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Cognitive Artificial Intelligence Countermeasure for Enhancing the Security of Big Data Hardware from Power Analysis Attack
Putra S.D.a, Sumari A.D.W.b,c, Ahmad A.S.d, Sutikno S.d, Kurniawan Y.d
a Informatics Management, Politeknik Negeri Lampung, Kota Bandar Lampung, Indonesia
b Department of Electrical Engineering, State Polytechnic of Malang, Malang, Indonesia
c Faculty of Defense Technology, Indonesia Peace and Security Center (IPSC), Indonesia Defense University, Sentul, Indonesia
d Cognitive Artificial Intelligence Research Group (CAIRG), School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020, Springer Nature Switzerland AG.Digital communication systems as the part of big data are utilized to transmit data and information. The increase of the digital communication system utilization will increase the value of information and on the other hand also induces an increase in the number of attacks on such systems. Side Channel Attack (SCA) is an attack model that could disrupt the information security when hardware implements a cryptographic algorithm. Differential Power Analysis (DPA), a kind of SCA, can reveal 75% of secret key used in encryption hardware. Other techniques called Correlation Power Analysis (CPA) which uses correlation factor between trace and hamming weight from the input of key generation can reveal the right secret key of Advanced Encryption Standard (AES) in significantly shorter span of time. The objective of this research is to design and implement an electronic countermeasure to deal with power analysis attack. The attacking aspect is reviewed as a form of identification of the correct countermeasure method against power analysis attack using Cognitive Artificial Intelligence (CAI)‘s method called cognitive countermeasure approach in an AES encryption device. Our main contribution is in the design of cognitive-countermeasure by altering the measured power consumption in affecting the secret key value of power analysis. The measured signal is altered by generating random masking value using CAI’s information fusion. CAI is a new perspective in Artificial Intelligence which is characterized by its capability to grow new knowledge based on the information from the sensory system. The random alteration of measured signal and continuous evolution of the masking value by using CAI’s information fusion is very significant in tackling the risk of power analysis. We also succeeded in implementing an AES encryption device based on CAI method on the Field-Programmable Gate Array (FPGA) platform.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Big data,Cognitive Artificial Intelligence,Cognitive-countermeasure,Encryption,Field-Programmable Gate Array,Hardware attack,Information fusion,Information security,Knowledge-growing system,power analysis attack[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1007/978-3-030-35642-2_4[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]