Generic Attacks Research Articles

Improved Quantum Rebound Attacks on Double Block Length Hashing with Round-Reduced AES-256 and ARIA-256

At EUROCRYPT 2020, Hosoyamada and Sasaki proposed the first dedicated quantum collision attacks on hash functions. Their proposal presented a quantum adaptation of the rebound attack and revealed that differential trails, which have too low probability for use in classical settings, might be exploitable in quantum settings. After their work, subsequent research has actively delved into analyzing the security of hash functions in the quantum setting.In this paper, we revisit the quantum rebound attacks on the double block hash function Hirose instantiated with 10-round AES-256 (HCF-AES-256) and 7-round ARIA-256 (HCF-ARIA-256) proposed by Chauhan et al. and Baek et al., respectively. Initially, we identify the flaws in their work and reevaluate the complexity of the attacks. We reveal that the flaws stem from not considering the issue that the S-box differential equation has one solution on average. Earlier research addressed this problem by adding auxiliary bits to the search space. If this method is used to correct the flaws, the resulting time complexities are 217.36 and 220.94 times higher than their proposals. Consequently, in some settings, their attacks become less efficient than generic attacks.Subsequently, we propose improved quantum rebound attacks using nested quantum amplitude amplification and quantum state preparation. Our improved attack efficiently pre-filters the search space, leading to a reduction in overall time complexity. We first classically reduce the search space and employ quantum state preparation to generate a superposition state over the pre-filtered search space. We then use nested quantum amplitude amplification to further reduce the search space quantumly. As a result, we achieve a reduction in the time complexity of the quantum rebound attacks on HCF-AES-256 and HCF-ARIA-256 by factors of 211.2 and 219.5, respectively, making the attacks more efficient than generic attacks again.

Guidance Through Surrogate: Toward a Generic Diagnostic Attack.

Adversarial training (AT) is an effective approach to making deep neural networks robust against adversarial attacks. Recently, different AT defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well-studied adversarial attacks, such as projected gradient descent (PGD). High adversarial robustness can also arise if an attack fails to find adversarial gradient directions, a phenomenon known as "gradient masking." In this work, we analyze the effect of label smoothing on AT as one of the potential causes of gradient masking. We then develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed guided projected gradient attack (G-PGA). Our attack approach is based on a "match and deceive" loss that finds optimal adversarial directions through guidance from a surrogate model. Our modified attack does not require random restarts a large number of attack iterations or a search for optimal step size. Furthermore, our proposed G-PGA is generic, thus it can be combined with an ensemble attack strategy as we demonstrate in the case of auto-attack, leading to efficiency and convergence speed improvements. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.

Integrating Novel Mechanisms for Threat Detection in Enhanced Data Classification using Ant Colony Optimization with Recurrent Neural Network

In new technologies like fog computing, edge computing, cloud computing, and the Internet of Things (IoT), cybersecurity concerns and cyber-attacks have surged. The demand for better threat detection and prevention systems has increased due to the present global uptick in phishing and computer network attacks. In order to identify irregularities and attacks on the network, which have increased in scale and prevalence, threat identification is essential. However, the community is forced to investigate and create novel threat detection approaches that are capable of detecting threats using anomalies due to the increase in network threats, the growth of new methods of attack and computations, and the requirement to ensure security measures. A novel mechanism is employed to identify threats in a data based on optimized deep learning. The main aim of this paper is the usage of data classification system based on Deep Learning (DL). The proposed mechanism employed the TCP (Transmission Control Protocol) communication protocol to extract data from loud IoT (Internet of Things) networks for the purpose of threat detection. To perform feature extraction an Ant Colony Optimization (ACO) is utilised, through Recurrent Neural Network (RNN), the attacks in data are classified and detected. Additionally, the suggested approach has been evaluated and trained using the BOUN DDoS contemporary dataset, which comprises a variety of attack types and allows for the effectiveness of the framework to be determined to compare it to previous approaches. The Findings indicate that the suggested approach achieved higher accuracy in DDoS attack identification in comparison with Traditional deep learning methods. The existing method detects the generic attack with lower efficiency however; the proposed mechanism achieves better accuracy in both the detection of the DDoS attack and the detection of regular traffic.

Multidimensional Linear Cryptanalysis of Feistel Ciphers

This paper presents new generic attacks on Feistel ciphers that incorporate the key addition at the input of the non-invertible round function only. This feature leads to a specific vulnerability that can be exploited using multidimensional linear cryptanalysis. More specifically, our approach involves using key-independent linear trails so that the distribution of a combination of the plaintext and ciphertext can be computed. This makes it possible to use the likelihood-ratio test as opposed to the χ2 test. We provide theoretical estimates of the cost of our generic attacks and verify these experimentally by applying the attacks to CAST-128 and LOKI91. The theoretical and experimental findings demonstrate that the proposed attacks lead to significant reductions in data-complexity in several interesting cases.

Generic attacks on small-state stream cipher constructions in the multi-user setting

Small-state stream ciphers (SSCs), which violate the principle that the state size should exceed the key size by a factor of two, still demonstrate robust security properties while maintaining a lightweight design. These ciphers can be classified into several constructions and their basic security requirement is to resist generic attacks, i.e., the time–memory–data tradeoff (TMDTO) attack. In this paper, we investigate the security of small-state constructions in the multi-user setting. Based on it, the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a setting. It is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing attack. Moreover, SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length, no matter whether the keystream length is limited or not. Finally, we apply these two generic attacks to TinyJAMBU and DRACO in the multi-user setting. The TMDTO distinguishing attack on TinyJAMBU with a 128-bit key can be mounted with time, memory, and data complexities of 2^{64}, 2^{48}, and 2^{32}, respectively. This attack is comparable with a recent work on ToSC 2022, where partial key bits of TinyJAMBU are recovered with more than 2^{50} users (or keys). As DRACO’s IV length is smaller than its key length, it is vulnerable to the TMDTO key recovery attack. The resulting attack has a time and memory complexity of both 2^{112}, which means DRACO does not provide 128-bit security in the multi-user setting.

Provable security against generic attacks on stream ciphers

Abstract Recent lightweight hardware-based stream cipher designs keep an external non-volatile internal state that is not part of the cipher’s hardware module. The purpose of these so-called small-state ciphers is to keep the size of the hardware and the power consumption low. We propose a random oracle model for stream ciphers. This will allow us to analyse the recent small-state stream cipher designs’ resistance against generic attacks and, in particular, time-memory-data tradeoff attacks. We analyse the conventional construction underlying stream ciphers like Grain and Trivium, constructions continuously using the external non-volatile secret key during keystream generation like Sprout, Plantlet, Fruit, and Atom, constructions continuously using the external non-volatile IV, and constructions using a combination of the IV and the key like DRACO. We show the tightness of all bounds by first presenting the time-memory-data tradeoff attacks on the respective constructions, establishing the upper bound on security, and then presenting the proof of security to establish the lower bound on security. In this work, we extend the theoretical work done by Hamann et al. who introduced the DRACO stream cipher at FSE 2023. We use the same random oracle model as the aforementioned work and apply it to the earlier work by Hamann et al. presented at SAC 2019, which showed security for two of the four constructions we consider in this work. Our model is equivalent but allows for a much simpler proof of security. Furthermore, we provide a proof of security for stream ciphers continuously using the secret key during keystream generation, giving upper and lower bounds for all four generic stream cipher constructions proposed so far.

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

PudgyTurtle Mode Resists Bit-Flipping Attacks

Cryptosystems employing a synchronous binary-additive stream cipher are susceptible to a generic attack called ’bit-flipping’, in which the ciphertext is modified to decrypt into a fraudulent message. While authenticated encryption and message authentication codes can effectively negate this attack, encryption modes can also provide partial protection against bit-flipping. PudgyTurtle is a stream-cipher mode which uses keystream to encode (via an error-correcting code) and to encipher (via modulo-2 addition). Here, we describe the behavior of this mode during bit-flipping attacks and demonstrate how it creates uncertainty about the number, positions, and identities of decrypted bits that will be affected.

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

Tight Multi-User Security Bound of DbHtS

In CRYPTO’21, Shen et al. proved that Two-Keyed-DbHtS construction is secure up to 22n/3 queries in the multi-user setting independent of the number of users. Here the underlying double-block hash function H of the construction realized as the concatenation of two independent n-bit keyed hash functions (HKh,1,HKh,2), and the security holds under the assumption that each of the n-bit keyed hash function is universal and regular. The authors have also demonstrated the applicability of their result to the key-reduced variants of DbHtS MACs, including 2K-SUM-ECBC, 2K-PMAC_Plus and 2K-LightMAC_Plus without requiring domain separation technique and proved 2n/3-bit multi-user security of these constructions in the ideal cipher model. Recently, Guo and Wang have invalidated the security claim of Shen et al.’s result by exhibiting three constructions, which are instantiations of the Two-Keyed-DbHtS framework, such that each of their n-bit keyed hash functions are O(2−n) universal and regular, while the constructions themselves are secure only up to the birthday bound. In this work, we show a sufficient condition on the underlying Double-block Hash (DbH) function, under which we prove an improved 3n/4-bit multi-user security of the Two-Keyed-DbHtS construction in the ideal-cipher model. To be more precise, we show that if each of the n-bit keyed hash function is universal, regular, and cross-collision resistant then it achieves the desired security. As an instantiation, we show that two-keyed Polyhash-based DbHtS construction is multi-user secure up to 23n/4 queries in the ideal-cipher model. Furthermore, due to the generic attack on DbHtS constructions by Leurent et al. in CRYPTO’18, our derived bound for the construction is tight.

Detection of Network Attacks using Machine Learning and Deep Learning Models

Anomaly-based network intrusion detection systems are highly significant in detecting network attacks. Robust machine learning and deep learning models for identifying network intrusion and attack types are proposed in this paper. Proposed models have experimented with the UNSW-NB15 dataset of 49 features for nine different attack samples. Decision Tree classifier produced the best accuracy of 99.05% compared to ensemble models - Random forest(98.96%), Adaboost(97.87%), and XGBoost(98.08%). K-Nearest Neighbour classifier trained for various values of K and best performance obtained for K=7 with the accuracy of 95.58%. A Deep Learning model with two dense layers with ReLU activation and a third dense layer with a Sigmoid activation function is designed for binary classification and produced good accuracy of 98.44% with ADAM optimizer, 80:20 Train-Test Split Ratio. Network attack exploits are detected with an accuracy 95% by XGBoost, Fuzzers attack with 90% accuracy by Random Forest, Generic attacks with 99% accuracy by Random Forest, and Reconnaissance attacks with 79% by Decision Trees. All features are relevant and strong in network attack detection, which eliminates the requirement of feature selection.

Security Analysis of Scan Obfuscation Techniques

Scan is the de-facto standard for testing, which provides high observability and test coverage by enabling direct access to chip memory elements. The scan-based Design-for-Testability (DfT) technique has also become the prime target of attackers whose aim is to extract the secret information embedded inside a chip by misusing its scan infrastructure. Several countermeasures have been proposed to protect the chip against scan-based attacks. Recently, obfuscation-based defense mechanisms have gained significant popularity, which protect scan data by corrupting some of the scan cell’s content. In this paper, we perform a detailed security analysis of three best-known obfuscation techniques, namely, static, dynamic, and advanced dynamic obfuscation techniques, designed to protect the AES crypto-chip. We exploit their vulnerabilities and propose a generic scan-based signature attack, leading to the leakage of the secret cipher key that too, using only one observable scan cell. We also propose upgrades to the two dynamic scan obfuscation techniques to patch the discovered vulnerabilities with negligible changes to the original design. In order to show the generality of the attack, we also applied our attack to the similar cipher PRESENT and seven other scan obfuscation techniques. The result shows that in addition to the above three best-known obfuscation techniques, five out of the seven other scan obfuscation techniques were also successfully broken by our generic attack.

MAD-EN: Microarchitectural Attack Detection Through System-Wide Energy Consumption

Microarchitectural attacks have become increasingly threatening the society with diverse set of attacks such as Spectre and Meltdown. Vendor patches cannot keep up with the pace of the new threats, which makes the dynamic anomaly detection tools more evident than before. Unfortunately, hardware performance counters (HPCs) utilized in previous works can detect a few microarchitectural attacks due to the small number of counters that can be profiled concurrently while introducing high performance overhead. These challenges consequently yield to inefficient detection tools in real-world security-critical systems. In this study, we introduce MAD-EN dynamic detection tool that leverages system-wide energy consumption traces collected from a generic Intel RAPL tool to detect ongoing anomalies in two different microarchitectures, namely Intel Comet Lake and Intel Tiger Lake. In the first phase of MAD-EN, we can distinguish 16 variants from 11 different micro-architectural attacks from benign applications by utilizing a binary-class CNN-based model with an F1 score of 0.998, which makes our tool the most generic attack detection tool so far. In the second phase, MAD-EN can recognize the respective attack types with a 95% accuracy by utilizing a multi-class CNN-based classification technique after the anomaly is detected. We demonstrate that MAD-EN introduces 69.3% less performance overhead compared to performance counter-based detection mechanisms, allowing more feasible real-time detection tool for generic purpose systems.

Attack Identification for Nonlinear Systems Based on Sparse Optimization

Adversarial attacks on controllers of dynamic systems have become a serious threat to many real-world systems, making methods for fast identification of attacks an indispensable part of autonomous systems. With the increasing use of model-based controllers, it is valid to exploit model knowledge also for attack identification as long as privacy of individual components is maintained. A scalable, model-based method to reveal generic attacks was introduced in our previous work and is further investigated here. It is designed for coupled systems with nonlinear dynamics and monitors certain coupling states. Based on the exchange of local sensitivity information, it approximates the propagation of an attack through the network and solves a sparse optimization problem to identify the attack. We provide a thorough derivation of the approach and analyze the involved approximation errors to prove rigorous guarantees for successful identification. In an extensive numerical case study with the IEEE 30 bus power system, we prove that not only the guarantees apply for a nonlinear, practically relevant example, but the method also identifies attacked buses with very high success rates.

Find the Bad Apples: An efficient method for perfect key recovery under imperfect SCA oracles – A case study of Kyber

Side-channel resilience is a crucial feature when assessing whether a postquantum cryptographic proposal is sufficiently mature to be deployed. In this paper, we propose a generic and efficient adaptive approach to improve the sample complexity (i.e., the required number of traces) of plaintext-checking (PC) oracle-based sidechannel attacks (SCAs), a major class of key recovery chosen-ciphertext SCAs on lattice-based key encapsulation mechanisms (KEMs). This new approach is preferable when the constructed PC oracle is imperfect, which is common in practice, and its basic idea is to design new detection codes that can determine erroneous positions in the initially recovered secret key. These secret entries are further corrected with a small number of additional traces. This work benefits from the generality of PC oracle and thus is applicable to various schemes and implementations.Our main target is Kyber since it has been selected by NIST as the KEM algorithm for standardization. We instantiated the proposed generic attack on Kyber512 and then conducted extensive computer simulations against Kyber512 and FireSaber. We further mounted an electromagnetic (EM) attack against an optimized implementation of Kyber512 in the pqm4 library running on an STM32F407G board with an ARM Cortex-M4 microcontroller. These simulations and real-world experiments demonstrate that the newly proposed attack could greatly improve the state-of-the-art in terms of the required number of traces. For instance, the new attack requires only 41% of the EM traces needed in a majority-voting attack in our experiments, where the raw oracle accuracy is fixed.

Quantum generic attacks on key-alternating Feistel ciphers for shorter keys

Quantum generic attacks on key-alternating Feistel ciphers for shorter keys

Detecting block ciphers generic attacks: An instance-based machine learning method

Cryptography facilitates selective communication through encryption of messages and or data. Block-cipher processing is one of the prominent methods for modern cryptographic symmetric encryption schemes. The rise in attacks on block-ciphers led to the development of more difficult encryption schemes. However, attackers decrypt block-ciphers through generic attacks given sufficient time and computing. Recent research had applied machine learning classification algorithms to develop intrusion detection systems to detect multiple types of attacks. These intrusion detection systems are limited by misclassifying generic attacks and suffer reduced effectiveness when evaluated for detecting generic attacks only. Hence, this study introduced and proposed k-nearest neighbors, an instance-based machine learning classification algorithm, for the detection of generic attacks on block-ciphers. The value of k was varied (i.e., 1, 3, 5, 7, and 9) and multiple nearest neighbors classification models were developed and evaluated using two distance functions (i.e., Manhattan and Euclidean) for classifying between generic attacks and normal network packets. All nearest neighbors models using the Manhattan distance function performed better than their Euclidean counterparts. The 1-nearest neighbor (Manhattan distance function) model had the highest overall accuracy of 99.6%, a generic attack detection rate of 99.5% which tallies with the 5, 7, and 9 nearest neighbors models, and a false alarm rate of 0.0003 which is the same for all Manhattan nearest neighbors classification models. These instance-based methods performed better than some existing methods that even implemented an ensemble of deep-learning algorithms. Therefore, an instance-based method is recommended for detecting block-ciphers generic attacks.

A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System

Cyber-attacks and unauthorized application usage have increased due to the extensive use of Internet services and applications over computer networks, posing a threat to the service’s availability and consumers’ privacy. A network Intrusion Detection System (IDS) aims to detect aberrant traffic behavior that firewalls cannot detect. In IDSs, dimension reduction using the feature selection strategy has been shown to be more efficient. By reducing the data dimension and eliminating irrelevant and noisy data, several bio-inspired algorithms have been employed to improve the performance of an IDS. This paper discusses a modified bio-inspired algorithm, which is the Grey Wolf Optimization algorithm (GWO), that enhances the efficacy of the IDS in detecting both normal and anomalous traffic in the network. The main improvements cover the smart initialization phase that combines the filter and wrapper approaches to ensure that the informative features will be included in early iterations. In addition, we adopted a high-speed classification method, the Extreme Learning Machine (ELM), and used the modified GWO to tune the ELM’s parameters. The proposed technique was tested against various meta-heuristic algorithms using the UNSWNB-15 dataset. Because the generic attack is the most common attack type in the dataset, the primary goal of this paper was to detect generic attacks in network traffic. The proposed model outperformed other methods in minimizing the crossover error rate and false positive rate to less than 30%. Furthermore, it obtained the best results with 81%, 78%, and 84% for the accuracy, F1-score, and G-mean measures, respectively.

Not all datasets are born equal: On heterogeneous tabular data and adversarial examples

Recent work on adversarial learning has mainly focused on neural networks and domains in which those networks excel, such as computer vision and audio processing. Typically, the data in those domains is homogeneous, whereas domains with heterogeneous tabular datasets remain underexplored, despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data and the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proven challenging, and a generic attack method has not yet been proposed. However, this study argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data, such as images. To support this claim, a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces is introduced. The framework defines distribution-aware constraints to preserve the consistency of the adversarial examples and then incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets, we focus on ℓ0 perturbations and demonstrate their applicability in real life. The effectiveness of the suggested approach is demonstrated using three datasets from different content domains. The results show that despite the constraints imposed on the input validity in heterogeneous datasets, machine learning models trained using such data are still susceptible to adversarial examples.

Exploiting side-channel leaks in web traffic of incremental search

The explosion of information has resulted in incremental search becoming an essential tool for many websites. This technology provides real-time suggestions by sending the current query to the server. Despite encryption, search requests can be leveraged by passive attackers to infer the query typed by the user. In this paper, we show that at least nine of Alexa’s top 50 websites have serious side-channel leaks. More importantly, we use information theory to quantify the leakage and report the upper bound of recognition accuracy that an attacker can achieve. We further develop a generic attack attempting to infer users’ queries by monitoring web search traffic. Experimentally, the attack performance is close to the theoretical bounds. The most vulnerable website allows up to 53% of English queries and 76% of Chinese queries to be identified from 825k and 140k queries, respectively. Overall, our work highlights the prevalence of such side-channel leaks on the Internet and provides insights for developers to help mitigate the threat.