Search Results (26)

Quantum Key Distribution (QKD) enables the sharing of cryptographic keys secured by quantum mechanics. The BB84 protocol assumes single-photon sources, but practical systems rely on weak coherent pulses vulnerable to Photon-Number-Splitting (PNS) attacks. The Gottesman–Lo–Lütkenhaus–Preskill (GLLP) framework addresses these imperfections, deriving secure key rate bounds under limited PNS scenarios. The decoy-state protocol further improves performance by refining single-photon yield estimates, but still considers multi-photon states as insecure, thereby limiting intensities and constraining key rate and distance. More recently, finite-key security bounds for decoy-state QKD have been extended to address general attacks, ensuring security against adversaries capable of exploiting arbitrary strategies. In this work, we focus on a specific class of attacks, the generalized PNS attack, and demonstrate that higher pulse intensities can be securely used by employing Bayesian inference to estimate key parameters directly from observed data. By raising the pulse intensity to 10 photons, we achieve a 50-fold increase in key rate and a 62.2% increase in operational range (about 200 km) compared to the decoy-state protocol. Furthermore, we accurately model after-pulsing using a Hidden Markov Model (HMM) and reveal inaccuracies in decoy-state calculations that may produce erroneous key-rate estimates. While this methodology does not address all possible attacks, it provides a new approach to security proofs in QKD by shifting from worst-case assumption analysis to observation-dependent inference, advancing the reach and efficiency of discrete-variable QKD protocols. Full article

Sharding is currently one of the mainstream technologies for solving the scalability problem in blockchain systems. However, with the increase in shard numbers, the coordination and management of cross-shard transactions become more complex, limiting the scalability of the system. Existing methods usually split cross-shard transactions into multiple sub-transactions for processing, which not only reduces throughput but also increases transaction latency. This paper proposes a blockchain sharding protocol based on multi-shard storage to address this issue. In this protocol, nodes can store data from multiple shards, and nodes that store the same shard set form a consensus zone, which can directly handle cross-shard transactions and improve transaction processing efficiency. We propose a priority sorting mechanism to defend against double-spending attacks effectively. In addition, we introduce a P-probability return update completion proof mechanism to ensure node data consistency while enhancing blockchain security. Finally, we conduct a security analysis and performance testing of this protocol. The results show that the multi-shard storage protocol has significant advantages in terms of throughput, latency, and security compared to traditional sharding protocols. Full article

Maintaining security in communication networks has long been a major concern. This issue has become increasingly crucial due to the emergence of new communication architectures like the Internet of Things (IoT) and the advancement and complexity of infiltration techniques. For usage in networks based on the Internet of Things, previous intrusion detection systems (IDSs), which often use a centralized design to identify threats, are now ineffective. For the resolution of these issues, this study presents a novel and cooperative approach to IoT intrusion detection that may be useful in resolving certain current security issues. The suggested approach chooses the most important attributes that best describe the communication between objects by using Black Hole Optimization (BHO). Additionally, a novel method for describing the network’s matrix-based communication properties is put forward. The inputs of the suggested intrusion detection model consist of these two feature sets. The suggested technique splits the network into a number of subnets using the software-defined network (SDN). Monitoring of each subnet is done by a controller node, which uses a parallel combination of convolutional neural networks (PCNN) to determine the presence of security threats in the traffic passing through its subnet. The proposed method also uses the majority voting approach for the cooperation of controller nodes in order to more accurately detect attacks. The findings demonstrate that, in comparison to the prior approaches, the suggested cooperative strategy can detect assaults in the NSLKDD and NSW-NB15 datasets with an accuracy of 99.89 and 97.72 percent, respectively. This is a minimum 0.6 percent improvement. Full article

Due to the fine-grained splitting of microservices and frequent communication between microservices, the exposed attack surface of microservices has exploded, facilitating the lateral movement of attackers between microservices. To solve this problem, a multi-dimensional moving target defense method based on an adaptive simulated annealing genetic algorithm (MD2RS) is proposed. Firstly, according to the characteristics of microservices in the cloud, a microservice attack graph is proposed to quantify the attack scenario of microservices in the cloud so as to conveniently and intuitively observe the vulnerability of microservices in the cloud and the dependency relationship between microservices. Secondly, the security gain and resource cost are quantified for the key nodes selected by measuring the degree of dependence of each node according to the degree centrality. Finally, the Adaptive Simulated Annealing Genetic Algorithm (ASAGA) is used to solve the optimal security configuration information of the moving target defense, that is, the combination of the number of copies of the multi-copy deployment and the rotation cycle of the dynamic rotation of microservices, in order to quickly evaluate the security risks of microservices and optimize the security policy. Experiments show that the defense return rate of MD2RS is 85.95% higher than that of the mainstream methods, and the experimental results are conducive to applying this method to the dynamic defense of microservices in the cloud. Full article

Postselective transformations of quantum states is a broader class of operations than deterministic quantum channels. Here, we describe the possibility of increasing the success probability of postselective operations by using additional information, which has a form of pure quantum states and should not be changed in case of success. We describe the conditions under which assistance becomes useful, and provide application of our method which improves the efficiency of photon number splitting attack for a variant of SARG04 quantum key distribution protocol. In our attack scenario, one extra photon, which is unchanged, plays the role of assistance. Full article

Ransomware is one type of malware that involves restricting access to files by encrypting files stored on the victim’s system and demanding money in return for file recovery. Although various ransomware detection technologies have been introduced, existing ransomware detection technologies have certain limitations and problems that affect their detection ability. Therefore, there is a need for new detection technologies that can overcome the problems of existing detection methods and minimize the damage from ransomware. A technology that can be used to detect files infected by ransomware and by measuring the entropy of files has been proposed. However, from an attacker’s point of view, neutralization technology can bypass detection through neutralization using entropy. A representative neutralization method is one that involves decreasing the entropy of encrypted files by using an encoding technology such as base64. This technology also makes it possible to detect files that are infected by ransomware by measuring entropy after decoding the encoded files, which, in turn, means the failure of the ransomware detection-neutralization technology. Therefore, this paper derives three requirements for a more sophisticated ransomware detection-neutralization method from the perspective of an attacker for it to have novelty. These requirements are (1) it must not be decoded; (2) it must support encryption using secret information; and (3) the entropy of the generated ciphertext must be similar to that of plaintext. The proposed neutralization method satisfies these requirements, supports encryption without decoding, and applies format-preserving encryption that can adjust the input and output lengths. To overcome the limitations of neutralization technology using the encoding algorithm, we utilized format-preserving encryption, which could allow the attacker to manipulate the entropy of the ciphertext as desired by changing the expression range of numbers and controlling the input and output lengths in a very free manner. To apply format-preserving encryption, Byte Split, BinaryToASCII, and Radix Conversion methods were evaluated, and an optimal neutralization method was derived based on the experimental results of these three methods. As a result of the comparative analysis of the neutralization performance with existing studies, when the entropy threshold value was 0.5 in the Radix Conversion method, which was the optimal neutralization method derived from the proposed study, the neutralization accuracy was improved by 96% based on the PPTX file format. The results of this study provide clues for future studies to derive a plan to counter the technology that can neutralize ransomware detection technology. Full article

Rubber concrete (RC) is a new type of concrete that is currently receiving a lot of attention, solving serious pollution problems by grinding waste tires into granules and adding them to concrete. However, rubber concrete has deficiencies in mechanics and durability, and has been reinforced by adding fibers in many studies. In this study, the mechanical and durability properties of rubber concrete with added polypropylene and basalt fibers (PBRC) were investigated in a series of experiments including apparent morphology, mass, static compressive and tensile tests, ultrasonic non-destructive testing, and scanning electron microscope (SEM) tests under coupled environments of sulfate attack and freeze–thaw. The results showed that the mass loss rate of RC and PBRC gradually increased with the number of freeze–thaw cycles, with more pits and cement paste peeling from the specimen surface. Moreover, the compressive and splitting tensile strengths of RC and PBRC groups exhibited distinct trends, with the former group showing a lower residual strength relative to the latter. The residual compressive strength of the RC group was only 69.4% after 160 freeze–thaw cycles in 5% MgSO₄ solution. However, it is worth noting that the addition of too many fibers also had a negative effect on the strength of the rubber concrete. Additionally, the scanning electron microscopy (SEM) results indicated that the fibers restricted the formation of microcracks in the microstructure and curtailed the brittleness of the concrete. This study can provide a valuable reference for the application of environmentally friendly material fibers in recycled aggregate concrete. Full article

As a top construction material worldwide, concrete has core weakness relating to low tensile resistance without reinforcement. It is the reason that a variety of innovative materials are being used on concrete to overcome its weaknesses and make it more reliable and sustainable. Further, the embodied carbon of concrete is high because of cement being used as the integral binder. Latest research trends indicate significant potential for carbon fiber as an innovative material for improving concrete mechanical strength. Although significant literature is available on the use of carbon fiber in concrete, a limited number of studies have focused on the utilization of carbon fiber for concrete mechanical strength improvement and the reduction of embodied carbon. Following the gap in research, this study aimed to investigate and optimize the use of carbon fiber for its mechanical characteristics and embodied carbon improvements. The use of carbon fiber in self-compacting concrete lowers sagging. The greatest quantity of carbon fiber is that it reduces the blockage ratio, forcing the concrete to solidify as clumps develop. With time, carbon fiber improves the durability of concrete. Self-compacting concrete with no carbon fiber has a poor tensile strength. Experiments were conducted by adding carbon fiber at 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% by weight. Fresh concrete tests including slump test and L-box test, hardened concrete tests involving compressive strength and splitting tensile strength, and durability tests involving water absorption and acid attack test were conducted. Embodied carbon ratios were calculated for all of the mix ratios and decreasing impact, in the form of eco-strength efficiency, is observed with changes in the addition of carbon fiber in concrete. From the testing results, it is evident that 0.6% carbon fiber is the ideal proportion for increasing compressive strength and split tensile strength by 20.93% and 59%, respectively, over the control mix. Response Surface Methodology (RSM) is then applied to develop a model based on results of extensive experimentation. Optimization of the model is performed and final modelled equations are provided in terms of calculating the impact of addition of carbon fiber in concrete. Positive implications are devised for the development of concrete in the future involving carbon fiber. Full article

Recently, convolutional neural networks (CNNs) have become the main drivers in many image recognition applications. However, they are vulnerable to adversarial attacks, which can lead to disastrous consequences. This paper introduces ShuffleDetect as a new and efficient unsupervised method for the detection of adversarial images against trained convolutional neural networks. Its main feature is to split an input image into non-overlapping patches, then swap the patches according to permutations, and count the number of permutations for which the CNN classifies the unshuffled input image and the shuffled image into different categories. The image is declared adversarial if and only if the proportion of such permutations exceeds a certain threshold value. A series of 8 targeted or untargeted attacks was applied on 10 diverse and state-of-the-art ImageNet-trained CNNs, leading to 9500 relevant clean and adversarial images. We assessed the performance of ShuffleDetect intrinsically and compared it with another detector. Experiments show that ShuffleDetect is an easy-to-implement, very fast, and near memory-free detector that achieves high detection rates and low false positive rates. Full article

The accelerated advancement of industrialization, urbanization, and technology produces an enormous amount of waste materials that are channelled into the environment, contaminating the soil, water and air. This exceedingly large volume of waste in the planet’s environment has made it challenging and difficult to handle; thus, it is urgent to facilitate alternative methods of waste disposal. Moreover, the consumption of concrete raw materials increases as a consequence of a sudden increase in concrete usage. In this study, printed circuit boards (PCB), cutting waste (e-waste) (0%, 5%, 10%, 15%, 20%) and recycled concrete aggregate (construction and demolition waste) (0%, 20%, 40%, 60%, 80%, 100%) replace the fine and coarse aggregate; this is utilised in the making of self-compacting concrete (SCC). To mitigate the impact of shrinkage and micro-cracks produced during loading, synthetic fibres (polypropylene fibres) (0%, 0.25%, 0.5%, 0.75%, 1%) are incorporated into the dense matrix of concrete. Based on the experiments conducted, it is concluded that the optimum percentages of e-waste, recycled aggregate and synthetic fibres are 10%, 60% and 0.5%, respectively. It is proposed to use response surface methodology for the statistical modelling of fibre-reinforced self-compacting concrete (FRSCC) ingredients, which will diminish the number of experiments conducted during optimisation. Experimental optimisation of ingredients was carried out by determining the workability properties (slump flow, L-Box, V-Funnel and Sieve test), strength properties (compressive, split tensile, flexural at 7, 14, 28 days of curing) and durability properties against chemical exposure (sulphuric and hydrochloric acid attack, sulphate attack at 29 and 90 days of immersion). In the statistical optimisation process, the central composite design (CCD) is utilised, and it is concluded that the optimum percentages of e-waste, recycled aggregate and synthetic fibres are 9.90%, 51.35% and 0.503%, respectively, as these produce a compressive strength (CS) of 47.02 MPa at the end of the 28th day of curing, whereas FRSCC created with experimentally optimised ingredients shows a strength of 46.79 MPa with the use of 60% of recycled aggregate, 10% of e-waste and 0.5% polypropylene fibre. Hence, it is observed that the CCD-optimised ingredients were the optimum dosage of ingredients based on the compressive strength values at 28 days. It is concluded that the FRSCC specimens created with CCD-optimised parameters show better resistance against loading and chemical exposure, as these show minimum weight and strength loss when compared to FRSCC with experimentally optimised parameters. Full article

Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism. The proposed CAD system has the capability to recognize breast cancer in two scenarios: Scenario A (Binary classification) and Scenario B (Multi-classification). Data collection and preprocessing, patch image creation and splitting, and artificial intelligence-based breast lesion identification are all components of the execution framework that are applied consistently across both cases. The effectiveness of the proposed AI model is compared against three separate deep learning models: a custom CNN, the VGG16, and the ResNet50. Two datasets, CBIS-DDSM and DDSM, are utilized to construct and test the proposed CAD system. Five-fold cross validation of the test data is used to evaluate the accuracy of the performance results. The suggested hybrid CAD system achieves encouraging evaluation results, with overall accuracies of 100% and 95.80% for binary and multiclass prediction challenges, respectively. The experimental results reveal that the proposed hybrid AI model could identify benign and malignant breast tissues significantly, which is important for radiologists to recommend further investigation of abnormal mammograms and provide the optimal treatment plan. Full article

Cloud storage services are an important application of cloud computing. An increasing number of data owners store their data on cloud platforms. Since cloud platforms are far away from users, data security and privacy protection are very important issues that need to be addressed. Identity-based broadcast encryption (IBBE) is an important method to provide security and privacy protection for cloud storage services. Because the side channel attacks may lead to the disclosure of the key information of the cryptographic system, which will damage the security of the system, this paper provides an identity-based broadcast encryption with leakage resilience by state partition (LR-SP-IBBE). By using a binary extractor to compensate for the loss in entropy of the symmetric key caused by side-channel attacks, the proposed scheme randomizes the encapsulated symmetric key. Furthermore, using a state partition technique, we split the private key into two parts, and the corresponding decryption was divided into two stages. Through the double-system encryption skill, the security and leakage-resilience were proved in the composite order group model. Full article

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber–physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information. Full article

Measurement-device-independent quantum key distribution (MDI-QKD) is innately immune to all detection-side attacks. Due to the limitations of technology, most MDI-QKD protocols use weak coherent photon sources (WCPs), which may suffer from a photon-number splitting (PNS) attack from eavesdroppers. Therefore, the existing MDI-QKD protocols also need the decoy-state method, which can resist PNS attacks very well. However, the existing decoy-state methods do not attend to the existence of PNS attacks, and the secure keys are only generated by single-photon components. In fact, multiphoton pulses can also form secure keys if we can confirm that there is no PNS attack. For simplicity, we only analyze the weaker version of a PNS attack in which a legitimate user’s pulse count rate changes significantly after the attack. In this paper, under the null hypothesis of no PNS attack, we first determine whether there is an attack or not by retrieving the missing information of the existing decoy-state MDI-QKD protocols via statistical hypothesis testing, extract a normal distribution statistic, and provide a detection method and the corresponding Type I error probability. If the result is judged to be an attack, we use the existing decoy-state method to estimate the secure key rate. Otherwise, all pulses with the same basis leading to successful Bell state measurement (BSM) events including both single-photon pulses and multiphoton pulses can be used to generate secure keys, and we give the formula of the secure key rate in this case. Finally, based on actual experimental data from other literature, the associated experimental results (e.g., the significance level is 5%) show the correctness of our method. Full article

Among the most significant problems that almost any blockchain faces are the problems of increasing its throughput (i.e., the number of transactions per unit of time) and the problem of a long waiting time before block confirmation. Thus, for example, in the most common BTC blockchain, according to various estimates, throughput is from 3 to 7 tps (transactions per second), and the average block confirmation time (block is considered confirmed if it has at least 6 blocks over it) is 1 h. At the same time, it is impossible to solve these problems directly by increasing the block size or increasing block generation intensity because this leads to essentially a decrease in the security of the blockchain in the first turn against double spend and splitting attacks. Such problems lead to the inconvenience of the practical use of cryptocurrencies to pay for goods and services. Proposed a few years ago, the PoP consensus protocol potentially helps to solve the problem of increasing blockchain throughput, although it was originally intended to ensure the stability of “young” blockchains, with “small” PoW, through the use of a secure blockchain, such as BTC. A blockchain that has provable security is called the security-provided blockchain (SPB), and one that uses SPB to achieve its security is called the security-inherited blockchain. In this paper, we give explicit formulas which describe how the number of confirmation blocks in the security-inherited blockchain, which is sufficient to achieve a given security level of this blockchain to a double spend attack, depends on the parameters of both blockchains. It is essential that we use a realistic model to obtain the results, taking into account the synchronization times of both blockchains. Such a model is much closer to the real situation, but at the same time, it leads to significant analytical difficulties in obtaining results. The obtained formulas are convenient for numerical calculations, the numerous examples of which are also given in this work. Full article