Side-channel Attack Method Research Articles

Active and Passive Physical Attacks on Neural Network Accelerators

Neural networks are being increasingly deployed as the method of choice in a variety of real-world applications. These applications may be privacy-sensitive or have safety implications such as medical image analysis or autonomous driving. Furthermore, the network models are intellectual property, which should be kept secret. Due to that, reverse-engineering neural networks in order to retrieve the secret parameters has become a popular research topic. One way to achieve this is by gathering side-channel information and then inferring the topology and parameters of the target network. Additionally, it is possible to recover the input to a neural network by utilizing side-channel attack methods. Another threat comes from fault injection attacks, which can crash an accelerator or lead to information leakage. With the growing ubiquity of neural networks and its deployment in edge computing it becomes more important to be aware of physical attacks.

A Novel Power System Side Channel Attack Method Based on Machine Learning CNN-Transformer

Continuous advancements in cryptography and information technology have rendered current cryptographic algorithms highly robust against traditional cryptanalysis methods. However, in modern power systems, the equipment’s inherent process characteristics result in the leakage of side channel information during the operation of cryptographic algorithms. This information includes power consumption, electromagnetic signals, and timing data. Adversaries can exploit these side channels to compromise encryption keys. To address this issue, a groundbreaking power system side-channel attack method is introduced in this paper, leveraging the CNN-Transformer architecture in machine learning. The proposed approach utilizes power consumption analysis techniques to identify relevant points of interest in the side channel power consumption data. By employing a machine learning model for training, encryption can be breached. Empirical results demonstrate the superior attack efficiency of the model compared to LSTM and CNN models in side channel attacks.

A Survey of Commercial Password Detection Methods

Commercial password has been widely used in China's information services, and their role and importance in information security are gradually increasing. In recent years, as the application of quantum computing in computer technology becomes more and more extensive, the performance of computers continues to improve, and cryptographic penetration technology has emerged in an endless stream. Some known cryptographic algorithms are no longer secure in the new environment, such as the MD5 cryptographic algorithm with obvious security risks and the cracked RSA public key password algorithm, etc. Therefore, it is essential to detect and evaluate the security of commercial passwords. Based on a large number of literature at home and abroad and the practical work experience of Beijing Software Testing and Quality Assurance Center, this study comprehensively analyzes and combs the commercial password detection technology and forms a relatively complete research review on commercial password detection methods. The specific contents include traditional cryptanalysis methods, key penetration attack methods, side-channel attack methods, quantum computing cryptanalysis, etc. The research results provide important theoretical support for the commercial password security detection of the Winter Olympic information system, enhance the technical ability of the inspectors to the commercial password detection, and ensure the comprehensiveness of the information system security detection.

A Systematic Study on Spectre Attacks and Defenses

Spectre attacks is an important category of side channel methods, which allows attacker to obtainsensitive data by observing the system. Spectre attacks exploit modern processors’ features designedfor the performance: out-of-order execution and speculative execution. Also, in Spectre attacks,cache side-channel attack methods play an important role. The high-level goal of Spectre attacksis to load target data into the cache through the speculative execution. Once it has been done, thenext step is leaking information. To the end, the cache side-channel methods are employed to leakinformation, because there is no direct way to read data from the cache. In this paper, we discussvariations of Spectre attacks and discuss defense mechanisms for each of them.

Secure and Lightweight Conditional Privacy-Preserving Authentication for Securing Traffic Emergency Messages in VANETs

Owing to the development of wireless communication technology and the increasing number of automobiles, vehicular ad hoc networks (VANETs) have become essential tools to secure traffic safety and enhance driving convenience. It is necessary to design a conditional privacy-preserving authentication (CPPA) scheme for VANETs because of their vulnerability and security requirements. Traditional CPPA schemes have two deficiencies. One is that the communication or storage overhead is not sufficiently low, but the traffic emergency message requires an ultra-low transmission delay. The other is that traditional CPPA schemes do not consider updating the system secret key (SSK), which is stored in an unhackable Tamper Proof Device (TPD), whereas side-channel attack methods and the wide usage of the SSK increase the probability of breaking the SSK. To solve the first issue, we propose a CPPA signature scheme based on elliptic curve cryptography, which can achieve message recovery and be reduced to elliptic curve discrete logarithm assumption, so that traffic emergency messages are secured with ultra-low communication overhead. To solve the second issue, we design an SSK updating algorithm, which is constructed on Shamir's secret sharing algorithm and secure pseudo random function, so that the TPDs of unrevoked vehicles can update SSK securely. Formal security proof and analysis show that our proposed scheme satisfies the security and privacy requirements of VANETs. Performance analysis demonstrates that our proposed scheme requires less storage size and has a lower transmission delay compared with related schemes.

Deep learning side-channel attack against hardware implementations of AES

In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled images to be identified are input to a neural network, and repeatedly learning the images enables the neural network to identify objects with high accuracy. A new profiling side-channel attack method, the deep learning side-channel attack (DL-SCA), utilizes the neural network’s high identifying ability to unveil a cryptographic module’s secret key from side-channel information. In DL-SCAs, the neural network is trained with power waveforms captured from a target cryptographic module, and the trained network extracts the leaky part that depends on the secret. However, at this stage, the main target of investigation has been software implementation, and studies regarding hardware implementation, such as ASIC, are somewhat lacking. In this paper, we first depict deep learning techniques, profiling side-channel attacks, and leak models to clarify the relation between secret and side channels. Next, we investigate the use of DL-SCA against hardware implementations of AES and discuss the problem derived from the Hamming distance model and ShiftRow operation of AES. To solve the problem, we propose a new network training method called “mixed model dataset based on round-round XORed value.” We prove that our proposal solves the problem and gives the attack capability to neural networks. We also compare the attack performance and characteristics of DL-SCA to conventional analysis methods such as correlation power analysis and conventional template attack. In our experiment, a dedicated ASIC chip for side-channel analysis is utilized and the chip is also equipped with a side-channel countermeasure AES. We show how DL-SCA can recover secret keys against the side-channel countermeasure circuit. Our results demonstrate that DL-SCA can be a more powerful option against side-channel countermeasure implementations than conventional SCAs.

Multilabel Deep Learning-Based Side-Channel Attack

In recent years, deep learning methods make a big difference in side-channel attack (SCA) community especially in the profiled scenario. Multiclass classification method is the common way to complete such classification task. In this article, we propose a novel SCA method utilizing multilabel classification from bit-to-byte view. Accordingly, each leakage trace has eight labels when considering a byte. The experimental results on several datasets show that our multilabel classification method is efficient and even performs better in some situations compared with the original multiclass classification model while model complexity is much reduced. Besides, our multilabel model can be seen as ensemble of monobit models and we verify the ensemble effect experimentally.

A Countermeasure against DPA on SIMON with an Area-Efficient Structure

Differential power analysis (DPA) is an effective side channel attack method, which poses a critical threat to cryptographic algorithms, especially lightweight ciphers such as SIMON. In this paper, we propose an area-efficient countermeasure against DPA on SIMON based on the power randomization. Firstly, we review and analyze the architecture of SIMON algorithm. Secondly, we prove the threat of DPA attack to SIMON by launching actual DPA attack on SIMON 32/64 circuit. Thirdly, a low-cost power randomization scheme is proposed by combining fault injection with double rate technology, and the corresponding circuit design is implemented. To the best of our knowledge, this is the first scheme that applies the combination of fault injection and double rate technology to the DPA-resistance. Finally, the t-test is used to evaluate the security mechanism of the proposed designs with leakage quantification. Our experimental results show that the proposed design implements DPA-resistance of SIMON algorithm at certain overhead the cost of 47.7% LUTs utilization and 39.6% registers consumption. As compared to threshold implementation and bool mask, the proposed scheme has greater advantages in resource consumption.

Finding the key

Page 1114, ‘Deep learning (DL)-assisted and combined attack: a novel side-channel attack (SCA) ‘, W. Yu, J. Chen Researcher Weize Yu sitting adjacent to the cryptographic circuit setup testing the novel SCA technique. Flowchart illustrating the setup of the cryptographic system and paths to the secret key. Researchers from the Old Dominion University and the University of Minnesota Twin Cities in the US have exploited a deep learning (DL)-assisted and combined side-channel attack (SCA) method to disclose the secret key of an advanced encryption standard (AES) cryptographic circuit. The results demonstrate that, despite standard SCAs being ineffective even after 1 million plaintexts are enabled, the DL-assisted attack method requires only 32,500 plaintexts be enabled to leak the circuits secret key. Cryptographic circuits are the digital hardware implementations of cryptographic algorithms, used in situations which require private data to be processed confidentially and can be used for encrypting processors and Internet of Things (IoT) devices in our daily lives. Cryptographic algorithms are visible to the public while the secret (original) keys are private. Without obtaining the secret key, no one can unravel the relationship between the plaintexts and the ciphertexts for a cryptographic circuit. As a result, if the secret key of a cryptographic circuit is unknown, an adversary is incapable of leaking confidential information by exploring the correlation between the plaintexts (input data) and corresponding physical leakages such as EM noise. AES is the most popular cryptographic algorithm and, with the assistance of the original key, can encrypt plaintexts and decrypt ciphertexts. This encryption protects the data from external attacks, but AES is vulnerable to non-invasive malicious attacks such as SCAs. The teams Letter introduces a novel DL-assisted side-channel attack to obtain the secret key from an AES circuit and is the first work to test this approach. To secure AES cryptographic circuits, countermeasures like dynamic voltage scaling (DVS) and dynamic frequency scaling (DFS) were proposed. These add random noises into physical leakage profiles to weaken the correlation between the processed data and the physical leakages to thwart standard SCAs. To break through this added layer of security, one of the authors Weize Yu notes that “the random noise inserted into different physical leakage profiles are strongly correlated and deep neural networks (DNN) can be utilised to model the correlation to filter the inserted random noises by combing two different physical leakages.” Ultimately, the protected AES cryptographic circuit can be cracked without much effort by a regular side-channel attack after filtering out the inserted random noises. If the DL technique is used to pre-process the physical leakages to enhance the correlation, the protected cryptographic circuit can be cracked easily with only 32,500 plaintexts being sufficient to leak the secret key. This work sets a representative example of combining side-channel analysis and machine learning to strengthen circuit attacks. Previously, side-channel attacks and machine learning attacks were two separate research areas. However, with the drastic development of machine learning, more algorithms have been demonstrated to be suitable for mining unknown information. By continuing to utilise machine learning to assist side-channel analysis, more secret information can be obtained more efficiently than with conventional techniques. The greatest challenge faced by the group was in designing a suitable DNN architecture that can model the relationship between the power noise and the EM emission noise efficiently and accurately. Since there was no solid theory to guide the teams design of the DNN, its features were determined empirically so as to find an appropriate DNN architecture to maximize the training efficiency. This required testing a variety of hidden layer and neuron numbers and analysing the sensitivities on the training error of the DNN, until the optimised results were acquired. The next step in the team's work, according to Yu, is “to demonstrate our novel attack on a cryptographic circuit which employs other countermeasures such as the masking technique.” This adds a random mask of data to the plaintext file generated by an embedded pseudo-random number generator (PRNG). The team will attempt to use the kernel support vector machine (SVM) algorithm to classify the output of the PRNG and the corresponding prediction accuracy achieves over 90%. This area is likely to achieve greater attention in the future given the recent focus on information security and privacy. More effort will be devoted to designing machine learning-resistant cryptographic circuits and these countermeasures may eventually win out. If both side-channel and machine learning attacks need to be considered, the design of more sophisticated cryptographic circuits is inevitable. As a result, designing cryptographic circuits against non-invasive attacks will be an area of key focus in future research.

Similar operation template attack on RSA-CRT as a case study

A template attack, the most powerful side-channel attack methods, usually first builds the leakage profiles from a controlled profiling device, and then uses these profiles to recover the secret of the target device. It is based on the fact that the profiling device shares similar leakage characteristics with the target device. In this study, we focus on the similar operations in a single device and propose a new variant of the template attack, called the similar operation template attack (SOTA). SOTA builds the models on public variables (e.g., input/output) and recovers the values of the secret variables that leak similar to the public variables. SOTAs advantage is that it can avoid the requirement of an additional profiling device. In this study, the proposed SOTA method is applied to a straightforward RSA-CRT implementation. Because the leakage is (almost) the same in similar operations, we reduce the security of RSA-CRT to a hidden multiplier problem (HMP) over ${\rm~GF}(q)$, which can be solved byte-wise using our proposed heuristic algorithm. The effectiveness of our proposed method is verified as an entire prime recovery procedure in a practical leakage scenario.

Distance Based Leakage Alignment for Side Channel Attacks

Side Channel Attack (SCA) recovers secret information from an embedded device with implementation of cryptographic algorithm by exploiting its physical leakages. For most SCA methods to achieve good performance, the measured leakages are often desired to be well aligned. However, due to some specific reasons such as inaccurate measurements or carefully designed countermeasures, misalignment of leakages frequently occurs in practice. Misalignment significantly reduces the efficiency of SCA methods, or even makes them fail. To address this issue, two alignment approaches are proposed: a local alignment based on shotgun distance and a global alignment based on weighted edit distance . Compared with previous methods, the proposed methods are capable of keeping the secret dependant leakages, while not introducing any redundant information. In addition, the proposed methods could also reduce the negative effects of noise, which is another factor seriously decreasing the efficiency of SCA methods. Interestingly, it is pretty easy to set appropriate parameters for these two methods. Practical experiments show that the proposed methods outperform previous methods in three different circumstances and different noise levels.

Practical Electromagnetic Disturbance Analysis on Commercial Contactless Smartcards

Contactless smart cards are being widely employed in electronic passports, monetary payments, access control systems, and so forth, because of their advantages such as convenience and ease of maintenance. In this paper, we present a new side-channel attack method for contactless smart cards. This method exploits the information leakage stemming from electromagnetic disturbances (EMD). We also made a convenient and low-cost EMD reader board that performs side-channel attacks on contactless smart cards. In order to demonstrate that EMDs can become another information-leakage side channel, we have carried out side-channel analysis on a commercial contactless smart card that performs 128-bit ARIA encryptions, and we have been able to successfully find all 16 bytes of the ARIA key from the target device. From our experimental results, we conclude that the proposed EMD analysis yields better results than the conventional power analysis.

Scan-Based Attack against Trivium Stream Cipher Using Scan Signatures

Trivium is a synchronous stream cipher using three shift registers. It is designed to have a simple structure and runs at high speed. A scan-based side-channel attack retrieves secret information using scan chains, one of design-for-test techniques. In this paper, a scan-based side-channel attack method against Trivium using scan signatures is proposed. In our method, we reconstruct a previous internal state in Trivium one by one from the internal state just when a ciphertext is generated. When we retrieve the internal state, we focus on a particular 1-bit position in a collection of scan chains and then we can attack Trivium even if the scan chain includes other registers than internal state registers in Trivium. Experimental results show that our proposed method successfully retrieves a plaintext from a ciphertext generated by Trivium.

Scan-Based Attack on AES through Round Registers and Its Countermeasure

Scan-based side channel attack on hardware implementations of cryptographic algorithms has shown its great security threat. Unlike existing scan-based attacks, in our work we observed that instead of the secret-related-registers, some non-secret registers also carry the potential of being misused to help a hacker to retrieve secret keys. In this paper, we first present a scan-based side channel attack method on AES by making use of the round counter registers, which are not paid attention to in previous works, to show the potential security threat in designs with scan chains. And then we discussed the issues of secure DFT requirements and proposed a secure scan scheme to preserve all the advantages and simplicities of traditional scan test, while significantly improve the security with ignorable design overhead, for crypto hardware implementations.

First-order side channel attacks on Zhang’s countermeasures

Zhang’s three countermeasures are known to be secure against certain first-order side channel attacks such as differential power analysis and correlation power analysis. This security comes from the countermeasures’ use of random points to blind the message and random integers to blind the secret scalar. In this paper, we propose first-order side channel attack methods that can perfectly break these three countermeasures. Even though Zhang’s countermeasures use random points and random integers our attacks are made possible by the fact that intermediate values computed by these countermeasures are dependent on specific values that we can guess. The experimental results verify that the proposed attack methods can successfully break existing countermeasures.