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Fog computing

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Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks,
Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network.
Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network .
As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data.

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Fog computing

  1. 1. Prepared By: Ankit Ap www.facebook.com/Ank1tAp FOG COMPUTING (INTERNET OF THINGS)
  2. 2.  User Behavior Profiling.  Application of Fog Computing.  Decoy System.  Security And Privacy issues.  Conclusion.  Future Scope & Prediction.  References.  Abstract.  Introduction.  Existing System.  Disadvantage Of Existing System.  Internet Of Things (IOT).  Proposed System.  Characteristics. CONTENTS
  3. 3. • Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks ABSTRACT
  4. 4. • Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network. • Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network . • As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data. INTRODUCTION
  5. 5. • Cloud computing is a type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Cloud computing is comparable to grid computing, a type of computing where unused processing cycles of all computers in a network are harnesses to solve problems too intensive for any stand-alone machine. EXISTING SYSTEM
  6. 6. DISADVANTAGE OF EXISTING SYSTEM • ENCRYPTION WAS FAILED IN SECURING THE DATA FROM THE ATTACKER. • IT DOES NOT VERIFY WHETHER THE USER IS AUTHORISED OR NOT. • NO BODY IS IDENTIFIED WHEN THE ATTACK HAPPENS. • IT IS COMPLEX TO DETECT WHICH USER IS ATTACK. • WE CAN’T DETECT WHICH FILE IS BEING HACKED. • LATENCY TOO HIGH. • RESILIENCY IMPRACTICAL.
  7. 7. INTERNET OF THINGS (IOT) • The Internet of Things (IoT) is the network of physical objects—devices, vehicles, buildings and other items embedded with electronics, software, sensors, and network connectivity—that enables these objects to collect and exchange data.
  8. 8. • We proposed a completely new technique to secure user’s data in cloud using user behavior Profiling and Decoy information technology called as fog computing. • In this technique when the unauthorized person try to access the data of the real user the system generates the fake documents in such a way that the unauthorized person was also not able to identify the data is fake or real. PROPOSED SYSTEM
  9. 9. • SECURITY • - STRONG SECURITY (HARDWARE ROOT OF TRUST) • - RESILIENCY/FAULT TOLERANCE. • PROGRAMMABILITY • - MULTIPLE APPLICATION SUPPORT. • - VERSATILITY IN OPERATING ENVIRONMENT. CHARACTERISTICS
  10. 10. • REAL TIME FEATURES - DETERMINISTIC TIMING CAPABILITIES. • SUPPORTS MULTIPLE OPERATING PLATFORMS:UNIX,WINDOWS,MAC etc. • EMPLOYS SIMPLE,FAST AND STANDARIZED IOT INTERNET PROTOCOLS(TCP/IP ,SOCKETS etc.). • RUNS ON AFFORDABLE,OFF THE SHELF COMPUTING TECHNOLOGIES. CHARACTERISTICS
  11. 11. • Admin monitor data access in the cloud and notice abnormal data access patterns User profiling will a well known Technique that can be applied here to check how, when, and how much a client access their data in the Cloud. • This method of behavior based security will regularly used in scheme uncovering applications. USER BEHAVIOR PROFILING
  12. 12. • IT INCLUDES VOLUMETRIC INFORMATION,HOW MANY INFORMATIONS ARE TYPICALLY READ AND HOW OFTEN. • “NORMAL USER ” BEHAVIOR IS CONTINUOUSLY CHECKED TO DETERMINE ABNORMAL ACCESS . • THIS SECURITY IS COMMONLY USED IN FRAUD DETECTION APPLICATION. USER BEHAVIOR PROFILING
  13. 13. • SOFTWARE DEFINED NETWORKS(SDN): SDN CONCEPT TOGETHER WITH FOG WILL RESOLVE THE ISSUE IN VEHICULAR NETWORKS , INTERMITTENT CONNECTIVITY , COLLISION HIGH PACKET LOSS RATE. • IoT AND CYBER-PHYSICAL SYSTEMS(CPSs): INTEGRATE THE ABSTRACTIONS AND PRECISION OF SOFTWARE AND NETWORKING WITH THE DYNAMICS IN THE PHYSICAL ENVIRONMENT. • DECENTRALIZED SMART BUILDING CONTROL: WITH FOG COMPUTING ,SMART BUILDING CAN MAINTAIN THEIR FABRIC,EXTERNAL AND INTERNAL ENVIRONMENTS. APPLICATION OF FOG COMPUTING.
  14. 14. • The main security issues are authentication at different levels of gateways as well as (in case of smart grids) at the smart meters installed in the consumer’s home. • Each smart meter and smart appliance has an IP address. A malicious user can either tamper with its own smart meter, report false readings, or spoof IP addresses. • In smart grids, privacy issues deal with hiding details, such as what appliance was used at what time, while allowing correct summary information for accurate charging. SECURITY AND PRIVACY ISSUES
  15. 15. • Decoy system is a different approach for securing data in the cloud using nasty decoy technology. • data access in the cloud and sense irregular data access patterns. • We use this technology to begin disinformation attacks against malicious insiders, preventing them from distinguishing the valid aware customer data from bogus useless. DECOY SYSTEM
  16. 16. DECOY SYSTEM
  17. 17. • Extending the cloud closer to the things that generate and act on data benefits the business in the following ways: • Greater business agility: With the right tools, developers can quickly develop fog applications and deploy them where needed. • Better security: Protect your fog nodes using the same policy, controls, and procedures you use in other parts of your IT environment. Use the same physical security and ADVANTAGE OF FOG COMPUTING
  18. 18. • ● Deeper insights, with privacy control: Analyze sensitive data locally instead of sending it to the cloud for analysis. • ● Lower operating expense: Conserve network bandwidth by processing selected data locally instead of sending it to the cloud for analysis. ADVANTAGE OF FOG COMPUTING
  19. 19. CONCLUSION • Fog computing gives the cloud a companion to handle the two exabytes of data generated daily from the Internet of Things. • Processing data closer to where it is produced and needed solves the challenges of exploding data volume, variety, and velocity. • Fog computing accelerates awareness and response to events by eliminating a round trip to the cloud for analysis.
  20. 20. CONCLUSION • It avoids the need for costly bandwidth additions by offloading gigabytes of network traffic from the core network. • It also protects sensitive IoT data by analyzing it inside company walls. Ultimately, organizations that adopt fog computing gain deeper and faster insights, leading to increased business agility, higher service levels, and improved safety.
  21. 21. • 2010, IBM (describing their Rational software design) • “A World with 1 Trillion Connected Devices” … by 2015 • 2011, Ericsson CEO Hans Vestberg • “50 Billion Connected Devices” … by 2020 • 2013, ABI Research report • “30 Billion” … by 2020 • 2013, Morgan Stanley report • “75 Billion Devices Connected to the IoT” … by 2020 • 2014, Intel infographic • “31 billion devices connected to the Internet” … by 2020 • 2014, ABI Research updated report • “41 billion active wireless connected devices” … by 2020 FUTURE SCOPE & PREDICTIONS
  22. 22. FUTURE SCOPE & PREDICTIONS • Cellular M2M Devices send on average: 2 MBytes / month • India: 15M cellular devices (8% of all M2M): 30 TBytes / Month Total cellular M2M data use is increasing • Number of cellular M2M devices (at 5%): 2.5 Billion • Data (assuming 4MB / month): 10,000,000 TBytes / month With 50 Billion Devices by 2020 • Transmissions and total data haystacks are enormous • Demands “processing at the edge” … at device  Fog Computing Even with conservative estimates
  23. 23. • Sources • Fog Computing and the Internet of Things: Extend http://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing- • Cisco Fog Computing Solutions: Unleash the Power of Internet • http://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-solutions.pdf • A Simple Explanation Of 'The Internet Of Things' – Forbes • http://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can- understand/#793c56ca6828 • The Internet of Things (IoT) Starts with Intel Inside® • http://www.intel.com/content/www/us/en/internet-of-things/overview.html BIBLIOGRAPHY

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