Skip to main content

Advertisement

SpringerLink
Log in

A novel cuckoo search strategy for automated cryptanalysis: a case study on the reduced complex knapsack cryptosystem

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

During the past decade several new variants of knapsack cryptosystems have been reported in the literature. Hence, there is a growing demand for automated cryptanalysis of knapsack cryptosystems. Brute force approach is capable to cryptanalyze simple stages of cryptosystems while cryptanalysis of complex cryptosystems demands efficient methods and high-speed computing systems. In the literature, several search heuristics have proven to be promising and effective in automated cryptanalysis (or attacks) of classical or reduced cryptosystems. This paper presents the automated cryptanalysis of the reduced multiplicative knapsack cryptosystem using three different search heuristics, namely, cuckoo search, particle swarm optimization and genetic algorithm. It should be noted that the considered cryptosystem is reduced but is complex and practical representative. To the best of our knowledge, this is the first time when the cuckoo search is utilized for automated cryptanalysis of the complex cryptosystem. The performance of developed techniques has been measured in terms of time taken by the algorithm (i.e., how efficient the algorithm is?), number of times the original plaintext is determined (i.e., success rate), and the number of candidate plaintexts is examined before determining the original plaintext (i.e., how effective the algorithm is?). For the case considered, performance of the proposed techniques, namely, novel binary cuckoo search (NBCS), improved genotype–phenotype binary particle swarm optimization (IGPBPSO), and new genetic algorithm (NGA) is as follows: roughly the NBCS technique is 12% and 8% more efficient, 6% and 5% more successful, and 16% and 12% more effective than IGPBPSO and NGA, respectively. This results show that the proposed NBCS strategy is superior to IGPBPSO and NGA, and therefore NBCS strategy can be used as an efficient and effective choice for solving similar binary discrete problems such as 0–1 knapsack problem, set covering problem, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • AbdulHalim MF, Bara’a AA, Hameed SM (2008) A binary particle swarm optimization for attacking knapsacks cipher algorithm. In: International conference on computer and communication engineering, pp 77–81, IEEE

  • Awad WS, El-Alfy ES (2015) Computational intelligence in cryptology. Improv Inf Secur Pract Through Comput Intell, 28–45

  • Bansal JC, Deep K (2012) A modified binary particle swarm optimization for knapsack problems. Appl Math Comput 218(22):11042–11061

    MathSciNet  MATH  Google Scholar 

  • Bhateja AK, Bhateja A, Chaudhury S, Saxena PK (2015) Cryptanalysis of vigenere cipher using cuckoo search. Appl Soft Comput 26:315–324

    Article  Google Scholar 

  • Boryczka U, Dworak K (2014) Genetic transformation techniques in cryptanalysis. In: Asian conference on intelligent information and database systems, Springer, pp 147–156

  • Danziger M, Henriques MA (2012) Computational intelligence applied on cryptology: a brief review. IEEE Lat Am Trans 10(3):1798–1810

    Article  Google Scholar 

  • Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, London

    Book  Google Scholar 

  • Forsyth WS, Safavi-Naini R (1993) Automated cryptanalysis of substitution ciphers. Cryptologia 17(4):407–418

    Article  MATH  Google Scholar 

  • Garg P, Shastri A (2006) An improved cryptanalytic attack on knapsack cipher using genetic algorithm. Int J Inf Technol 3(3):145–152

    Google Scholar 

  • Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99

    Article  Google Scholar 

  • Gonzalez TF (2007) Handbook of approximation algorithms and metaheuristics. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  • Hei X, Song B (2014) SHipher: families of block ciphers based on subset-sum problem. IACR Cryptol ePrint Arch 2014:103

    Google Scholar 

  • Jadon SS, Bansal JC, Tiwari R, Sharma H (2014) Artificial bee colony algorithm with global and local neighborhoods. Int J Syst Assur Eng Manag 1–13

  • Jain A, Bharadwaj A (2017) A genotype–phenotype binary particle swarm optimization technique with Lévy flights. In: ICONIP 2017, LNCS Springer (Accepted)

  • Jain A, Chaudhari NS (2014) Cryptanalytic results on knapsack cryptosystem using binary particle swarm optimization. In: International joint conference SOCO’14-CISIS’14-ICEUTE’14, Springer, pp 375–384

  • Jain A, Chaudhari NS (2015a) A new heuristic based on the cuckoo search for cryptanalysis of substitution ciphers. In: International conference on neural information processing, LNCS Springer, pp 206–215

  • Jain A, Chaudhari NS (2015b) Evolving highly nonlinear balanced boolean functions with improved resistance to dpa attacks. In: 9th International conference on network and system security, LNCS Springer, pp 316–330

  • Karagöz S, Yıldız AR (2017) A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. Int J Veh Des 73(1–3):179–188

    Article  Google Scholar 

  • Kate A, Goldberg I (2011) Generalizing cryptosystems based on the subset sum problem. Int J Inf Secur 10(3):189–199

    Article  Google Scholar 

  • Kennedy J, Eberhart RC et al. (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, pp 1942–1948, IEEE

  • Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on systems, man, and cybernetics, vol 5, pp 4104–4108, IEEE

  • Khanesar MA, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization. In: International conference on control & automation, MED’07, pp 1–6, IEEE

  • Kiani M, Yildiz AR (2016) A comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization. Arch Comput Methods Eng 23(4):723–734

    Article  MathSciNet  MATH  Google Scholar 

  • Laskari EC, Meletiou GC, Stamatiou YC, Vrahatis MN (2007) Cryptography and cryptanalysis through computational intelligence. In: Computational intelligence in information assurance and security, Springer, pp 1–49

  • Lee S, Hong S (2016) Modified binary particle swarm optimization for multidimensional knapsack problem. Adv Sci Lett 22(11):3688–3691

    Article  Google Scholar 

  • Ma EY, Obimbo C (2011) An evolutionary computation attack on one-round TEA. Procedia Comput Sci 6:171–176

    Article  Google Scholar 

  • Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev E 49(5):46–77

    Article  Google Scholar 

  • Martin KM (2017) Everyday cryptography: fundamental principles and applications. Oxford Press, Oxford

    Book  MATH  Google Scholar 

  • Matthews RA (1993) The use of genetic algorithms in cryptanalysis. Cryptologia 17(2):187–201

    Article  Google Scholar 

  • Menezes AJ, Van Oorschot PC, Vanstone SA (2010) Handbook of applied cryptography. CRC Press, Boca Raton

    MATH  Google Scholar 

  • Merkle R, Hellman M (1978) Hiding information and signatures in trapdoor knapsacks. IEEE Trans Inf Theory 24(5):525–530

    Article  Google Scholar 

  • Michalewicz Z (2013) Genetic algorithms + data structures = evolution programs. Springer, New York

    MATH  Google Scholar 

  • Muthuregunathan R, Venkataraman D, Rajasekaran P (2009) Cryptanalysis of knapsack cipher using parallel evolutionary computing. Int J Recent Trends Eng 1(1):3–6

    Google Scholar 

  • Nalini N, Rao GR (2007) Attacks of simple block ciphers via efficient heuristics. Inf Sci 177(12):2553–2569

    Article  MATH  Google Scholar 

  • Palit S, Sinha SN, Molla MA, Khanra A, Kule M (2011) A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm. In: 2nd International conference on computer and communication technology (ICCCT), pp 428–432, IEEE

  • Pampara G, Franken N, Engelbrecht AP (2005) Combining particle swarm optimisation with angle modulation to solve binary problems. In: IEEE congress on evolutionary computation, vol 1, pp 89–96, IEEE

  • Pholdee N, Bureerat S, Yıldız AR (2017) Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame. Int J Veh Des 73(1–3):20–53

    Article  Google Scholar 

  • Pohlig SC, Hellman ME (1978) An improved algorithm for computing logarithms over and its cryptographic significance. IEEE Trans Inf Theory 24(1):106–110

    Article  MathSciNet  MATH  Google Scholar 

  • Sadri J, Suen CY (2006) A genetic binary particle swarm optimization model. In: IEEE congress on evolutionary computation, pp 656–663, IEEE

  • Sharma K, Chhamunya V, Gupta PC, Sharma H, Bansal JC (2015) Fitness based particle swarm optimization. Int J Syst Assur Eng Manag 6(3):319–329

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE world congress on computational intelligence, pp 69–73, IEEE

  • Shor PW (1997) Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J Comput 41(2):303–332

    MathSciNet  MATH  Google Scholar 

  • Sinha SN, Palit S, Molla MA, Khanra A, Kule M (2011) A cryptanalytic attack on knapsack cipher using differential evolution algorithm. In: Recent advances in intelligent computational systems (RAICS), pp 317–320, IEEE

  • Spillman R (1993) Cryptanalysis of knapsack ciphers using genetic algorithms. Cryptologia 17(4):367–377

    Article  MATH  Google Scholar 

  • Spillman R, Janssen M, Nelson B, Kepner M (1993) Use of a genetic algorithm in the cryptanalysis of simple substitution ciphers. Cryptologia 17(1):31–44

    Article  Google Scholar 

  • Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26

    Article  Google Scholar 

  • Stinson DR (2005) Cryptography: theory and practice. CRC Press, Boca Raton

    MATH  Google Scholar 

  • Vose MD (1999) The simple genetic algorithm: foundations and theory. MIT press, Cambridge

    MATH  Google Scholar 

  • Wang B, Hu Y (2010) Quadratic compact knapsack public-key cryptosystem. Comput Math Appl 59(1):194–206

    Article  MathSciNet  MATH  Google Scholar 

  • Wang B, Wu Q, Hu Y (2007) A knapsack-based probabilistic encryption scheme. Inf Sci 177(19):3981–3994

    Article  MathSciNet  MATH  Google Scholar 

  • Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing, NaBIC 2009, pp 210–214, IEEE

  • Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2014) Swarm intelligence and bio-inspired computation: theory and applications, Newnes

  • Yildiz AR (2013a) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intell 26(1):327–333

    Article  Google Scholar 

  • Yildiz AR (2013b) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64:55–61

    Article  Google Scholar 

  • Yildız AR (2009) Hybrid immune-simulated annealing algorithm for optimal design and manufacturing. Int J Mater Prod Technol 34(3):217–226

    Article  Google Scholar 

  • Yıldız AR (2009a) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robot Comput Integr Manuf 25(2):261–270

    Article  Google Scholar 

  • Yıldız AR (2009b) An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mater Process Technol 209(6):2773–2780

    Article  Google Scholar 

  • Yıldız BS (2017) A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems. Int J Veh Des 73(1–3):208–218

    Google Scholar 

  • Yıldız BS, Lekesiz H (2017) Fatigue-based structural optimisation of vehicle components. Int J Veh Des 73(1–3):54–56

    Google Scholar 

  • Yildiz AR, Saitou K (2011) Topology synthesis of multicomponent structural assemblies in continuum domains. J Mech Des 133(1):1–9

    Article  Google Scholar 

  • Yildiz BS, Huseyin L, Ali RY (2016) Structural design of vehicle components using gravitational search and charged system search algorithms. Mater Test 58(1):79–81

    Article  Google Scholar 

  • Yıldız AR, Kurtuluş E, Demirci E, Yıldız BS, Karagöz S (2016) Optimization of thin-wall structures using hybrid gravitational search and Nelder–Mead algorithm. Mater Test 58(1):75–78

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Discipline of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore, Indore, India

    Ashish Jain & Narendra S. Chaudhari

  2. Discipline of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT) Nagpur, Nagpur, India

    Narendra S. Chaudhari

Authors
  1. Ashish Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Narendra S. Chaudhari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Jain.

Appendices

Appendix A

Appendix B

See Table 7

Table 7 public Key Generated by RMKC Scheme
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jain, A., Chaudhari, N.S. A novel cuckoo search strategy for automated cryptanalysis: a case study on the reduced complex knapsack cryptosystem. Int J Syst Assur Eng Manag 9, 942–961 (2018). https://doi.org/10.1007/s13198-017-0690-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-017-0690-9

Keywords

Search

Navigation