Generalizing Direct Sequence Spread-Spectrum to Mitigate Autocorrelation-Based Attacks
Avinash Kumar, Ye Zhu
Abstract
Avinash Kumar, Ye Zhu
Abstract
In this paper, we propose to generalize the Direct Sequence Spread Spectrum (DSSS) communication scheme with a Dual Hidden Markov Model-based Spread Spectrum (DHSS) communication scheme. The generalization aims to defeat existing autocorrelation-based attacks, which are proven effective on DSSS. DHSS is highly resistant to the autocorrelation-based attacks because chip sequences in DHSS are generated randomly for each information bit with Hidden Markov Models. As a spread spectrum communication technique, DHSS still takes advantage of benefits brought by processing gain. The cost of resistance to the autocorrelation-based attacks for DHSS is slight degradation in BER performance, about 2dB inferior to DSSS.
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In this paper, we propose to generalize the Direct Sequence Spread Spectrum (DSSS) communication scheme with a Dual Hidden Markov Model-based Spread Spectrum (DHSS) communication scheme. The generalization aims to defeat existing autocorrelation-based attacks, which are proven effective on DSSS. DHSS is highly resistant to the autocorrelation-based attacks because chip sequences in DHSS are generated randomly for each information bit with Hidden Markov Models. As a spread spectrum communication technique, DHSS still takes advantage of benefits brought by processing gain. The cost of resistance to the autocorrelation-based attacks for DHSS is slight degradation in BER performance, about 2dB inferior to DSSS.
Key concepts: Direct-sequence spread spectrum, Autocorrelation, Spread spectrum, Generalization, Computer science, Hidden Markov model, Sequence (biology), Algorithm