Side-channel Data Research Articles

CPSGD: A Novel Optimization Algorithm and Its Application in Side-Channel Analysis

In recent years, side-channel analysis based on deep learning has garnered significant attention from researchers. A pivotal reason for this lies in the fact that deep learning-based side-channel analysis requires minimal preprocessing of side-channel data. The automatic feature extraction property of deep learning methods drastically reduces the workload for researchers, enabling them to focus more on the core issues of side-channel analysis, namely, extracting sensitive information by attacking devices. However, in prior studies, most scholars have concentrated more on the model construction process, with little research focusing on the choice of optimizers.This paper explores a novel deep learning-based optimization algorithm—CPSGD (combined projection stochastic gradient descent). The algorithm comprises two variants, designed, respectively, for unprotected side-channel analysis (CPSGD1) and desynchronized side-channel analysis (CPSGD2), and their convergence has been theoretically proven. Experimental results demonstrate that, while maintaining the neural network structure unchanged, CPSGD1 exhibits the best performance on unprotected datasets compared to other publicly available optimizers, whereas CPSGD2 performs optimally on desynchronized datasets.

Design of Deep Learning Techniques for Side-Channel Attacks on Masked 128-bit AES Implementations

Researchers are exploring the use of convolutional neural networks (CNNs) in side-channel attacks to understand the weaknesses in cryptographic implementation. CNNs can learn hierarchical characteristics automatically from electromagnetic radiation or power usage during cryptographic processes. Researchers train CNNs on side-channel data to extract meaningful representations and deduce secret keys. Deep learning algorithms are helpful in evaluating the security of embedded systems, and CNNs are a feasible paradigm for profiling side-channel analysis attacks. In this paper, it has been introduced a VGG (Visual Geometry Group)-Net architecture, which is a typical deep convolutional neural network design with numerous layers. It uses the ASCAD dataset to conduct experiments. They found that VGG-Net architecture Side Channel Attacks (SCA) provides better results than the previously optimized CNN model by significantly reducing the number of side-channel traces required for successful attacks on desynchronized datasets. The researchers also discovered that synchronous traces serve as the pre-training source for VGG-Net architecture, functioning successfully in terms of jittering with minimal fine-adjusting after training

Detection method of Golden Chip-Free Hardware Trojan based on the combination of ResNeXt structure and attention mechanism

Since 2007, the use of side-channel data to detect hardware Trojans (HT) has been widely studied. Machine learning methods are widely used in the detection of hardware Trojans, but with the development of integrated circuits (IC), machine learning methods are not able to obtain a higher accuracy rate compared to deep learning. In this paper, we propose to use an architecture inspired by the ResNeXt network architecture and combine it with an attention mechanism, referred to as the Attention-Res-Attention (ARA) network. Firstly, the side channel data are extracted by convolutional layer with features and focus on important points under the attention module; then, the feature map enters the ResNeXt architecture that achieves classification accuracy improvement by adding the attention module; finally, the data are classified by the fully connected layer. Our proposed solution is observable to natural variations that may occur in side-channel measurements, and can accurately detect abnormal behavior of the chip when HT is triggered. And using a self-referential method for HT detection eliminates the need for a golden chip. The effectiveness of the method proposed in this paper is evaluated based on the AES series hardware Trojans publicly provided by TrustHub. Experimental results show that the method proposed in this paper has a high accuracy rate when a single Trojan exists, and can effectively detect the existence of hardware Trojans. And when a variety of hardware Trojans exist at the same time, the method used in this paper can effectively distinguish the types of hardware Trojans, and the highest average accuracy rate reached 97% during the experiment. Compared with the existing deep learning methods, the network model in this paper has higher classification accuracy.

Hardware Trojan Detection Method Based on Dual Discriminator Assisted Conditional Generation Adversarial Network

Hardware Trojans are usually implanted by making malicious changes to a chip circuit, which can destroy chip functions or expose sensitive information once activated. The hardware Trojan detection method based on side channel information has now become one of the most widely used detection methods. However, due to the influence of the deviation of the acquisition equipment and the noise of the actual chip working environment, insufficient acquisition of useful information of the collected side channel information occurs, affecting the final results. To address the problem, this paper proposes a detection method based on a dual discriminator assisted conditional generation adversarial network (D2ACGAN), which combines the benefits of CGAN, ACGAN, and D2GAN models and can learn a variety of valid information of the tested chip. It can distinguish between side channel data with and without hardware Trojan and classify hardware Trojan using the extended data. Furthermore, to compare the performance of the proposed model, we use the existing CGAN and ACGAN models equally for side channel information expansion and hardware Trojan detection. Finally, the designed hardware Trojan is implanted in an encryption chip for generating data quality evaluation experiments and model method performance experiments. The results show that the average detection accuracy of the D2ACGAN-based hardware Trojan classification model can reach 97.08%, which is better than the detection models based on CNN, SVM, etc. The D2ACGAN model also outperforms the CGAN and ACGAN models in terms of generated data and hardware Trojan classification.

XAI-Based Microarchitectural Side-Channel Analysis for Website Fingerprinting Attacks and Defenses

Website Fingerprinting attacks aim to track the visited websites in browsers and infer confidential information about users. Several studies showed that recent advancements in Machine Learning (ML) and Deep Learning (DL) algorithms made it possible to implement website fingerprinting attacks even though various defense techniques are present in the network. Nevertheless, trained models for website detection are not analyzed deeply to identify the leakage sources which are not always visible to both attackers and Cyber Threat Intelligence engineers. This study focuses on explaining ML and DL models in the context of microarchitecture-based website fingerprinting attacks. In the attack model, performance counters and cache occupancy side-channels are implemented on Google Chrome and Tor browsers. After ML and DL models are trained, LIME and saliency map XAI methods are applied to examine the leakage points in the side-channel data. In order to match the leakage samples in the measurements to the network traces, a novel dataset is collected by utilizing Google Chrome and Firefox browser developer tools. Next, the efficiency of explainable methods are analyzed with XAI metrics. Finally, an XAI-based obfuscation defense technique is proposed as a countermeasure against microarchitecture-based website fingerprinting attacks

Side-Channel Security Analysis of Connected Vehicle Communications Using Hidden Markov Models

This paper investigates side-channel vulnerabilities of a wireless communication application in vehicular environments (DSRC/WAVE) protocol implementation of a traffic intersection application. A prototype roadside unit (RSU) was implemented using real DSRC devices. The functionality of the WAVE short message (Wsm)-channel is extended to include an implementation of WAVE short message protocol (WSMP) for broadcasting GPS data and RSU instructions in vehicular communications. In the example used, DSRC is used to replace an intersection stoplight. Denial of service attacks are executed that leverage DSRC RSU timing and packet size side-channels to selectively disable the stoplight. Simulations are implemented to determine our ability to stealthily drop packets so as to force two vehicles to collide. Hidden Markov models (HMM) and Support Vector Machines (SVM) are constructed from sniffed side-channel information. We use inter-packet delay time and packet size side-channel information to design our attackes. In operational networks, packets should be encrypted in order to hide the contents of the packet payloads, but packet sizes and timing are not affected by encryption. HMMs were inferred using only side-channel information. The inferred HMMs track the protocol status over time. The SVM classifier was inferred using both side-channel data and packet payloads. At run-time, though, the SVM only had access to side-channel information. Simulation experiments were implemented to test HMM and SVM ability to identify packets used to signal vehicles to stop and yield right-of-way. Timing HMM side-channel attack caused collision with 2.5% false positive rate (FPR), while the packet size one resulted 9.5% FPR.

Side-Channel Leakage Detection with One-Way Analysis of Variance

Side-channel analysis (SCA) is usually used for security evaluation to test the side-channel vulnerability of a cryptographic device. However, in practice, an analyser may need to cope with enormous amounts of side-channel measurement data to extract valuable information for SCA. Under the circumstances, side-channel leakage detection can be used to identify leakage points which contain secret information and therefore improve the efficiency of security assessment. This investigation proposes a new black-box leakage detection approach on the basis of the one-way analysis of variance (ANOVA). In accordance with the relevance between leakage points and inputs of a cryptographic algorithm, the proposed method divides side-channel samples into multiple classes and tests the difference among these classes by taking advantage of the one-way ANOVA. Afterwards, leakage points and nonleakage points can be distinguished by determining whether the null hypothesis is accepted. Further, we extend our proposed method to multichannel leakage detection. In particular, a new SCA attack with a F -statistic-based distinguisher is capable of developing if the input of the leakage detection approach is replaced by a sensitive intermediate variable. Practical experiments show the effectiveness of the proposed methods.

CONDENSE: A Moving Target Defense Approach for Mitigating Cache Side-Channel Attacks

Common consumer electronic devices (including smartphones, smart TVs, personal computers, etc.) feature increasingly complex processors for the required computations. These processors typically feature caches, which substantially improve the devices’ performance, but may also be prone to side-channel attacks, wherein an attacker measures and analyzes the side-channel data in order to deduce private information. In this article, we explore CONDENSE: Configurability-Driven Security as a viable low-overhead technique for mitigating security vulnerabilities in caches. CONDENSE takes advantage of runtime reconfigurable caches to provide a moving target defense against side-channel attacks, while also optimizing the cache's energy consumption. Experimental results reveal that employing cache configurability as a defense mechanism is promising and warrants further studies.

Cutting Through the Emissions: Feature Selection from Electromagnetic Side-Channel Data for Activity Detection

Electromagnetic side-channel analysis (EM-SCA) has been used as a window to eavesdrop on computing devices for information security purposes. It has recently been proposed to use as a digital evidence acquisition method in forensic investigation scenarios as well. The massive amount of data produced by EM signal acquisition devices makes it difficult to process in real-time making on-site EM-SCA infeasible. Uncertainty surrounds the precise information leaking frequency channel demanding the acquisition of signals over a wide bandwidth. As a consequence, investigators are left with a large number of potential frequency channels to be inspected; with many not containing any useful information leakages. The identification of a small subset of frequency channels that leak a sufficient amount of information can significantly boost the performance enabling real-time analysis. This work presents a systematic methodology to identify information leaking frequency channels from high dimensional EM data with the help of multiple filtering techniques and machine learning algorithms. The evaluations show that it is possible to narrow down the number of frequency channels from over 20,000 to less than a hundred (81 channels). The experiments presented show an accuracy of 0.9315 when all the 20,000 channels are used, an accuracy of 0.9395 with the highest 500 channels after calculating the variance between the average value of each class, and an accuracy of 0.9047 when the best 81 channels according to Recursive Feature Elimination are considered.

Secondary Side-Channel Wireline Communication Using Transmitter Clock Frequency Modulation

A secondary communication link, or side-channel, is proposed, which uses binary frequency shift keying to modulate the frequency of a high-speed wireline transmitter clock. This side-channel data is then received using the corresponding receiver’s conventional clock and data recovery (CDR) circuit. An analysis of the CDR loop parameters demonstrates that, within certain limits, the side-channel does not impact the signal integrity of the primary high-speed data. Measurements were made on a side-channel prototype using a 56-Gb/s PAM-4 (half-rate 14-GHz clock) transceiver in 7-nm FinFET technology through a 15-dB loss channel. Over 104 side-channel bits were transmitted at 50 kb/s, and the side-channel remained error-free with no visible impact on the high-speed link.

Secure and duplication detection in cloud using cryptographic hashing method

De-duplication systems are adopting De-duplication strategies such as client side or server side De-duplication.Particularly, in the beginning of cloud storage, data De-duplication techniques happen to add importance to store original volumes of data in the cloud. This technique motivates the enterprise and organization to farm out data storage to cloud service providers, as proof of several case studies was done.Block level de-duplication is used to discover, removes redundancies compare with previously stored information. The file will be separated into smaller segment as given by the system size or uneven size blocks or chunks whatever we need. Using predetermined size of blocks, the system can simplify the computations of block limits, although using uneven size blocks provides improved de-duplication. Secure Cloud introduce a new concept to audit entities with continuation of a Map Reduce cloud, which is used to help the client is easy to make data tags before feeding the data and audit with theprobity of data is analysed and stored in the cloud. the subject of previously finished work is fixed so that the computational load at user or auditor has huge amount of tag making. In accumulation, Secure Cloud also enables secure de-duplication. Perceive that the “validity” measured in Secure Cloud is the deterrence of leakage, the side channel information. In order to check the leakage of such side channel data, the work accept the custom and design a proof of rights protocol amid cloud servers and clients, which permit clients to confirm the cloud servers what they exactly own the object information.

Robust Smartphone App Identification via Encrypted Network Traffic Analysis

The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. In addition, the presence or absence of particular apps on a smartphone can inform an adversary, who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data, such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices, and across different versions of apps. In addition, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps, such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions.

Side-channel attacks and learning-vector quantization

The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. Side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.

Your WiFi is leaking: What do your mobile apps gossip about you?

This paper describes how mobile device apps can inadvertently broadcast personal information through their use of wireless networks despite the correct use of encryption. Using a selection of personas we illustrate how app usage can be tied to personal information. Users would likely assume the confidentiality of personal information (including age, religion, sexuality and gender) when using an encrypted network. However, we demonstrate how encrypted traffic pattern analysis can allow a remote observer to infer potentially sensitive data passively and undetectably without any network credentials.Without the ability to read encrypted WiFi traffic directly, we process the limited side-channel data available (timings and frame sizes) to enable remote app detection. These side-channel data measurements are represented as histograms and used to construct a Random Forest classifier capable of accurately identifying mobile apps from the encrypted traffic they cause. The Random Forest algorithm was able to correctly identify apps with a mean accuracy of ∼99% within the training set.The classifier was then adapted to form the core of a detection program that could monitor multiple devices in real-time. Tests in a closed-world scenario showed 84% accuracy and demonstrated the ability to overcome the data limitations imposed by WiFi encryption. Although accuracy suffers greatly (67%) when moving to an open-world scenario, a high recall rate of 86% demonstrates that apps can unwittingly broadcast personal information openly despite using encrypted WiFi. The open-world false positive rate (38% overall, or 72% for unseen activity alone) leaves much room for improvement but the experiment demonstrates a plausible threat nevertheless.Finally, avenues for improvement and the limitations of this approach are identified. We discuss potential applications, strategies to prevent these leaks, and consider the effort required for an observer to present a practical privacy threat to the everyday WiFi user. This paper presents and demonstrates a nuanced and difficult to solve privacy vulnerability that cannot not be mitigated without considerable changes to current- and next-generation wireless communication protocols.

Side-channel cryptographic attacks using pseudo-boolean optimization

Symmetric block ciphers, such as the Advanced Encryption Standard (AES), are deterministic algorithms which transform plaintexts to ciphertexts using a secret key. These ciphers are designed such that it is computationally very difficult to recover the secret key if only pairs of plaintexts and ciphertexts are provided to the attacker. Constraint solvers have recently been suggested as a way of recovering the secret keys of symmetric block ciphers. To carry out such an attack, the attacker provides the solver with a set of equations describing the mathematical relationship between a known plaintext and a known ciphertext, and then attempts to solve for the unknown secret key. This approach is known to be intractable against AES unless side-channel data --- information leaked from the cryptographic device due to its internal physical structure --- is introduced into the equation set. A significant challenge in writing equations representing side-channel data is measurement noise. In this work we show how casting the problem as a pseudo-Boolean optimization instance provides an efficient and effective way of tolerating this noise. We describe a theoretical analysis, connecting the measurement signal-to-noise ratio and the tolerable set size of a non-optimizing solver with the success probability. We then conduct an extensive performance evaluation, comparing two optimizing variants for dealing with measurement noise to a non-optimizing method. Our best optimizing method provides a successful attack on the AES cipher which requires surprisingly little side-channel data and works in reasonable computation time. We also make available a set of AES cryptanalysis instances and provide some practical feedback on our experience of using open-source constraint solvers.

Differential Electromagnetic Attacks on a 32-bit Microprocessor Using Software Defined Radios

Side-channel analysis has been used to successfully attack many cryptographic systems. However, to improve trace quality and make collection of side-channel data easier, the attacker typically modifies the target device to add a trigger signal. This trigger implies a very powerful attacker with virtually complete control over the device. This paper describes a method to collect side-channel data using a software defined radio (SDR) in real-time without requiring a collection device trigger. A correlation-based frequency-dependent leakage mapping technique is introduced to evaluate a 32-bit microprocessor, revealing that individual key bytes leak at different frequencies. Key byte-dependent leakage is observed in both SDR collected and triggered oscilloscope-based collections (which serve to validate the SDR data). This research is the first to demonstrate effective differential attack using SDRs. Successful attacks are presented using two SDRs, including a US$20 digital television receiver with modified drivers.