Side-channel Research Articles

Hardware/Software Cooperative Design Against Power Side-Channel Attacks on IoT Devices

Hardware/Software Cooperative Design Against Power Side-Channel Attacks on IoT Devices

Recovery of the secret on Binary Ring-LWE problem using random known bits - Extended Version

There are cryptographic systems that are secure against attacks by both quantum and classical computers. Some of these systems are based on the Binary Ring-LWE problem which is presumed to be difficult to solve even on a quantum computer. This problem is considered secure for IoT (Internet of things) devices with limited resources. In Binary Ring-LWE, a polynomial a is selected randomly and a polynomial b is calculated as b = a.s + e where the secret s and the noise e are polynomials with binary coefficients. The polynomials b and a are public and the secret s is hard to find. However, there are Side Channel Attacks that can be applied to retrieve some coefficients (random known bits) of s and e. In this work, we analyze that the secret s can be retrieved successfully having at least 50% of random known bits of s and e.

Analysis of Road Damage Level Using the Pavement Condition Index (PCI) Method on the Surabaya-Gresik Toll Road, East Java

The Surabaya–Gresik toll road is a crucial infrastructure connecting Surabaya City with Gresik Regency. In industrial areas such as Gresik Regency, this toll road is indeed needed to expedite the flow of industrial goods distribution to Surabaya City. The toll road experiences frequent damage, such as alligator cracking, as it is traversed daily by heavy traffic. If the road, as a connecting infrastructure, is damaged, it will impede the flow of vehicles passing through it. Therefore, road maintenance is necessary to ensure optimal service for passing vehicles. In this research, an analysis of road damage on the Surabaya – Gresik toll road seeks solutions for addressing these issues utilizing the method of Pavement Condition Index (PCI). The method involves several stages, including a survey of road conditions, determination of severity levels, assessment of damage, and determination of pavement condition. The survey was conducted in 100-meter segments from KM 10 to KM 14, revealing various types of damage on the toll road, including alligator cracking, bleeding, corrugation, shoving, potholes, and more. Thereafter, an analysis of the severity level and damage assessment was carried out, resulting in an average PCI value of 68, categorized as a "Good" condition. Based on these findings, it is recommended to address this issue through routine maintenance, including repairing minor damage, patching potholes, sweeping, repairing pavement edge damage, sidewalk maintenance, side channel and complementary building drainage, road equipment, and roadside maintenance.

PETRAK: A solution against DDoS attacks in vehicular networks

In recent years, the frequently reported incidents of Distributed Denial of Service assaults on vehicular networks in various countries have made researchers find new protective solutions. DDoS attacks can propagate through the charging points for electric vehicles in a charging station and affect the production of critical infrastructures such as electric grids. Existing solutions are efficient in attack detection; however, current systems do not offer multi-level protection, and zero-day vulnerabilities are prone to escape from the detection systems.In this paper, we address the problems mentioned above and introduce the first Machine Learning (ML)–based DDoS protective solution to combine prevention and detection mechanisms in vehicular networks. To be more specific, our proposed model is the first to consider the adaptive traffic threshold to generate the alarm for a suspicious amount of traffic flow in an Intrusion Detection Prevention System (IDPS). We call our proposed approach Protecting vEhicular neTworks against distRibuted deniAl of service attacKs (PETRAK). PETRAK uses four functions: prevention, alarm, training, and detection. The alarming system uses the flow parameters and activates the detection module to detect malicious packets. The prevention system works in two modes: immediate and future. PETRAK uses logistic regression to identify incoming packets and signatures of malicious packets to prevent future attacks. We also show that our proposed model is implacable towards advanced post-quantum cryptography-based traffic and also able to analyse side-channel attacks. We run a comprehensive set of experiments to test PETRAK on our synthesized dataset, KDDCUP’99 dataset, CIC-MalMem-2022, and the ToN-IoT dataset. We observe that PETRAK shows an accuracy of 99%. The results claim the efficiency of PETRAK in detecting and preventing DDoS attacks in vehicular networks.

Efficient Separation of Methanol Single-Micron Droplets by Tailing Phenomenon Using a PDMS Microfluidic Device.

Microdroplet-based fluidic systems have the advantages of small size, short diffusion time, and no cross-contamination; consequently, droplets often provide a fast and precise reaction environment as well as an analytical environment for individual molecules. In order to handle diverse reactions, we developed a method to create organic single-micron droplets (S-MDs) smaller than 5 μm in diameter dispersed in silicone oil without surfactant. The S-MD generation microflow device consists of a mother droplet (MoD) generator and a tapered separation channel featuring multiple side channels. The tapered channel enhanced the shear forces to form tails from the MoDs, causing them to break up. Surface treatment with the fluoropolymer CYTOP protected PDMS fluid devices from organic fluids. The tailing separation of methanol droplets was accomplished without the use of surfactants. The generation of tiny organic droplets may offer new insights into chemical separation and help study the scaling effects of various chemical reactions.

A deep learning based solution for data disproportionproblem in side channel attacks using intelligent sensors

Recently, Deep learning (DL) based Side Channel Attacks (SCAs) has been emerged as new paradigm in which the cryptographic devices are attacked through the side channel information. SCAs use external characteristics like power consumption, electromagnetic radiation, sound etc. of cryptographic devices to attack and estimate the secret key. However, the accomplishment of Deep learning for SCAs has not been fully analyzed especially at the data used to train and test. The major problem observed for DL based SCAs are Data Disproportionation Problem (DDP) using Intelligent Sensors which results in low success rate. Methods like data augmentation are used to make the data proportionate, but they resulted in poor accuracy because the original data will get disturbed. Hence, this paper proposed an ew solution to solve DDP without affecting the original data distribution. Unlike the traditional methods which predict the secret key based on Hamming Weight based likelihood function, the proposed solution uses Key value based likelihood function. We explore the validity of proposed solution through extensive simulations over the standard and public ASCAD dataset. The obtained results prove the superiority of proposed solution from the state-of-the-art methods.

Simple Method for On-Demand Droplet Trapping in a Microfluidic Device Based on the Concept of Hydrodynamic Resistance.

We demonstrate an innovative method to catch the desired droplets from a train of droplets and immobilize them in traps located in an integrated microfluidic device. To this end, water-in-oil droplets are generated in a flow-focusing junction and then guided to a channel connected to chambers designated for on-demand droplet trapping. Each chamber is connected to a side channel through a batch of microposts. The side channels are also connected to the flexible poly(vinyl chloride) tubes, which can be closed by attaching binder clips. The hydrodynamic resistance of the routes in the device can be changed by opening and closing the binder clips. In this way, droplets are easily guided into individual traps based on the user's demand. A set of numerical simulations was also conducted to investigate the authenticity of the employed idea and to find the optimal geometry for implementing our strategy. This simple method can be easily employed for on-demand droplet trapping without using on-chip valves or complex off-chip actuators proposed in previous studies.

Active Learning for Detecting Hardware Sensors-Based Side-Channel Attack on Smartphone

Active Learning for Detecting Hardware Sensors-Based Side-Channel Attack on Smartphone

Revealing IoT Cryptographic Settings through Electromagnetic Side-Channel Analysis

The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become more robust with longer key lengths, they provide higher levels of security. However, non-invasive side channels, specifically through electromagnetic (EM) emanations, can expose confidential cryptographic details, thus presenting a novel solution to the pressing forensic challenge. This research delves into the capabilities of EM side-channel analysis (EM-SCA), specifically focusing on detecting both cryptographic key lengths and the algorithms employed utilizing a machine-learning-based approach, which can be instrumental for digital forensic experts during their investigations. Through meticulous data processing and analysis, the Support Vector Machine (SVM) model, among others, demonstrated a notable accuracy of 94.55% in distinguishing between AES and ECC cryptographic operations. This capability significantly enhances digital forensic methodologies, offering a novel avenue for noninvasively uncovering encrypted data’s cryptographic settings. By identifying key lengths and algorithms without invasive procedures, this research contributes substantially to the advancement of forensic investigations in encrypted environments.

Investigating CRYSTALS-Kyber Vulnerabilities: Attack Analysis and Mitigation

Significant advancements have been achieved in the field of quantum computing in recent years. If somebody ever creates a sufficiently strong quantum computer, many of the public-key cryptosystems in use today might be compromised. Kyber is a post-quantum encryption technique that depends on lattice problem hardness, and it was recently standardized. Despite extensive testing by the National Institute of Standards and Technology (NIST), new investigations have demonstrated the effectiveness of CRYSTALS-Kyber attacks and their applicability in non-controlled environments. We investigated CRYSTALS-Kyber’s susceptibility to side-channel attacks. In the reference implementation of Kyber512, additional functions can be compromised by employing the selected ciphertext. The implementation of the selected ciphertext allows the attacks to succeed. Real-time recovery of the entire secret key is possible for all assaults.

Side-Channel Attacks Against the FESH Algorithm

The FESH algorithm is a block cipher algorithm based on finite field operations. Currently, no research has been conducted on its side-channel attack security. Therefore, this study proposes two methods to address this issue: a correlation power analysis attack method targeting the FESH algorithm, and a template attack method based on an improved TransNet model. The first method theoretically analyzed the vulnerabilities of the FESH algorithm and successfully obtained valid leaked information through a correlation power attack; The second method introduced BlurPool blurring and downsampling techniques, as well as normalization operations, which reduced the training parameters of the improved model by approximately 50%. Additionally, the validation was performed on both the FESH dataset and the desynchronized ASCAD public dataset, which provided evidence that the entropy estimates were significantly better than those of the original TransNet model. The experimental results highlight the importance of considering side-channel security when implementing the FESH algorithm.

Sedimentary evolutionary processes, architecture, and sedimentary model of a lobate shallow-water delta: insights from flume simulation experiments

By using equipment such as time-lapse cameras and 3D laser scanners, this study conducted flume simulation experiments to obtain topographic elevation, the amount of sediment increment, sedimentary cross sections, and other related data and diagrams. Quantitative software was then utilized for sedimentological analysis to clarify the depositional evolution, architecture, and depositional model of lobate shallow-water deltas. The research results show that under the influence of hydraulic conditions and main distributary channels, lobate shallow-water deltas primarily undergo three evolution stages: initial channel formation, distributary channel system formation, and continuous stable evolution stage. The architecture elements of lobate shallow-water deltas can be divided into distributary channels dominated by channel deposition, proximal river mouth bars, transitional bars, and distal bars. The lateral composition and downstream deposition of the river-bar combination are the main structural units of the shallow water delta. The proximal profile distributary channels exist in the form of thick channel sand bodies with multiple repetitive intercalation cycles, and thick proximal bar deposits form on both sides of the distributary channels. The bifurcation of distributary channels in the middle profile leads to a reduction in channel scale, while large-scale proximal bars still develop on both sides of the channel. In the distal profile, a substantial river mouth bar complex is formed at the break of the delta front slope, and with the gradual progradation of the delta, the river mouth bar deposition zone becomes flattened. Based on the insights of the flume simulation experiments, a sedimentary model for lobate shallow-water delta was established.

Timing Side-channel Attacks and Countermeasures in CPU Microarchitectures

Microarchitectural vulnerabilities, such as Meltdown and Spectre, exploit subtle microarchitecture state to steal the user’s secret data and even compromise the operating systems. In recent years, considerable discussion lies in understanding the attack-defense mechanisms and exploitability of such vulnerabilities. Unfortunately, there have been few investigations into a systematic elaboration of threat models, attack scenarios and requirements, and defense targets of the resulting attacks. In this article, we fill this gap and make the following contributions. We first propose two sets of taxonomies for classifying microarchitectural timing side-channel attacks and their countermeasures according to various attack conditions. Based on the taxonomies proposed, we then review published attacks and existing defenses and systematically analyze their internals. In particular, we also provide a comprehensive analysis of the similarities and differences among those attacks, uncovering the corresponding practicality and severity by identifying the attack targets/platforms and the security boundaries that can be bypassed to reveal information. We further examine the scalability of those defenses through specifying expected defense goals and costs. We also discuss corresponding detection methods based on different classifications. Finally, we propose several key challenges of existing countermeasures and the attack trends, and discuss directions for future research.

Survey: Recovering cryptographic keys from partial information, by example

Side-channel attacks targeting cryptography may leak only partial or indirect information about the secret keys. There are a variety of techniques in the literature for recovering secret keys from partial information. In this work, we survey several of the main families of partial key recovery algorithms for RSA, (EC)DSA, and (elliptic curve) Diffie-Hellman, the classical public-key cryptosystems in common use today. We categorize the known techniques by the structure of the information that is learned by the attacker, and give simplified examples for each technique to illustrate the underlying ideas.

Scoring the predictions: a way to improve profiling side-channel attacks

Scoring the predictions: a way to improve profiling side-channel attacks

A Secure VM Placement Strategy to Defend against Co-residence Attack in Cloud Datacentres

With the increasing number of co-residence attacks, the security of the multi-tenant public IaaS cloud environment has become a growing concern. The co-residence attacker creates a side channel to retrieve the secured data. These attacks help the adversary to leak out the sensitive information of the user with whom it is co-located. This paper discusses a secured VM placement technique, Previous Server and Co-resident users First (PSCF), which focuses on facilitating security against the co-residence attack by minimizing the probability of co-locating the malicious user with the authentic user. Co-location resistance and core utilization metrics are utilized to evaluate the algorithm’s performance. The proposed method is simulated, and the result is analysed and compared with existing approaches like Best Fit, Worst Fit, PSSF, and SC-PSSF. It is observed that the proposed approach furnished maximum co-location resistance of 74.32% and a core utilization of 82.63%. Further, the algorithm has shown significant performance in balancing the load and energy consumption. The result has reduced the probability that malicious users co-located with the authentic one, thus reducing the security breach of confidential information.

Abstract LB437: Development of a bladder cancer-on-a-chip assay to assess the invasive potential of bladder cancer cells

Abstract Goals: The primary goal of this study was to develop a 3D functional bioassay to assess the relative invasiveness of bladder cancer cells. This model will support the testing of migrastatic drugs and investigation of the molecular mechanisms which allow for bladder cancer cell invasion to occur. Methods: Our model uses an organ-on-a-chip platform to assess bladder cancer cell invasiveness. This platform supports 3D culture of cells as well as pharmacokinetic studies. Our chips were designed to have a center channel to hold cells supported in Matrigel®, 2 adjacent side channels filled with Matrigel® only, and 2 outer media channels. Total cell and Matrigel® side channel length is 3 cm, channel height is 150 µm, and each channel width is 1 mm. Four bladder cancer cell lines (MB49, T24, J82, RT4) and cells isolated from a mouse bladder were used to develop our model. Cells were mixed with 1:1 media and Matrigel® and a total of 5,000 cells/µl introduced into the center channel. The 2 adjacent side channels were then filled with Matrigel®. The upper media channel was filled with 10% FBS cell culture media while the lower media channel was filled with 1% charcoal stripped cell culture media to thereby create a nutrient and growth factor gradient. Invasion of cells into the Matrigel®-containing side channels was assessed every 24 hours. Using the ImageXpress machine (Molecular Dynamics), we measured the furthest distance travelled by the cells. The chips were then fixed in 4% paraformaldehyde and stained using FITC-phalloidin and DAPI. Results: J82 and T24 cell lines, derived from patients with muscle invasive bladder cancer, and the MB49 cell line, a highly invasive mouse bladder cancer cell line, started invading into the Matrigel® side channels within 24 hours. T24, J82, and MB49 cells moved a maximum of 110, 87, and 170 µm by 48 hours, respectively. In contrast, RT4 cells, derived from a patient with non-invasive bladder cancer, took 48 hours to invade the Matrigel® channels and only invaded a maximum of 3 µm. Cells isolated from a mouse bladder did not invade into the Matrigel® side channels but survived within the chips for over 20 days. Staining of chips with DAPI and FITC-phalloidin allowed for clear visualization of cells and the actin cytoskeleton and demonstrated that immunostaining of cells within chips is possible. Conclusions: Our chip model allows for studying the invasiveness of bladder cancer cells and immunostaining experiments which can be used to assess the molecular mechanisms which drive invasion. It can also support the growth and assessment of primary cells. This model has the potential to improve bladder cancer patient treatments and outcomes by providing a physiologically relevant system to test new and existing drug combinations and to better understand the molecular mechanisms which drive bladder cancer cell invasion. Citation Format: Desiree Ewell, Nita Vue, Sakib Moinuddin, Tanoy Sarkar, Fakhrul Ahsan, Ruth L. Vinall. Development of a bladder cancer-on-a-chip assay to assess the invasive potential of bladder cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB437.

A new chaotic system and its practical applications in substitution box and random number generator

AbstractOne of the successful practical applications of chaos theory and nonlinear dynamics is chaos-based cryptology studies. In this study, a new chaotic system is proposed. The proposed chaotic system generator model has a simple algorithmic structure. In addition to, generated chaotic systems have rich dynamics. It has been examined whether this system has potential advantages, especially for information security applications. As a result of the analysis and experimental studies, it is seen that the study makes many contributions to the literature. The simple mathematical generator structure has the potential to reduce computational complexity, which is an important problem for cryptology applications. One of the most comprehensive studies has been realized for chaos-based substitution box designs. 70,000 different substitution box structures have been generated. It has been known that the highest nonlinearity value that can be achieved for designs that transform chaotic system outputs into substitution box structures is 106.75 today. It has been shown that this value may be higher. The new highest nonlinearity value is calculated as 107. Side channel analysis has been examined for substitution box structures with the highest nonlinearity value among the substitution boxes generated in the study. Analysis results show that the proposed new substitution box structures may have an advantage for application-oriented attacks. Among the generated substitution box structures, 289 substitution box structures with a nonlinearity value of 106 and above are presented as a public dataset for researchers to use. In addition, 3 different random sequences with a length of 1 million-bit were produced with the proposed map, and the NIST SP 800–22 test was applied. Test results for all datasets were successful. In this way, the proposed map can also be used as a random number generator.

A Data Augmentation Method for Side-Channel Attacks on Cryptographic Integrated Circuits

The leaked signals, including electromagnetic, power, timing, and temperature generated during the operation of cryptographic devices, contain highly correlated key value information, leading to security vulnerabilities. In practical operations, due to information collection conditions and time limitations, attackers can only obtain limited valid data. At the same time, the attacker’s data environment cannot be ideal, and noise can affect the acquisition of valid information. Therefore, to improve the effectiveness of obtaining key values from side-channel information analysis in cryptographic devices, we proposed a data augmentation method based on cycle-consistent generative adversarial networks named EME-CycleGAN. By using generators and discriminators, new data are generated to expand the original electromagnetic information dataset, aiming for better modeling effects. For evaluating the data augmentation effect on side-channel electromagnetic signals, we employed the Kolmogorov–Smirnov test to characterize the original and generated data, serving as the evaluation standard for our network model and work. We utilize the existing data to model and verify side-channel attacks, evaluating the impact of the generated information on the overall experimental results. The proposed structure consists of three main parts: side-channel information acquisition, data verification analysis, and determination of attack positions. Experimental results demonstrate that effective attacks on encryption algorithms can be achieved under small-sample dataset conditions.

PowerScout: Security-Oriented Power Delivery Network Modeling for Side-Channel Vulnerability Analysis

The growing complexity of modern electronic systems leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared hardware resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains which are not explicitly specified in the functional specification. Recent works have demonstrated that the exploitation of the side channel can compromise the system security such as information leakage and fault injection. In this work, we systematically investigate the PDN-based side channel as well as potential countermeasures. To facilitate this goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs a fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in the literature via simulation. Furthermore, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23 dB at mid-frequency range and inter-chip leakage by an average of 31.68 dB at mid- and high-frequency range. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control. In addition, the vulnerabilities are evaluated when hiding-based countermeasures are implemented. Based on the evaluation, we can understand the optimal defense configuration and explore the trade-off between information leakage mitigation and power supply stability.