…………........………………………… Date Cho Cho San ACKNOWLEDGEMENTS First of all, I would like to thank Hist Excellency, the Minister for the Ministry of Education, for providing full facilities support during the Ph.D. course at the University of Computer Studies, Yangon. Secondly, my profound gratitude goes to Dr. Mie Mie Thet Thwin, Rector of the University of Computer Studies, Yangon, for allowing me to develop this research and giving me general guidance during the period of my study. I would like to express my greatest pleasure and the deepest appreciation to my supervisor, Dr. Mie Mie Su Thwin, Professor, the University of Computer Studies, Yangon, for her excellent guidance, caring, patient supervision, and providing me with excellent ideas throughout the study of this thesis. I would also like to extend my special appreciation to Dr. Khine Moe Nwe, Professor and Course-coordinator of the Ph.D. 9th Batch, the University of Computer Studies, Yangon, for her useful comments, advice, and insight which are invaluable through the process of researching and writing this dissertation. -
Chapter 3: Viruses, Worms, and Blended Threats
Chapter 3 Chapter 3: Viruses, Worms, and Blended Threats.........................................................................46 Evolution of Viruses and Countermeasures...................................................................................46 The Early Days of Viruses.................................................................................................47 Beyond Annoyance: The Proliferation of Destructive Viruses .........................................48 Wiping Out Hard Drives—CIH Virus ...................................................................48 Virus Programming for the Masses 1: Macro Viruses...........................................48 Virus Programming for the Masses 2: Virus Generators.......................................50 Evolving Threats, Evolving Countermeasures ..................................................................51 Detecting Viruses...................................................................................................51 Radical Evolution—Polymorphic and Metamorphic Viruses ...............................53 Detecting Complex Viruses ...................................................................................55 State of Virus Detection.........................................................................................55 Trends in Virus Evolution..................................................................................................56 Worms and Vulnerabilities ............................................................................................................57