Big Data for CyberSecurity
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A key concern in today's Internet is the threat of cybercrime. Cybercrimes on the Web use different types of malware and fraud for various purposes such as financial theft, espionage, copyright infringement, denial of service and cyber-warfare. They spread using different protocols such as HTTP or HTTPS, links in email or IM, IRC, malware attachments, and phishing attacks. This cyber threat landscape, often controlled by organized crime and nation states, has been evolving rapidly and is becoming more evasive and difficult to detect. They often make use of multiple infection mechanisms to take control of machines and make them part of botnets, which can then be utilized to perpetrate other kinds of attacks such as data leakage and denial of service attacks. As threats blend across diverse data channels, their detection requires scalable distributed monitoring and cross-correlation with a substantial amount of contextual information. Conventional methods of protecting against cyber attacks such as signature-based detection and firewalls have become less effective. Many corporations, security companies and governments, thus, are beginning to employ more and more sophisticated means of detecting and protecting against cyber attacks. Recently, data-driven approaches have become popular for detecting new kinds of attacks. Instead of relying on static signature-based detection, these techniques seek to detect anomalies and other patterns from various kinds of data such as network traffic statistics and server and application logs. For example, a sudden increase in the number of unresolvable DNS requests from a laptop might indicate that it is infected by a bot. These approaches rely on very large volumes of data and a variety of analytics to analyze the data. In this talk, I will describe some Big-Data based analytics and systems that IBM has built for detecting different kinds of cyber-attacks, particularly for detecting new kinds or new sources of cyber-attacks that may have not been seen before. These analytics span both real-time processing on the IBM InfoSphere Streams platform as well as off-line processing using InfoSphere Big Insights and data mining tools like SPSS.
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Big Data for CyberSecurity
- 1. © 2013 IBM CorporationMay 14, 2013 Big Data for CyberSecurity Anand Ranganathan Research Staff Member, TJ Watson Research Center <arangana@us.ibm.com>
- 2. Agenda Cyber Threats IBM Big Data Suite Big Data Analytics for CyberSecurity – Monitor Network Behaviors to detect known and unknown cyber-threats in Enterprises – Detect Denial of Service Attacks in large ISPs – Detect Data-Leakage from organizations 2IB
- 3. Cyber-Threats Are Becoming More Sophisticated 3
- 4. 2011: Year of the Targeted Attack Source: IBM X-Force® Research 2011 Trend and Risk Report JK2012-04-26 Marketing Services Online Gaming Online Gaming Online Gaming Online Gaming Central Government Gaming Gaming Internet Services Online Gaming Online Gaming Online Services Online Gaming IT Security Banking IT Security Government Consulting IT Security Tele- communic ations Enter- tainment Consumer Electronics Agriculture Apparel Insurance Consulting Consumer Electronics Internet Services Central Govt Central Govt Central Govt Attack Type SQL Injection URL Tampering Spear Phishing 3rd Party Software DDoS SecureID Trojan Software Unknown Size of circle estimates relative impact of breach in terms of cost to business Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Entertainment Defense Defense Defense Consumer Electronics Central Government Central Government Central Government Central Government Central Government Central Government Central Government Consumer Electronics National Police National Police State Police State Police Police Gaming Financial Market Online Services Consulting Defense Heavy Industry Entertainment Banking 2011 Sampling of Security Incidents by Attack Type, Time and Impact conjecture of relative breach impact is based on publicly disclosed information regarding leaked records and financial losses
- 5. 2012: The explosion of breaches continues! Source: IBM X-Force® Research 2012 Trend and Risk Report 2012 Sampling of Security Incidents by Attack Type, Time and Impact Conjecture of relative breach impact is based on publicly disclosed information regarding leaked records and financial losses
- 6. A Denial of Service attack that prevents or impairs the use of networks, systems, or applications by exhausting resources Malware infection - A virus, worm, Trojan horse, or other code-based malicious entity that successfully infects a host A targeted, advanced attack – also known as an advanced persistent threat (APT) - which is designed to be undetectable. Loss or theft of technology (laptops, memory sticks, PDAs) which contain sensitive data; Inadvertent disclosure of data Defacement - A person gains logical or physical access without permission and defaces a Web application Common Cyber Security Risks and Potential Impacts Loss of Customers Impact to Brand Sensitive Data Disclosure Stolen Intellectual Property Loss of Data & Productivity Personal and National Security Common Security Risks Potential Impacts Loss of Data or Productivity
- 7. Botnets Botnet = A network of compromised computers controlled by the botmaster, ranging in size from hundreds to millions of hosts Purpose: denial of service attacks, spam delivery, stealing credentials and data, compromising control systems, etc. Hosts infected by downloads from malicious websites, emailed executables, web, memory stick, PDF, … Bots receive updates and commands from the Command and Control node and communications are becoming more sophisticated 7
- 8. Botnet Communication There is need to talk: Bots receive updates and commands from the C&C node Utilize a command and control structure, through IRC, HTML, SSL, Twitter, IM or custom built solutions. Botnet communications are becoming more sophisticated and harder to track – peer-to-peer, distributed vs. hierarchical control structure – fast fluxing, name generation 8 C&C P2P
- 9. A Typical Threat Example 9 2 Malicious Web server sends or reflects exploit code <click> 1 Install Malware Mail-Client 5 Victim Domain Name Server Spammer Command & Control 4 web-page + 3 Follow link Execute (Spam..) 9 C&C / U pdater IP Address Lookup C &C / U pdater D N 6 Remotely Control Malware Contact Updater By IP Address (C&C)7 8
- 10. A Typical Threat Example 10 2 Malicious Web server sends or reflects exploit code <click> 1 Install Malware Mail-Client 5 Victim Domain Name Server Spammer Command & Control 4 web-page + 3 Follow link Execute (Spam..) 9 C&C / U pdater IP Address Lookup C &C / U pdater D N 6 Remotely Control Malware Contact Updater By IP Address (C&C)7 8 d) Monitor Web Traffic a) Monitor DNS c) Monitor Port & Protocol Usage b) Monitor NetFlowb) Monitor NetFlow
- 11. Typical Solution Architecture 11 01/11/10 DNS NetFlow ….. X86 Box X86 Blade Cell Blade X86 Blade FPGA Blade Operating System TransportSystem S Data Fabric Unsupervised Real-Time AnalyticsUnsupervised Real-Time Analytics Supervised LearningSupervised Learning Dashboarding / Visualization 1 3 2 Real-time Results (Tickets, Monitoring) Collect Results + Evidence Trends, History 4 Adapted Analytics Models • Cybersecurity Analytics • Real-Time processing of massive data streams • Advanced Data Mining, and Trend analytics • New and Incremental model learning PureData System for Analytics, BigInsights
- 12. IBM Confidential © 2012 IBM Corporation12 Smarter Communications BI / Reporting BI / Reporting Exploration / Visualization Functional App Industry App Predictive Analytics Content Analytics Analytic Applications IBM Big Data Platform Systems Management Application Development Visualization & Discovery Accelerators Information Integration & Governance Hadoop System Stream Computing Data Warehouse IBM Big Data Suite
- 13. IBM Confidential © 2012 IBM Corporation13 IBM InfoSphere Streams Millions of events per second Microse cond Latency Traditional / Non- traditional data sources Real time delivery Powerful Analytics Algo Trading Telco churn predict Smart Grid Cyber Security Government / Law enforcement ICU Monitoring Environment Monitoring A Platform for Real Time Analytics on BIG Data Volume Terabytes per second Petabytes per day Variety All kinds of data All kinds of analytics Velocity Insights in microseconds Agility Dynamically responsive Rapid application development
- 14. IBM Confidential © 2012 IBM Corporation14 continuous ingestion continuous analysis How Streams Works achieve scale by partitioning applications into components
- 15. IBM Confidential © 2012 IBM Corporation15 continuous ingestion continuous analysis achieve scale by partitioning applications into components by distributing across stream-connected hardware nodes How Streams Works infrastructure provides services for scheduling analytics across h/w nodes establishing streaming connectivity … TransformTransform FilterFilter ClassifyClassify CorrelateCorrelate AnnotateAnnotate where appropriate, elements can be “fused” together for lower communication latencies
- 16. Security Appliances (Firewalls, IDS, IPS, SIEMs) vs Big Data IBM Big Data PlatformIBM QRadar Security Intelligence Platform Security use cases Turnkey Custom User Interface All-in-one console Purpose-built applications Data Sources 450+ preconfigured (and growing) Everything else Data Volume 100+ Terabyte range Peta-byte range Real-time Analysis Seconds Milliseconds Analytics Pre-built, primarily rule-based Custom, learning Required Expertise Average - Security practitioners Skilled – Data scientists and analysts InfoSphere BigInsights, Streams and PureData for Analytics
- 17. Organizations have a growing need to identify and protect against threats by building insights from broader and larger data sets
- 18. A Typical Threat Example 20 2 Malicious Web server sends or reflects exploit code <click> 1 Install Malware Mail-Client 5 Victim Domain Name Server Spammer Command & Control 4 web-page + 3 Follow link Execute (Spam..) 9 C&C / U pdater IP Address Lookup C &C / U pdater D N 6 Remotely Control Malware Contact Updater By IP Address (C&C)7 8 d) Monitor Web Traffic a) Monitor DNS c) Monitor Port & Protocol Usage b) Monitor NetFlowb) Monitor NetFlow
- 19. Traditional Security Analytics 21 Monitored Network Monitored Network The Rest Of The World DNSDNSDNS DHCPDHCP Firewall IDS/ IPS Inline Conventional Setup Detect Signatures within Individual Data Streams
- 20. Streaming Analytics 22 Monitored Network Monitored Network The Rest Of The World (Internet) DNSDNSDNS DHCPDHCP Firewall IDS/ IPS Inline Real-Time Streaming Analytics Setup Detect Signatures within Individual Data Streams Real-Time Cyber Security Analytics Detects behaviors by correlating across diverse & massive data streams via Analytics in Motion Models learnt offline with Analytics on Data at Rest IDS/IPS Alerts…
- 21. Streaming Analytics for Fast-flux Botnets 23 DNS Response Records Suspected Fast-flux Domain Names JoinJoin DNS Queries (with internal querying host IP Addresses) FastFlux Analytics FastFlux Analytics FastFlux Analytics FastFlux Analytics FastFlux Analytics FastFlux Analytics Candidate Names/IP's with Confidence Values AggregatorAggregator Suspected Fast-Flux IP-addresses JoinJoin DHCP Traffic (IP MAC System/Owner) Fast-fluxing Bot alerts JoinJoin Host LogsHost Logs IPS AlertsIPS Alerts … Netflow
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- 23. Use Case 2 - Detect Distributed Denial of Service Attacks in ISPs DDOS attacks often launched by botnets to flood a target server Often use techniques to amplify the flooding – E.g. DNS Amplification Attacks Very hard to detect and prevent in time – Need to monitor 100s of Gbps – Need to monitor millions of DNS requests per second Use InfoSphere Streams for running analytics for detecting DDOS attacks – Look for anomalies in DNS server requests – Scale to internet level traffic rates © 2013 IBM Corporation25
- 24. Use Case 3 - Detect Data-Leakage from organizations Determine what information employees (or bots) are sending out of the company – Look at the all information flowing out of the company to the outside world – Determine if it contains any confidential or sensitive information Monitor what information employees (or bots) are seeing/accessing – Determine if they are accessing sensitive information (even if they may have the rights to access it) – Determine if their access patterns are suddenly changing • E.g. an employee that is suddenly accessing much more information than he (or someone else in his role) typically accesses may want to sell this information outside or leave the company © 2013 IBM Corporation26
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- 26. DNS Amplification Attack Key characteristics: 1) Targeted attack victimizing hosts & servers 2) DNS service provider becomes a participant and unavailable during attack 3) Attack attribution is hard 28 To delete
Notas del editor
- This slide shows you sort of a timeline of events during the first half of 2011. A bunch of different attacks against major organizations, many of whom we feel are probably pretty operationally competent. These are not surprising that some of these organizations were breached. Also, we sort of relate the attack vector as best we understand it based on what ’s been publicly disclosed. And we also - we sort of have a conjecture about the impact of the breach from a financial standpoint, and that’s a rough estimate based on what’s been publicly disclosed. So those numbers are certainly not to be bet on or anything. But it’s as good as we can do based on what we know.
- Open Security Foundation reported 40% increase in breach events for 2012 that cover loss, theft, and exposure of personally identifiable information
- There is need to talk: Bots receive updates and commands from the C&C node Utilize a command and control structure, through IRC, HTML, SSL, Twitter, IM or custom built solutions. Botnet communications are becoming more sophisticated and harder to track peer-to-peer, distributed vs. hierarchical control structure fast fluxing, name generation
- Key Points - Integrate v3 – the point is to have one platform to manage all of the data – there’s no point in having separate silos of data, each creating separate silos of insight. From the customer POV (a solution POV) big data has to be bigger than just one technology Analyze v3 – very important point – we see big data as a viable place to analyze and store data. New technology is not just a pre-processor to get data into a structured DW for analysis. Significant area of value add by IBM – and the game has changed – unlike DBs/SQL, the market is asking who gets the better answer and therefore sophistication and accuracy of the analytics matters Visualization – need to bring big data to the users – spreadsheet metaphor is the key to doing son Development – need sophisticated development tools for the engines and across them to enable the market to develop analytic applications Workload optimization – improvements upon open source for efficient processing and storage Security and Governance – many are rushing into big data like the wild west. But there is sensitive data that needs to be protected, retention policies need to be determined – all of the maturity of governance for the structured world can benefit the big data world
- IBM IOD 2011 05/14/13 Prensenter name here.ppt
- What we are monitoring: > 12.000 Systems, we have about 12.000 unique MAC addresses in our db and we can only get to MAC addresses for a part of the systems we monitor (mostly systems using DHCP) since we do not yet connect to infrastructure that assign fixed IP addresses. We added ARP monitoring to correlate static IP addresses with their MAC addresses but see only partially the ARP traffic since the taps are located at the network boundaries. We track about 200.000-600.000 unique domain names per day, 20K to 120K unique domain names per hour, just to give you an idea.