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Cryptanalysis on FPGA Based Hardware PowerPoint PPT Presentation

Cryptanalysis on FPGA Based Hardware Malcolm Alda Sumantri Bachelors of Engineering (Software) & Bachelors of Commerce (Finance) Supervisors: Matt Barrie Craig Jin The University of Sydney Introduction Welcome to the Digital Age where everything can be replicated! Cryptography is used…

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Cryptanalysis on FPGA Based Hardware

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Cryptanalysis on FPGA Based Hardware

Malcolm Alda SumantriBachelors of Engineering (Software) & Bachelors of Commerce (Finance)

Supervisors:Matt BarrieCraig Jin

The University of Sydney


  • Welcome to the Digital Age where everything can be replicated!

  • Cryptography is used…

    • To protect our privacy

      • For example: our real identity, our e-mails to family and friends, our digital photos, our work.

    • To protect corporate secrets

      • For example: future corporate strategies, intellectual property, pricing information, human resources information.

    • Bygovernments

      • For example: sending messages to spies, task forces, between agencies to protect civilians and against terrorism.

  • How secure are our currently deployed cryptosystems?


  • Information security is a resource game.

    • More funds means more access to information.

      • The US National Security Agency’s annual budget is classified but is said to be over US $13 billion.

      • Assessing the strength of our cryptosystems therefore involves determining the cost to break them.

  • Rapid development in Field Programmable Gate Array Technology (FPGA) technology that makes it cheaper to develop high-performance custom hardware systems. FPGA technology has proven to be effective for cryptographic use.

  • A recent optimization in cryptanalysis.

    • Rainbow Tables


  • Symmetric Cipher

  • Cryptanalysis: Code breaking, reveal the plaintext without the key.

    • Exhaustive Key Search: Try every key possible, requires large computational power.

    • Table Lookup: Store keys and ciphertexts in a massive tables to perform a lookup when trying to attack, requires a large amount of memory (infeasible).

    • Time-memory trade-off: Give up memory to achieve a faster attack time.

  • FPGAs

    • Reconfigurable logic (upload the bitstream to the hardware).

    • Cheaper than Application Specific Integrated Circuits (ASICs) for small volumes.

Time-Memory Trade-Off:Rainbow Tables

  • How does it work?

    • Assume a chosen-plaintext attack scenario.

      • The attacker can choose which plaintext to access.

      • This attacker will use this to attack the cryptosystem.

      • This is practical in the real-world (UNIX password hashing, “#include <stdio.h>”, “\n”)

    • Two Phases

      • Precomputation Phase

      • Online Attack Phase (Cryptanalytic Attack)

  • Precomputation Phase: Generate a rainbow table.

    • A rainbow table is a two-column table (start-point, end-point)

    • These points are possible keys.

    • This table is generated by a specific algorithm.

  • Online Attack Phase: Use the rainbow table.

    • We are given a ciphertext to break.

    • Now we perform a search on the rainbow table by using another algorithm

  • This method is probabilistic, but faster than exhaustive key search.

  • Unlike exhaustive key search that only requires computational resources (processor). This method uses memory as well as computational resources.

  • As a result, the attack time is faster but we have given up memory. This is the trade-off.


  • Design and implement an FPGA based cryptanalytic system that uses the rainbow tables method of cryptanalysis.

  • Use the Data Encryption Standard (DES) as the test symmetric cipher.

    • DES uses a 56-bit key.

    • DES is the most widely studied cipher.

    • DES is still used today (UNIX password hashing).

  • Determine the cost to break DES.

  • Extrapolate the cost to break other ciphers.

Design I – Data Encryption Standard

  • In designing a cryptanalytic system, the performance of the cipher module will determine the performance.

  • Security of DES derives from 16 rounds of permutations, substitutions and xoring.

  • Each round is implemented as a 3-stage pipeline. A total of 48-stages for the 16 rounds of DES.

    • Pipelining improves performance:

      • Attain higher clock frequencies.

      • Achieve parallelization: 48 encryptions per clock cycle.

Design II – The Rainbow Table Precomputation System

1. High Level System Design

2. Hardware Design

3. Hardware output behavior (Timing Diagram)

Design III – The Rainbow Table Online Attack System

1. High Level System Design

2. Hardware Design

3. Mechanism

Experiment and Results

  • Experiment:

    • Cryptanalytic attack on 40-bit DES since the resources to break DES is out-of-reach for the budget in this thesis.

    • Use Sensory NetworksTM NodalCoreTM C-1000 PCI Card.

      • Xilinx® Virtex-II Pro VP-40 FPGA

      • Flexible chipset architecture to embed our hardware engines.

      • PCI interface allows for high-speed communications.

  • Results

    • 40-bit DES Rainbow Table can be generated in less than 4 hours. Table parameters allows for 85% cryptanalytic success probability.

      • Fastest known implementation in the literature based on results.

    • Online attack of 40-bit DES in 30.8 seconds.

Data Analysis

  • Performance-Cost Analysis

    • Determine the FPGA chip that provides the highest performance for the lowest cost.

    • Synthesized the hardware designs for various Xilinx FPGAs.

    • Spartan 3 S-1500 provides the highest performance-cost relative to other Xilinx® FPGA chips.

  • Extrapolate the design of a machine to break DES (56-bit key length)

    • Result: DES can be broken with 85% success probability in 72 minutes for an approximate cost of US $1,210.

Performance-Cost of Precomputation Hardware System


  • FPGAs provides a low cost and effective solution to cryptanalysis.

  • Rainbow table attacks provide a faster attack time compared to brute-force, but brute-force uses less resources, that is, memory resources.

    • For large key sizes, the rainbow table attack becomes infeasible as memory costs is prohibitive.

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