Fortinet Threat Landscape Report Q3 2017
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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. -
ERP Applications Under Fire How Cyberattackers Target the Crown Jewels
ERP Applications Under Fire How cyberattackers target the crown jewels July 2018 v1.0 With hundreds of thousands of implementations across the globe, Enterprise Resource Planning (ERP) applications are supporting the most critical business processes for the biggest organizations in the world. This report is the result of joint research performed by Digital Shadows and Onapsis, aimed to provide insights into how the threat landscape has been evolving over time for ERP applications. We have concentrated our efforts on the two most widely-adopted solutions across the large enterprise segment, SAP and Oracle E-Business Suite, focusing on the risks and threats organizations should care about. According to VP Distinguished Analyst, Neil MacDonald “As financially motivated attackers turn their attention ‘up the stack’ to the application layer, business applications such as ERP, CRM and human resources are attractive targets. In many organizations, the ERP application is maintained by a completely separate team and security has not been a high priority. As a result, systems are often left unpatched for years in the name of operational availability.” Gartner, Hype Cycle for Application Security, 2017, July 2017 1 1 Gartner, Hype Cycle for Application Security, 2017, Published: 28 July 2017 ID: G00314199, Analyst(s): Ayal Tirosh, https://www.gartner.com/doc/3772095/hype-cycle-application-security- 02 Executive Summary With hundreds of thousands of implementations across the globe, Enterprise Resource Planning (ERP) applications support the most critical business processes and house the most sensitive information for the biggest organizations in the world. The vast majority of these large organizations have implemented ERP applications from one of the two market leaders, SAP and Oracle. -
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 . -
APT and Cybercriminal Targeting of HCS June 9, 2020 Agenda
APT and Cybercriminal Targeting of HCS June 9, 2020 Agenda • Executive Summary Slides Key: • APT Group Objectives Non-Technical: managerial, strategic • APT Groups Targeting Health Sector and high-level (general audience) • Activity Timeline Technical: Tactical / IOCs; requiring • TTPs in-depth knowledge (sysadmins, IRT) • Malware • Vulnerabilities • Recommendations and Mitigations TLP: WHITE, ID#202006091030 2 Executive Summary • APT groups steal data, disrupt operations, and destroy infrastructure. Unlike most cybercriminals, APT attackers pursue their objectives over longer periods of time. They adapt to cyber defenses and frequently retarget the same victim. • Common HPH targets include: • Healthcare Biotechnology Medical devices • Pharmaceuticals Healthcare information technology • Scientific research • HPH organizations who have been victim of APT attacks have suffered: • Reputational harm Disruption to operations • Financial losses PII/PHI and proprietary data theft • HC3 recommends several mitigations and controls to counter APT threats. TLP: WHITE, ID#202006091030 3 APT Group Objectives • Motivations of APT Groups which target the health sector include: • Competitive advantage • Theft of proprietary data/intellectual capital such as technology, manufacturing processes, partnership agreements, business plans, pricing documents, test results, scientific research, communications, and contact lists to unfairly advance economically. • Intelligence gathering • Groups target individuals and connected associates to further social engineering -
A PRACTICAL METHOD of IDENTIFYING CYBERATTACKS February 2018 INDEX
In Collaboration With A PRACTICAL METHOD OF IDENTIFYING CYBERATTACKS February 2018 INDEX TOPICS EXECUTIVE SUMMARY 4 OVERVIEW 5 THE RESPONSES TO A GROWING THREAT 7 DIFFERENT TYPES OF PERPETRATORS 10 THE SCOURGE OF CYBERCRIME 11 THE EVOLUTION OF CYBERWARFARE 12 CYBERACTIVISM: ACTIVE AS EVER 13 THE ATTRIBUTION PROBLEM 14 TRACKING THE ORIGINS OF CYBERATTACKS 17 CONCLUSION 20 APPENDIX: TIMELINE OF CYBERSECURITY 21 INCIDENTS 2 A Practical Method of Identifying Cyberattacks EXECUTIVE OVERVIEW SUMMARY The frequency and scope of cyberattacks Cyberattacks carried out by a range of entities are continue to grow, and yet despite the seriousness a growing threat to the security of governments of the problem, it remains extremely difficult to and their citizens. There are three main sources differentiate between the various sources of an of attacks; activists, criminals and governments, attack. This paper aims to shed light on the main and - based on the evidence - it is sometimes types of cyberattacks and provides examples hard to differentiate them. Indeed, they may of each. In particular, a high level framework sometimes work together when their interests for investigation is presented, aimed at helping are aligned. The increasing frequency and severity analysts in gaining a better understanding of the of the attacks makes it more important than ever origins of threats, the motive of the attacker, the to understand the source. Knowing who planned technical origin of the attack, the information an attack might make it easier to capture the contained in the coding of the malware and culprits or frame an appropriate response. the attacker’s modus operandi. -
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. -
Symantec Intelligence Report: June 2011
Symantec Intelligence Symantec Intelligence Report: June 2011 Three-quarters of spam send from botnets in June, and three months on, Rustock botnet remains dormant as Cutwail becomes most active; Pharmaceutical spam in decline as new Wiki- pharmacy brand emerges Welcome to the June edition of the Symantec Intelligence report, which for the first time combines the best research and analysis from the Symantec.cloud MessageLabs Intelligence Report and the Symantec State of Spam & Phishing Report. The new integrated report, the Symantec Intelligence Report, provides the latest analysis of cyber security threats, trends and insights from the Symantec Intelligence team concerning malware, spam, and other potentially harmful business risks. The data used to compile the analysis for this combined report includes data from May and June 2011. Report highlights Spam – 72.9% in June (a decrease of 2.9 percentage points since May 2011): page 11 Phishing – One in 330.6 emails identified as phishing (a decrease of 0.05 percentage points since May 2011): page 14 Malware – One in 300.7 emails in June contained malware (a decrease of 0.12 percentage points since May 2011): page 15 Malicious Web sites – 5,415 Web sites blocked per day (an increase of 70.8% since May 2011): page 17 35.1% of all malicious domains blocked were new in June (a decrease of 1.7 percentage points since May 2011): page 17 20.3% of all Web-based malware blocked was new in June (a decrease of 4.3 percentage points since May 2011): page 17 Review of Spam-sending botnets in June 2011: page 3 Clicking to Watch Videos Leads to Pharmacy Spam: page 6 Wiki for Everything, Even for Spam: page 7 Phishers Return for Tax Returns: page 8 Fake Donations Continue to Haunt Japan: page 9 Spam Subject Line Analysis: page 12 Best Practices for Enterprises and Users: page 19 Introduction from the editor Since the shutdown of the Rustock botnet in March1, spam volumes have never quite recovered as the volume of spam in global circulation each day continues to fluctuate, as shown in figure 1, below. -
Mid-Year Report Cyber Attack Trends 2017
MID-YEAR REPORT CYBER ATTACK TRENDS 2017 TABLE OF CONTENTS | 2 TABLE OF CONTENTS Introduction ................................................................................................. 3 Global Trends .............................................................................................. 4 Major Cyber Breaches ................................................................................ 5 Americas ................................................................................................. 5 Europe, the Middle East and Africa (EMEA)............................................ 6 Asia-Pacific (APAC) ................................................................................. 6 Global Malware Statistics ........................................................................... 7 Top Malware Families ............................................................................. 7 Top Ransomware ..................................................................................... 9 Top Banking Malware .............................................................................. 10 Top Mobile Malware ................................................................................ 11 Cyber Attack Categories by Region ............................................................ 12 Global Threat Index Map ............................................................................. 13 Additional Observations .............................................................................. 14 Recommendations ..................................................................................... -
The Dridex Swiss Army Knife: Big Data Dissolves the APT & Crime Grey Area
#RSAC SESSION ID: HT-W10 The Dridex Swiss Army knife: big data dissolves the APT & crime grey area Eward Driehuis Director of product Fox-IT @brakendelama #RSAC Understanding criminal evolution Global visibility Collaboration Investigations Feeds #RSAC May 2014 #RSAC Rewind 9 years… 2006 Slavik launches ZeuS 2009 SpyEye & Carberp compete for market share 2010 Slavik creates ZeuS2 Hands over ZeuS support to the SpyEye guy 2011 ZeuS2 code leaks 2012 Gribodemon & Carberp members arrested In 2009 Slavik had joined JabberZeuS And Evolved to GameOver / P2PZeuS #RSAC The Businessclub Legacy Businesslike Financial guy perfected money laundry Targeted commercial banking Perfected the Hybrid attack / Tokengrabber Perfected ransomware / Cryptolocker Did some “light espionage” #RSAC Business club after Slavik Dyre Businessclub (GameOver ZeuS gang until May 2014) EvilCorp (Dridex crew) #RSAC Dridex: EvilCorp’s Swiss Army knife #RSAC EvilCorp network expands Core businessclub members in EvilCorp & Dridex operators Leveraging existing money laundry networks Branching out: Dridex operators do ransomware, RATs, Credit Cards, high value targets Ties with Anunak / Carbanak #RSAC Dridex Malware Based on Bugat/Cridex/Feodo, since 2014 Speading: scattergun (spam / attachments) Modular architecture P2P, with 3 operating modes: Token Grabber, data mining, inter node comm Using businessclub technology Loader dropping many different malwares #RSAC #RSAC EvilCorp: Dridex Targets 2015 -2017 #RSAC EvilCorp: ”Gucci” accounts Harvesting data from victims Big data techniques -
Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide