摘要 :
Convolutional neural network (CNN) algorithms are utilized to build a machine learning block to assist the optimizations of voltage noise, temperature distribution, and security of multi-phase on-chip switched-capacitor (SC) volta...
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Convolutional neural network (CNN) algorithms are utilized to build a machine learning block to assist the optimizations of voltage noise, temperature distribution, and security of multi-phase on-chip switched-capacitor (SC) voltage converters. All the random sequences generated by the pseudorandom number generator (PRNG) are fed into the designed machine learning block in an SC converter sequentially to filter the unsatisfactory sequences. The results show that the maximum amplitude of the voltage noise and the highest temperature of the SC converter are reduced by 68.98% and 12.07%, respectively, while a negligible security degradation is achieved under the assistance of machine learning.
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In internet of things (IoT), ring oscillator physical unclonable functions (ROPUFs) are utilized for designing the wireless sensors against malicious invasive-attacks. However, the ROPUF sensors are not sufficiently secure since t...
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In internet of things (IoT), ring oscillator physical unclonable functions (ROPUFs) are utilized for designing the wireless sensors against malicious invasive-attacks. However, the ROPUF sensors are not sufficiently secure since there are strong linear relationships between the supply voltage and the oscillating frequencies of the ring oscillators of the ROPUFs. In order to demonstrate the vulnerability of the ROPUF sensors, in this paper, a hardware Trojan attack is performed by inserting a sequential Trojan circuit into the ROPUF sensors to leak the critical oscillating frequency. As shown in the result, analyzing about 200,000 number of leaked data with machine learning techniques are sufficient to crack a 128-bit Trojan-infected ROPUF sensor. Ultimately, so as to combat the hardware Trojan attack, a Trojan detection methodology is proposed by monitoring the statistical distribution of sensed data in real-time.
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摘要 :
Convolutional neural network (CNN) algorithms are utilized to build a machine learning block to assist the optimizations of voltage noise, temperature distribution, and security of multi-phase on-chip switched-capacitor (SC) volta...
展开
Convolutional neural network (CNN) algorithms are utilized to build a machine learning block to assist the optimizations of voltage noise, temperature distribution, and security of multi-phase on-chip switched-capacitor (SC) voltage converters. All the random sequences generated by the pseudorandom number generator (PRNG) are fed into the designed machine learning block in an SC converter sequentially to filter the unsatisfactory sequences. The results show that the maximum amplitude of the voltage noise and the highest temperature of the SC converter are reduced by 68.98% and 12.07%, respectively, while a negligible security degradation is achieved under the assistance of machine learning.
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摘要 :
In internet of things (IoT), ring oscillator physical unclonable functions (ROPUFs) are utilized for designing the wireless sensors against malicious invasive-attacks. However, the ROPUF sensors are not sufficiently secure since t...
展开
In internet of things (IoT), ring oscillator physical unclonable functions (ROPUFs) are utilized for designing the wireless sensors against malicious invasive-attacks. However, the ROPUF sensors are not sufficiently secure since there are strong linear relationships between the supply voltage and the oscillating frequencies of the ring oscillators of the ROPUFs. In order to demonstrate the vulnerability of the ROPUF sensors, in this paper, a hardware Trojan attack is performed by inserting a sequential Trojan circuit into the ROPUF sensors to leak the critical oscillating frequency. As shown in the result, analyzing about 200,000 number of leaked data with machine learning techniques are sufficient to crack a 128-bit Trojan-infected ROPUF sensor. Ultimately, so as to combat the hardware Trojan attack, a Trojan detection methodology is proposed by monitoring the statistical distribution of sensed data in real-time.
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摘要 :
In open society-based applications, inferring unknown trust relations attracts increasing attention in recent years. Most existing work assumes that trust relations are static. In this paper, we incorporate temporal dynamics in tr...
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In open society-based applications, inferring unknown trust relations attracts increasing attention in recent years. Most existing work assumes that trust relations are static. In this paper, we incorporate temporal dynamics in trust prediction by modeling the dynamics of user preferences in two principled ways. Initial experiments on a real-world data set are conducted and the results demonstrate the effectiveness of the proposed models.
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In this paper, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their chal...
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In this paper, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their challenge-to-response pairs (CRPs) against machine learning attacks. Moreover, an internal confidential data is added to the secret key of the AES cryptographic circuit in the new PUF architecture to update the secret key in real-time against side-channel attacks. As shown in the results, even if 1 million number of data are enabled by the adversary to implement machine learning or side-channel attacks, the proposed PUF can not be cracked. By contrast, only 100,000 (1,000) number of data are sufficient to leak the confidential information of a conventional PUF via machine learning (side-channel) attacks.
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摘要 :
In this paper, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their chal...
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In this paper, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their challenge-to-response pairs (CRPs) against machine learning attacks. Moreover, an internal confidential data is added to the secret key of the AES cryptographic circuit in the new PUF architecture to update the secret key in real-time against side-channel attacks. As shown in the results, even if 1 million number of data are enabled by the adversary to implement machine learning or side-channel attacks, the proposed PUF can not be cracked. By contrast, only 100,000 (1,000) number of data are sufficient to leak the confidential information of a conventional PUF via machine learning (side-channel) attacks.
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In this study, a multi-channel Electroencephalogram (EEG) mental fatigue detection algorithm is proposed based on the Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) network. The CNN-LSTM deep network model is used...
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In this study, a multi-channel Electroencephalogram (EEG) mental fatigue detection algorithm is proposed based on the Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) network. The CNN-LSTM deep network model is used to distinguish three different mental fatigue states: awake, mild fatigue and severe fatigue. The model is validated on a self-collected dataset collected by the 2-BACK experimental paradigm. The data set contains fatigue data for a total of 10 healthy adults without adverse habits. The average recognition accuracy of the model is 97.12%. The model yields a sensitivity of 97.80% and a specificity of 99.28%. Results show that our proposed CNN-LSTM model can distinguish the three different mental fatigue states effectively.
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In this paper, the leakage power analysis (LPA) attack in the breakdown mode is explored as a new kind of side-channel attacks (SCA) to leak the secret key of cryptographic circuits. As demonstrated in the simulation result, a cry...
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In this paper, the leakage power analysis (LPA) attack in the breakdown mode is explored as a new kind of side-channel attacks (SCA) to leak the secret key of cryptographic circuits. As demonstrated in the simulation result, a cryptographic circuit with a conventional LPA attack countermeasure is quite vulnerable against the LPA attack in the breakdown mode due to the malfunction of the countermeasure. In order to protect the cryptographic circuit against the LPA attack in the breakdown mode, breakdown-aware power noise insertion circuit (BAPNIC) is proposed as a new LPA attack countermeasure by generating a large amplitude of random power noise to scramble the leakage power profile of the cryptographic circuit, once the cryptographic circuit is set to the breakdown mode. The result shows that the BAPNIC-based countermeasure enhances the measurement-to-disclose (MTD) value of the cryptographic circuit over 2,000 times against the LPA attack in the breakdown mode.
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摘要 :
In this paper, the leakage power analysis (LPA) attack in the breakdown mode is explored as a new kind of side-channel attacks (SCA) to leak the secret key of cryptographic circuits. As demonstrated in the simulation result, a cry...
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In this paper, the leakage power analysis (LPA) attack in the breakdown mode is explored as a new kind of side-channel attacks (SCA) to leak the secret key of cryptographic circuits. As demonstrated in the simulation result, a cryptographic circuit with a conventional LPA attack countermeasure is quite vulnerable against the LPA attack in the breakdown mode due to the malfunction of the countermeasure. In order to protect the cryptographic circuit against the LPA attack in the breakdown mode, breakdown-aware power noise insertion circuit (BAPNIC) is proposed as a new LPA attack countermeasure by generating a large amplitude of random power noise to scramble the leakage power profile of the cryptographic circuit, once the cryptographic circuit is set to the breakdown mode. The result shows that the BAPNIC-based countermeasure enhances the measurement-to-disclose (MTD) value of the cryptographic circuit over 2,000 times against the LPA attack in the breakdown mode.
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