The Real Face of KOOBFACE: the Largest Web 2.0 Botnet Explained
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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. -
Fortinet's March Threatscape Report Shows Domination of Ransomware and Troublesome Zero-Day
Fortinet's March Threatscape Report Shows Domination of Ransomware and Troublesome Zero-Day Rise of Ransomware Is Primarily Driven by Bredolab and Pushdo Botnets SUNNYVALE, CA, Apr 01, 2010 (MARKETWIRE via COMTEX News Network) -- Fortinet(R) (NASDAQ: FTNT) -- a leading network security provider and worldwide leader of unified threat management (UTM) solutions -- today announced its March 2010 Threatscape report showed domination of ransomware threats with nine of the detections in the malware top ten list resulting in either scareware or ransomware infesting the victim's PC. Fortinet observed the primary drivers behind these threats to be two of the most notorious botnet "loaders" -- Bredolab and Pushdo. Another important finding is the aggressive entrance of a new zero-day threat in FortiGuard's top ten attack list, MS.IE.Userdata.Behavior.Code.Execution, which accounted for 25 percent of the detected activity last month. Key threat activities for the month of March include: -- SMS-based Ransomware High Activity: A new ransomware threat -- W32/DigiPog.EP -- appeared in Fortinet's top ten malware list. DigiPog is an SMS blocker using Russian language, locking out a system and aggressively killing off popular applications like Internet Explorer and Firefox until an appropriate code is entered into a field provided to the user. To obtain the code, a user must send an SMS message to the provided number, receiving a code in return. Upon execution, DigiPog registers the user's MAC address with its server. It is the first time that SMS-based ransomware enters Fortinet's top ten list, showing that the rise of ransomware is well on its way. -
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 . -
PC Anti-Virus Protection 2011
PC Anti-Virus Protection 2011 12 POPULAR ANTI-VIRUS PROGRAMS COMPARED FOR EFFECTIVENESS Dennis Technology Labs, 03/08/2010 www.DennisTechnologyLabs.com This test aims to compare the effectiveness of the most recent releases of popular anti-virus software1. The products include those from Kaspersky, McAfee, Microsoft, Norton (Symantec) and Trend Micro, as well as free versions from Avast, AVG and Avira. Other products include those from BitDefender, ESET, G-Data and K7. The tests were conducted between 07/07/2010 and 22/07/2010 using the most up to date versions of the software available. A total of 12 products were exposed to genuine internet threats that real customers could have encountered during the test period. Crucially, this exposure was carried out in a realistic way, reflecting a customer’s experience as closely as possible. For example, each test system visited real, infected websites that significant numbers of internet users were encountering at the time of the test. These results reflect what would have happened if those users were using one of the seven products tested. EXECUTIVE SUMMARY Q Products that block attacks early tended to protect the system more fully The nature of web-based attacks means that the longer malware has access to a system, the more chances it has of downloading and installing further threats. Products that blocked the malicious and infected websites from the start reduced the risk of compromise by secondary and further downloads. Q 100 per cent protection is rare This test recorded an average protection rate of 87.5 per cent. New threats appear online frequently and it is inevitable that there will be times when specific security products are unable to protect from some of these threats. -
Large-Scale Malware Experiments
LARGE-SCALE MALWARE EXPERIMENTS ... CALVET ET AL. LARGE-SCALE MALWARE • Unlike with in-the-wild experiments [1], there are fewer ethical or legal issues to deal with than when performing EXPERIMENTS: WHY, HOW, AND arbitrary attacks against infected computers. SO WHAT? • Having an in vitro environment provides us with a way to Joan Calvet, Jose M. Fernandez conduct computer security research in a scientifi c way: we École Polytechnique de Montréal, Montréal, Canada can reproduce experiments and test the effect of various independent variables. Email {joan.calvet, jose.fernandez}@polymtl.ca We decided to use the Waledac botnet as a fi rst experiment for the following reasons: Pierre-Marc Bureau ESET, Montréal, Canada • Thanks to prior reverse engineering [2], we had in-depth knowledge of this threat family. Email [email protected] • This malware does not replicate, thus limiting the risk of running an experiment that might get out of control. Jean-Yves Marion LORIA, Nancy, France • There exists a set of vulnerabilities in Waledac’s peer-to- peer protocol that were worth investigating. We wanted to Email [email protected] evaluate the impact of a mitigation scheme against the botnet. ABSTRACT 1.1 The Waledac case study One of the most popular research areas in the anti-malware The architecture of the Waledac botnet is split into four layers. industry (second only to detection) is to document malware The fi rst layer contains infected hosts with private IP addresses characteristics and understand their operations. Most initiatives that are referred to as spammers. They are essentially the are based on reverse engineering of malicious binaries so as to ‘worker’ bots and constitute approximately 80% of the botnet. -
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 -
MODELING the PROPAGATION of WORMS in NETWORKS: a SURVEY 943 in Section 2, Which Set the Stage for Later Sections
942 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 16, NO. 2, SECOND QUARTER 2014 Modeling the Propagation of Worms in Networks: ASurvey Yini Wang, Sheng Wen, Yang Xiang, Senior Member, IEEE, and Wanlei Zhou, Senior Member, IEEE, Abstract—There are the two common means for propagating attacks account for 1/4 of the total threats in 2009 and nearly worms: scanning vulnerable computers in the network and 1/5 of the total threats in 2010. In order to prevent worms from spreading through topological neighbors. Modeling the propa- spreading into a large scale, researchers focus on modeling gation of worms can help us understand how worms spread and devise effective defense strategies. However, most previous their propagation and then, on the basis of it, investigate the researches either focus on their proposed work or pay attention optimized countermeasures. Similar to the research of some to exploring detection and defense system. Few of them gives a nature disasters, like earthquake and tsunami, the modeling comprehensive analysis in modeling the propagation of worms can help us understand and characterize the key properties of which is helpful for developing defense mechanism against their spreading. In this field, it is mandatory to guarantee the worms’ spreading. This paper presents a survey and comparison of worms’ propagation models according to two different spread- accuracy of the modeling before the derived countermeasures ing methods of worms. We first identify worms characteristics can be considered credible. In recent years, although a variety through their spreading behavior, and then classify various of models and algorithms have been proposed for modeling target discover techniques employed by them. -
An Introduction to Malware
Downloaded from orbit.dtu.dk on: Sep 24, 2021 An Introduction to Malware Sharp, Robin Publication date: 2017 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Sharp, R. (2017). An Introduction to Malware. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. An Introduction to Malware Robin Sharp DTU Compute Spring 2017 Abstract These notes, written for use in DTU course 02233 on Network Security, give a short introduction to the topic of malware. The most important types of malware are described, together with their basic principles of operation and dissemination, and defenses against malware are discussed. Contents 1 Some Definitions............................2 2 Classification of Malware........................2 3 Vira..................................3 4 Worms................................ -
Security Chapter
Barbarians at the Gateway (and just about everywhere else): A Brief Managerial Introduction to Information Security Issues1 a gallaugher.com case provided free to faculty & students for non-commercial use © Copyright 1997-2009, John M. Gallaugher, Ph.D. – for more info see: http://www.gallaugher.com/chapters.html Draft version last modified: Dec. 7 , 2009 – comments welcome [email protected] Note: this is an earlier version of the chapter. All chapters updated Dec. 2009 are now hosted (and still free) at http://www.flatworldknowledge.com. For details see the ‘Courseware’ section of http://gallaugher.com INTRODUCTION LEARNING OBJECTIVES: After studying this section you should be able to: 1. Recognize that information security breaches are on the rise. 2. Understand the potentially damaging impact of security breaches. 3. Recognize that information security must be made a top organizational priority. Sitting in the parking lot of a Minneapolis Marshalls, a hacker armed with a laptop and a telescope‐shaped antenna infiltrated the store’s network via an insecure Wi‐Fi base station. The attack launched what would become a billion‐dollar plus nightmare scenario for TJX, the parent of retail chains that include Marshalls, Home Goods, and T.J. Maxx. Over a period of several months, the hacker and his gang stole at least 45.7 million credit and debit card numbers, and pilfered driver’s license and other private information from an additional 450,000 customers2. TJX, at the time a $17.5 billion, Fortune 500 firm, was left reeling from the incident. The attack deeply damaged the firm’s reputation. -
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.