Modeling of cyber-attacks obfuscation, based on alteration technique of attack

Document Type : Original Article

Authors

1 Imam Hossein Comprehensive University

2 malek ashtar university

3 (استادیار دانشگاه جامع امام حسین(ع

Abstract

With the increasing rate of cyber-attacks, creating security for cyberspace has become more important and crucial. Therefore computers, computer networks and all current systems connected to the Internet are always at risk of cyber-attacks. In this paper, a novel technique based on alteration technique of attack is proposed by providing a new classification in the methods of obfuscation.
 In this method, by replacing the attacks that have similar characteristics in the attack strategies the attacker causes an increase in wrong classification and thus reduces the dependence between attack steps. Therefore, by increasing the length of the attack, network security managers cannot easily distinguish cyber-attacks.
The proposed model was assessed based on the Bayesian algorithm.
 The results of the research and implementation of the model indicate that the accuracy of classification (in terms of log) by intrusion detection systems for the best case of clean attacks in the sequel of attack, is -0.02 and for obfuscation attacks at the action level is -0.19. For obfuscate attacks with the alternative technique it becomes -3 and for the insertion technique it decreases to -6.74. In the proposed model, as in the obfuscation-based insertion technique, the corresponding attack method has been used.
  Due to the difference in the type of ambiguity model, different results are obtained, and the combination of these two obfuscating techniques in cyber-attacks can bring better results to the attacker in deceiving the intrusion detection systems and creating uncertainties in the sequence of observed attacks.

Keywords

References

 [1] A. Kott, C. Wang, and R. F. Erbacher, “Cyber defense and situational awareness,” vol. 62, Springer, 2015.##
[2] A. Veeraswamy, S. Appavu, and E. Kannan, “An Implementation of Efficient Datamining Classification Algorithm using Nbtree,” International Journal of Computer Applications, 2013.##
[3] F. Valeur, et al., “Comprehensive approach to intrusion detection alert correlation,” IEEE Transactions on dependable and secure computing, pp. 146-169, 2004.##
[4]  S. Ruggieri, “Efficient C4. 5 [classification algorithm] transactions on knowledge and data engineering,” IEEE,  vol. 14, no. 2, pp. 438-444, 2002.##
[5]   A. Kott, C. Wang, and R. F. Erbacher, “Cyber defense and situational awareness,” Springer, vol. 62, 2015.##
[6]  H. Du and S. J. Yang, “Probabilistic inference for obfuscated network attack sequences,” Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2014.#
 [7]  H. Debar and M. Dacier, “Towards a taxonomy of intrusion-detection systems,” Computer Networks, vol. 31, no. 8, pp. 805–822, 1999.##
[8] S. Parsa, H. salehi, and M. H. Alaeiyan, “Code Obfuscation to Prevent Symbolic Execution,” Journal of Electoronic & Cyber defence, Imam Hossein Comprhensive Univercity, vol. 6, no. 1, 2018.)In persian)##
 [9] M. H. Najari, “The design and simulation of an efficient algorithm for modeling the obfuscation of cyber attacks based on action insertion,” M.Sc, Malek-e-Ashtar University, 2017. (In persian)##
 [10] N. Ghafori, “The design and simulation of an efficient algorithm for modeling the obfuscation of cyber attacks based on action alteration,” M.Sc, Malek-e-Ashtar University, 2017. (In persian)## 
[11] R. Aliabadi, “The design and simulation of an efficient algorithm for modeling the obfuscation of cyber attacks based on action removal,” M.Sc, Malek-e-Ashtar University, 2017 .(In persian)##
[12] H. Du, “Probabilistic Modeling and Inference for Obfuscated Network Attack   Sequences,” PhD diss, Rochester, New York, 8-2014.##
 [13] L. Wang, A. Liu, and S. Jajodia, “Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts,” Comput. Commun, vol. 29, no. 15, pp. 2917–2933, 2006.##
[14] C. Phillips and L. P. Swiler, “A graph-based system for network-vulnerability analysis,” presented at the Proceedings of the 1998 workshop on new security paradigms, pp. 71–79, 1998.##
[15] T. Tidwell, R. Larson, K. Fitch, and J. Hale, “Modeling internet attacks,” presented at the Proceedings of the IEEE Workshop on Information Assurance and security, vol. 59, 2001.##
[16] K. Daley, R. Larson, and J. Dawkins, “A structural framework for modeling multi-stage network attacks,” Presented at the Parallel Processing Workshops, Proceedings, International Conference on, pp. 5–10, 2002.##
[17] S. Noel and S. Jajodia, “Advanced vulnerability analysis and intrusion detection through predictive attack graphs,” Crit. Issues C4I Armed Forces Commun. Electron. Assoc. AFCEA Solut. Ser. Int. J. Command Control, 2009.##
[18] “Common Attack Pattern Enumeration and Classification (CAPEC) Schema Description,” 2019.##
[19] K. Shoushian, A. J. Rashidi, and M. Dehghani, “Modeling of cyber attacks obfuscation based insertion technique  of attack,” Journal of Electoronic & Cyber defence, Imam Hossein Comprhensive Univercity(printing ), (In persian).##

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History

  • Receive Date: 31 December 2018
  • Revise Date: 15 July 2019
  • Accept Date: 04 March 2020
  • Publish Date: 21 May 2020

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