WB
W.L. Bubberman
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Hardware-based implementations in Side-Channel Analysis
A comparison study of DL SCA attacks against HW and SW AES and a novel methodology
Side-Channel Attacks (SCA) attempt to recover the secret cryptographic key from an electronic device by exploiting the unintended physical leakages of said device. With the devices that are being attacked becoming more sophisticated, so is SCA. In the past few years, the focus of the research in the field of SCA shifted towards the application of the powerful method of Deep Learning (DL). DL SCA methods can operate in a similar way as before seen methods in the profiled setting, such as Template Attack. In the profiled setting, SCA first creates a profile on a copy of the target device, to then subsequently use that same profile to perform a more powerful attack on the target device. DL SCA has proven to be quite the effective method, even showcasing its success against implementations utilising countermeasures. However, as DL SCA is fairly novel there still exist gaps in the knowledge we have of how to make DL SCA effective in all possible situations. Most research bases itself on software-based implementations, whilst rarely hardware-based implementations are discussed as they are often seen as more difficult to attack. Our contribution in this work is to showcase the difference in difficulty between hardware-based implementations and software-based implementations that both use countermeasures. We explore the attack performance of several state-of-the-art methods on hardware-based implementations with countermeasures and give insight into why their performance is the way it is. We also attempt to make a base methodology for attacking hardware-based implementations, both with and without countermeasures, as the current field of research is lacking this. Showcasing our suggested methodology, we achieve better than state-of-the-art results on a hardware-based implementation with countermeasures and competitive with the state-of-the-art results on a hardware-based implementation without countermeasures.
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Side-Channel Attacks (SCA) attempt to recover the secret cryptographic key from an electronic device by exploiting the unintended physical leakages of said device. With the devices that are being attacked becoming more sophisticated, so is SCA. In the past few years, the focus of the research in the field of SCA shifted towards the application of the powerful method of Deep Learning (DL). DL SCA methods can operate in a similar way as before seen methods in the profiled setting, such as Template Attack. In the profiled setting, SCA first creates a profile on a copy of the target device, to then subsequently use that same profile to perform a more powerful attack on the target device. DL SCA has proven to be quite the effective method, even showcasing its success against implementations utilising countermeasures. However, as DL SCA is fairly novel there still exist gaps in the knowledge we have of how to make DL SCA effective in all possible situations. Most research bases itself on software-based implementations, whilst rarely hardware-based implementations are discussed as they are often seen as more difficult to attack. Our contribution in this work is to showcase the difference in difficulty between hardware-based implementations and software-based implementations that both use countermeasures. We explore the attack performance of several state-of-the-art methods on hardware-based implementations with countermeasures and give insight into why their performance is the way it is. We also attempt to make a base methodology for attacking hardware-based implementations, both with and without countermeasures, as the current field of research is lacking this. Showcasing our suggested methodology, we achieve better than state-of-the-art results on a hardware-based implementation with countermeasures and competitive with the state-of-the-art results on a hardware-based implementation without countermeasures.
Bachelor thesis
(2020)
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Wolfgang Bubberman, Stefanie Roos, Satwik Prabhu Kumble, Ranga Rao Venkatesha Prasad
The Ripple XRP Ledger network hosts a cryptocurrency called XRP which uses the TLS protocol to send messages between nodes. It is crucial that the network is tested against attacks to ensure its security. The Ripple XRP Ledger could be vulnerable to a Man-in-the-Middle (MITM) attack. This MITM attack is an attack which can intercept and modify data by posing as intended receiver of the message. Potentially, this could have major implications as message content could be intercepted and used maliciously. Therefore this paper aims to answer the question: ``Is it possible to conduct a TLS MITM attack on the XRP ledger to gain access to message content and how does such an attack affect the performance of the system?". The research done in this paper concluded that it is possible to conduct a TLS MITM attack on the XRP Ledger. The impact on performance is a delay in the travel time of a message. However, how big this delay is, is dependent on the network the attack is executed on. In the tested environment it was roughly adding a 69.2\% delay to a message. Future research is necessary to be able to modify the message content and to confirm if the larger the delay added by the MITM, the higher the probability that the MITM is detected.
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The Ripple XRP Ledger network hosts a cryptocurrency called XRP which uses the TLS protocol to send messages between nodes. It is crucial that the network is tested against attacks to ensure its security. The Ripple XRP Ledger could be vulnerable to a Man-in-the-Middle (MITM) attack. This MITM attack is an attack which can intercept and modify data by posing as intended receiver of the message. Potentially, this could have major implications as message content could be intercepted and used maliciously. Therefore this paper aims to answer the question: ``Is it possible to conduct a TLS MITM attack on the XRP ledger to gain access to message content and how does such an attack affect the performance of the system?". The research done in this paper concluded that it is possible to conduct a TLS MITM attack on the XRP Ledger. The impact on performance is a delay in the travel time of a message. However, how big this delay is, is dependent on the network the attack is executed on. In the tested environment it was roughly adding a 69.2\% delay to a message. Future research is necessary to be able to modify the message content and to confirm if the larger the delay added by the MITM, the higher the probability that the MITM is detected.