Improving Power Analysis Peak Distribution Using Canberra Distance to Address Ghost Peak Problem

Improving Power Analysis Peak Distribution Using Canberra Distance to Address Ghost Peak Problem

Hridoy Jyoti Mahanta (Computer Science and Engineering, Assam University, Silchar, India) and Ajoy Kumar Khan (Computer Science and Engineering, Assam University, Silchar, India)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJISP.2018070103
OnDemand PDF Download:
No Current Special Offers


This article describes how differential power analysis has laid the foundations of such an attack that has challenged the security of almost all cryptosystems like DES, AES, and RSA. This non-invasive attack first extracts the power consumption details from devices embedded with cryptographic techniques and then uses these details to mount attacks on the cryptosystems to reveal the secret key. However, at times there appears multiple similar power peaks at the same points. This raises confusion in distinguishing the actual and the fake peaks named “ghost peaks.” This ghost peak problem affects the efficiency of power analysis attacks as it increases the number of power traces to be evaluated to identify the actual peak. In this article, the authors present an approach which uses the Canberra distance with Euclidean similarity to address this ghost peak problem. The proposed solution diminishes the values of all these ghost peaks, leaving only the actual peak behind that could reveal the secret key.
Article Preview


The evolution of power analysis attacks which could analyze the power consumptions of cryptographic devices like smart cards and break its security has challenged all the major private and public key cryptographic algorithms. The most convincing reason for using power as a medium of such attacks is due to the absence of internal cells in such devices. As a result, there is always a need of power supply from external sources for their executions. Use of Complementary Metal-oxide Semiconductors (CMOS) for designing the modern cryptographic devices has highly contributed in meeting their computational requirements. However, the property of CMOS by which its total power depends on the data and operations performed, as shown in equation 1, left a loophole for the eavesdroppers.

(1) where, Pnoise was due to unwanted noise which may be removed by techniques like averaging and Pconst. was the constant power of the hardware. Hence, if the instantaneous power consumption details of such devices could be monitored and retrieved, it could reveal both its operations as well as the data used (Sun, Yen & Zambreno, 2008). For cryptographic devices, these data could be plaintext, cipher text or the secret key used for encryption and decryption. Differential Power Analysis (DPA) attacks which analyses the power consumption statistically was first discussed by P. Kocher et al. (1999) where they examined Data Encryption Standard (DES) embedded cryptographic devices. Their work not only showed the success of power analysis to retrieve the secret key, but also the possibility to mount such attacks on any cryptographic devices. Later, J. D. Grolic et al. (2002), M. Joyce et al. (2005), J. Jaffe (2007) and many others showed than even Advanced Encryption Standard (AES) was vulnerable to DPA. Soon a number of works to make power analysis more potent and effective appeared in literature. However, the performance of these attacks significantly ascended with the evolution of Correlation Power Analysis (CPA) where the dependency between the power consumption of the device with respect to the processed data were analyzed through power models (Brier, Clavier & Oliver, 2004). But, while computing correlations there appeared some additional unwanted power consumption peaks called “ghost peaks” which created confusion in identifying the actual power details in the device. Due to these ghost peaks, a large number of power traces were required for analysis, thus challenging the early threat of power analysis attacks.

Complete Article List

Search this Journal:
Volume 17: 1 Issue (2023)
Volume 16: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 15: 4 Issues (2021)
Volume 14: 4 Issues (2020)
Volume 13: 4 Issues (2019)
Volume 12: 4 Issues (2018)
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing