Special Attack Research Articles

Improving security of efficient multiparty quantum secret sharing based on a novel structure and single qubits

Quantum secret sharing is a basic quantum cryptographic primitive, which has a lot of applications in information security and privacy preservation. An efficient multiparty quantum secret sharing protocol (Kuo et al. in EPJ Quantum Technol 10(1):29, 2023) based on a novel structure and single qubits was reported recently. In this paper, we give a cryptanalysis of this protocol and show that it cannot satisfy the security requirement for secret sharing because an unauthorized set of agents can gain access to some information on the dealer’s secret by a special collusion attack. Furthermore, we put forward a way to deal with the security problem.

Detailed Implementation about Street Fighter Game using Python

This paper examines the varied cultural meanings of computer game play in competitive and professional computer gaming and live-streaming. A fighting game is a genre of video game that involves combat between two or more characters. Fighting game combat often features mechanics such as blocking, grappling, counter-attacking, and chaining attacks together into "combos". Characters generally engage in battle using hand-to-hand combat—often some form of martial arts. The fighting game genre is related to, but distinct from, the beat 'em up genre, which pits large numbers of computer- controlled enemies against one or more player characters. The controls for Final Fight consist of an eight-way joystick and two buttons for attacking and jumping respectively. Pressing the attack button repeatedly when attacking an enemy Pressing the attack and jump buttons simultaneously allows the player to perform a special attack that strikes all surrounding enemies, but will drain a small portion of the player's health. Enemies can be grabbed simply by walking into one of them. When an enemy is grabbed, the player can perform a grab attack by pressing the attack button or perform a throw by tilting the joystick left or right. A thrown enemy can be tossed at another for additional damage. Items such as weapons, health recovery items, and items awarding extra points can be picked up by standing over one and pressing the attack button. Weapons have limited uses and will disappear if the player is disarmed by an enemy too much or when the player moves to a new area.

Optical single-channel cryptosystem based on the non-negative matrix factorization and face biometric in cyan–magenta–yellow–black color space

In this paper, an optical color single-channel asymmetric cryptosystem based on the non-negative matrix factorization (NMF) and a face biometric in cyan–magenta–yellow–black (CMYK) space is proposed. To the best of our knowledge, this is the first time that NMF has been introduced into optical color image encryption. In the proposed cryptosystem, the color image in CMYK space is first decomposed into four color channels: C, M, Y, and K. By performing NMF operations on the four color channels, the four basic and sparse matrices can be obtained, respectively, which achieves asymmetry and saves computational resources. The four basis matrices can be used as private keys, and the four coefficient matrices are synthesized by the inverse discrete wavelet transform for subsequent encryption. Finally, the synthesized image is encoded with double random phase encoding based on phase truncation (PT). Compared with the existing PT-based cryptosystems, our cryptosystem can improve security against a special attack. In addition, the chaotic random phase mask is generated by a face biometric, which is noncontact and unique. Numerical simulation results are shown to verify the feasibility and robustness of our cryptosystem. Further, the proposed cryptosystem can be extended to encrypt multiple images conveniently.

Detailed Review about Street Fighter Game using Python

This paper examines the varied cultural meanings of computer game play in competitive and professional computer gaming and live-streaming. A fighting game is a genre of video game that involves combat between two or more characters. Fighting game combat often features mechanics such as blocking, grappling, counter- attacking, and chaining attacks together into "combos". Characters generally engage in battle using hand-to-hand combat—often some form of martial arts. The fighting game genre is related to, but distinct from, the beat 'em up genre, which pits large numbers of computer-controlled enemies against one or more player characters. The controls for Final Fight consist of an eight-way joystick and two buttons for attacking and jumping respectively. Pressing the attack button repeatedly when attacking an enemy Pressing the attack and jump buttons simultaneously allows the player to perform a special attack that strikes all surrounding enemies, but will drain a small portion of the player's health. Enemies can be grabbed simply by walking into one of them. When an enemy is grabbed, the player can perform a grab attack by pressing the attack button or perform a throw by tilting the joystick left or right. A thrown enemy can be tossed at another for additional damage. Items such as weapons, health recovery items, and items awarding extra points can be picked up by standing over one and pressing the attack button. Weapons have limited uses and will disappear if the player is disarmed by an enemy too much or when the player moves to a new area.

Secure state estimation and actuator attack reconstruction for cyber‐physical systems based on sliding‐mode observer

AbstractIn this paper, the secure state estimation attack and reconstruction problems are considered for linear cyber‐physical systems (CPSs) under actuator attacks and unknown disturbances. By introducing a continuous sliding mode observer with an exponential reaching law strategy, the dynamic quality of approach motion is greatly improved and the chattering is eliminated. Then by transforming the original system into a special attack channel separation form so that an improved sliding mode observer with disturbance compensation is obtained to solve the secure state estimation problem. In addition, with the obtained observer's information, not only the actuator attack can be reconstructed but also the estimation of the disturbance can be obtained. Meanwhile, the influence of disturbance estimation error on attack reconstruction can be attenuated. Finally, simulations on a VTOL aircraft are presented to verify the effectiveness of the proposed scheme.

Implementation of Statistical Tool in Legendary Pokémon Analysis and the Pricing in Stock Market

Statistics are widely applied in wide spread of advanced science researches to give the quantitative crustily, which is now much more well-liked. In this paper, a dataset is analyzed and a pricing method is presented based on two different statistical models. In the random forest model, we first do a simple statistical data analysis to determine the association between attributes and "is legendary" using the Legendary Pokémon dataset. The above analysis results may then be used to determine which characteristics can make a Pokémon legendary. In the Black-Scholes model, we first discuss SDE before using it to provide a stock pricing method. For the former one, the statistical data analysis in the first application may determine that all the characteristics have a positive correlation with "is legendary," and lastly, random forest discovered that the characteristics that can turn a Pokémon legendary are special attack and speed. Through the second application, it can determine how an SDE’s trajectory and coefficients are related. The pricing method is then obtained by applying SDE to the Black-Scholes model. We value a company’s shares using this method and contrast the accuracy of this approach with more conventional approaches. According to data analysis, the first application easily and intuitively demonstrates random forest’s practical application capabilities, as well as its ease and accuracy for us. These results shed light on guiding further exploration of the practice of random forests in data analysis and the application of SDE related models.

Fingerprint image encryption using phase retrieval algorithm in gyrator wavelet transform domain using QR decomposition

In this paper, we propose a novel optical asymmetric fingerprint image encryption technique that uses QR decomposition in gyrator wavelet transform domain. A fingerprint image is bonded with a phase mask generated with Gerchberg–Saxton phase retrieval algorithm, which is gyrator wavelet transformed. This output is further applied to QR decomposition scheme and different keys are generated through it. The process is iterated to enhance the level of security. A Haar wavelet has been used in the study. Different phase images, gyrator transform orders and the parameters of the wavelet help achieve imperceptibility and robustness. The asymmetric keys make the system attack free like Brute force attack, known plain text attack, and special attack. Numerical simulations carried out on a MATLAB platform demonstrate the security, privacy, and validity of the proposed encryption scheme.

Optical double-image cryptosystem based on generalized singular value decomposition and five-dimensional hyperchaotic maps.

In this paper, we propose an asymmetric optical double-image cryptosystem based on generalized singular value decomposition (GSVD) and five-dimensional (5D) hyperchaotic maps. In the proposed cryptosystem, the two plain images are first decomposed into five components by the GSVD operation. The two unitary matrices obtained by GSVD are encoded as a complex function, which is then modulated by the chaotic random phase masks (CRPMs). The private key and the final encryption result are generated by phase-truncation and amplitude-truncation operations. The GSVD operation can decompose two images at the same time and is used to generate the private key that enables the encryption process to be asymmetric. Compared with the existing phase-truncated-based cryptosystems, our cryptosystem can improve security against a special attack. In addition, the CRPMs are generated by 5D hyperchaotic maps, which have a larger parameter space and better randomness. Numerical simulation results are shown to verify the feasibility and robustness of our cryptosystem. Furthermore, the proposed cryptosystem can be extended to encrypt multiple images conveniently.

Minimum Power Adversarial Attacks in Communication Signal Modulation Classification with Deep Learning

Integrating cognitive radio (CR) technique with wireless networks is an effective way to solve the increasingly crowded spectrum. Automatic modulation classification (AMC) plays an important role in CR. AMC significantly improves the intelligence of CR system by classifying the modulation type and signal parameters of received communication signals. AMC can provide more information for decision making of the CR system. In addition, AMC can help the CR system dynamically adjust the modulation type and coding rate of the communication signal to adapt to different channel qualities, and the AMC technique help eliminate the cost of broadcast modulation type and coding rate. Deep learning (DL) has recently emerged as one most popular method in AMC of communication signals. Despite their success, DL models have recently been shown vulnerable to adversarial attacks in pattern recognition and computer vision. Namely, they can be easily deceived if a small and carefully designed perturbation called an adversarial attack is imposed on the input, typically an image in pattern recognition. Owing to the very different nature of communication signals, it is interesting yet crucially important to study if adversarial perturbation could also fool AMC. In this paper, we make a first attempt to investigate how we can design a special adversarial attack on AMC. we start from the assumption of a linear binary classifier which is further extended to multi-way classifier. We consider the minimum power consumption that is different from existing adversarial perturbation but more reasonable in the context of AMC. We then develop a novel adversarial perturbation generation method that leads to high attack success to communication signals. Experimental results on real data show that the method is able to successfully spoof the 11-class modulation classification at a model with a minimum cost of about − 21 dB in automatic modulation classification task. The visualization results demonstrate that the adversarial perturbation manifests in the time domain as imperceptible undulations of the signal, and in the frequency domain as small noise outside the signal band.

Multiuser optical image authentication platform based on sparse constraint and polar decomposition in Fresnel domain

In this paper, a new multiuser optical image encryption and authentication technique is proposed. Sparse multiplexing and polar decomposition are used in the Fresnel domain to obtain the ciphertext of an input image. To enable the multiuser platform, multiple private keys are obtained through polar decomposition during the encryption process. It will allow multiple authorized users to access the secure information simultaneously without having a key distribution problem among them. The proposed method has a large key space and is robust against several attacks, such as contamination attacks (noise and occlusion), brute force attack, plaintext attacks, and special iterative attack. A comparative analysis of the presented technique is also performed with the similar existing techniques. The numerical simulation results demonstrate the robustness and feasibility of the proposed algorithm.

Delta-DAGMM: A Free Rider Attack Detection Model in Horizontal Federated Learning

Federated learning is a machine learning framework proposed in recent years. In horizontal federated learning, multiple participants cooperate to train and obtain a common final model. Participants only need to transmit the local updated model instead of local datasets. Some participants do not use effective local data sets, but provide disguised model parameters to participate in federal training and obtain common training models. This attack is called Free-rider attack. To the best of our knowledge, researches have proposed some Free-rider attack strategies with theoretical support, but there are few researches on Free-rider attack detection. However, the model disguised by some attackers using special attack strategies is similar to the real model in terms of convergence and weight, so it is difficult to detect the model provided by attacker as abnormal data. Based on DAGMM, a high-dimensional abnormal data detection model, this paper optimizes the sample processing and compression model, and proposes an improved detection algorithm, called Delta-DAGMM. Two types of large datasets are used for experiments. The experimental results show that Delta-DAGMM has higher precision and F1 score than DAGMM. On average, the Delta-DAGMM algorithm achieves a precision of 98.42% and an F1 score of 98.36%.

Optical asymmetric single-channel cryptosystem based on QZ synthesis for color images

In this paper, we propose an optical asymmetric single-channel cryptosystem based on QZ synthesis (QZS) for color images. In our cryptosystem, a QZS method for synthesizing color image into the grayscale image is proposed for the first time, thus realizing single-channel encryption. Then the synthesized image is modulated with the random phase masks, and transformed in the Gyrator domain to obtain the cipher image. With the QZS, our cryptosystem can achieve asymmetric and single-channel encryption. Compared with the existing phase truncated-based cryptosystems, our cryptosystem can improve security against the special attack. Numerical simulation results demonstrate the feasibility and robustness of our cryptosystem for encrypting one or even multiple color images.

Asymmetric double-image encryption using twin decomposition in fractional Hartley domain

Twin decomposition, consisting of equal and random modulus decompositions, not only makes a cryptosystem asymmetric but also resists special attack. A new double-image asymmetric cryptosystem using twin decomposition in fractional Hartley domain is proposed. An input grayscale image, bonded with another grayscale image as its phase mask, is transformed via fractional Hartley transform. Equal modulus decomposition is applied on the resulting image, giving us two intermediate images. One of them is subjected to another fractional Hartley transform followed by random modulus decomposition, whereas the other serves as the first private key. The application of random modulus decomposition also results in two images: encrypted image and the second private key. During the process of decryption, firstly the encrypted image is combined with second private key and thereafter it is subjected to inverse fractional Hartley transform. The resulting image is then combined with the first private key, and followed by another inverse fractional Hartley transform, thus recovering the two original images. The proposed cryptosystem is validated for pairs of grayscale images.

A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base

Advanced persistent threat (APT) is a special attack method, which is usually initiated by hacker groups to steal data or destroy systems for large enterprises and even countries. APT has a long-term and multi-stage characteristic, which makes it difficult for traditional detection methods to effectively identify. To detect APT attacks requires solving some problems: how to deal with various uncertain information during APT attack detection, how to fully train the APT detection model with small attack samples, and how to obtain the interpretable detection results for subsequent APT attack forensics. Traditional detection methods cannot effectively utilize multiple uncertain information with small samples. Meanwhile, most detection models are black box and lack a transparent calculation process, which makes it impossible for managers to analyze the reliability and evidence of the results. To solve these problems, a novel detection method based on belief rule base (BRB) is proposed in this paper, where expert knowledge and small samples are both utilized to obtain interpretable detection results. A case study with numerical simulation is established to prove the effectiveness and practicality of the proposed method.

Recent progress on the corrosion behavior of metallic materials in HF solution

Abstract Hydrofluoric acid (HF) or fluoride ion corrosion issues are often encountered in many fields, which have attracted extensive research due to its strong corrosiveness. In this paper, a critical review is presented based on recent progress on HF corrosion. In view of the discrepancy of fluoride ion compared with other ions, the special attack characteristics of fluoride ion are firstly discussed. Afterwards, the corrosion mechanisms of stainless steels, nickel-based alloys, and titanium alloys in HF solution or fluoride ion-containing environment are reviewed, and three typical corrosion behaviors are summarized, which are essentially weakening process of passivation of metallic materials. The effects of influencing factors (e.g. alloying elements, environmental factors, and stress, etc.) on HF corrosion are also discussed, which involve changes in passivation mechanism, the influence of HF attack mode and multiple damage mechanisms due to mechanical–chemical coupling. Finally, future research works on HF corrosion are proposed.

Unequal modulus decomposition and modified Gerchberg Saxton algorithm based asymmetric cryptosystem in Chirp-Z transform domain

In this paper, we have presented an image encryption method in Chirp-Z transform domain using unequal modulus decomposition (UMD) and modified Gerchberg–Saxton (GS) algorithm. The proposed encryption scheme is highly sensitive to the encryption keys and the modified GS algorithm introduces an additional layer of security. The validity of the proposed method is tested with various grayscale and binary images and the numerical simulation results are demonstrated for ‘Cameraman’, ‘Medical’ and Binary ‘CUH’ images. The presented results confirm the robustness of the proposed method against various existing attacks such as, the noise attack, special attack, statistical attack, and brute force attack. A comparative analysis with existing similar methods is also performed and the enhanced security and efficiency of the proposed method is verified.

Cryptanalysis and Improvement of Quantum Private Comparison without Classical Computation

Recently, Lang suggested a quantum private comparison (QPC) without classical computation (Int J Theor Phys, 59(2020)2984). Lang claimed that this QPC protocol is secure against both the participant attack and the outside attack. It is pointed out in this paper that the third party (TP) can totally obtain the private binary sequences of two communicants by launching a special measurement attack; and moreover, an outside attacker can make this protocol fail by launching the disturbance attack. The corresponding methods are further put forward to overcome these drawbacks.

A Fully Adaptively Secure Threshold Signature Scheme Based on Dual-Form Signatures Technology

In t , n threshold signature schemes, any subset of t participants out of n can produce a valid signature, but any fewer than t participants cannot. Meanwhile, a threshold signature scheme should remain robust and unforgeable against up to t − 1 corrupted participants. This nonforgeability property is that even an adversary breaking into up to t − 1 participants should be unable to generate signatures on its own. Existential unforgeability against adaptive chosen message attacks is widely considered as a standard security notion for digital signature, and threshold signature should also follow this accordingly. However, there are two special attack models in a threshold signature scheme: one is the static corruption attack and the other is the adaptive corruption attack. Since the adaptive corruption model appears to better capture real threats, designing and proving threshold signature schemes secure in the adaptive corruption model has been focused on in recent years. If a threshold signature is secure under adaptive chosen message attack and adaptive corruption attack, we say it is fully adaptively secure. In this paper, based on the dual pairing vector spaces technology, we construct a threshold signature scheme and use Gerbush et al.’s dual-form signatures technology to prove our scheme, which is fully adaptively secure in the standard model, and then compare it to other schemes in terms of the efficiency and computation.

Cryptanalysis and Improvement of Quantum Gate-Based Quantum Private Comparison

Recently, Lang put forward for the first time a quantum gate-based quantum private comparison (QPC) protocol (Int J Theor Phys, 59(2020)833) which uses the quantum controlled-not gates to implement comparison instead of classical exclusive-or computations. It was claimed that this quantum gate-based QPC protocol is secure against both the outside attack and the participant attack. However, in this paper, it is pointed out that this protocol cannot resist a special measurement attack from the third party (TP) and the disturbance attack from an outside attacker. The corresponding improvements are further suggested to remedy these security loopholes.

Novel LDoS attack detection by Spark‐assisted correlation analysis approach in wireless sensor network

Low-rate denial of service (LDoS) attack is a special DoS attack type of wireless sensor network (WSN). Routing protocol is the critical component of the WSN. Routing flood attack is a novel LDoS attack pattern in WSN. However, the attack is difficult to be detected by traditional intrusion detection algorithm. A novel LDoS attack detection method based on big data and signal analysis is proposed. Hilbert–Huang Transform (HHT) time–frequency signal analysis method is used to analyse the small non-linear signal from LDoS attack traffic signal. Spark-based Pearson and Spearman correlation coefficient calculation approaches are used to recognise the false intrinsic mode functions (IMFs) components decomposed by the HHT method. The effective threshold value of Pearson correlation coefficient is set to 0.2, the effective threshold value of Spearman correlation coefficient is set to 0.3, which are united to identify the false IMF components. SunSpot wireless nodes are used to build the wireless sensor nodes. If the difference between the IMF component and the normal IMF component is more than 40%, the LDoS attack will be detected. Experimental results show that this approach is effective to detect the LDoS attack in ZigBee WSN. This is a quantitative LDoS attack detection experimental research in WSN.