Biblio

In order to better cultivate engineering and application-oriented cryptographic talents, it is urgent to establish a joint school enterprise cryptographic laboratory. However, there is a core problem in the existing school enterprise joint laboratory construction scheme: the enterprise is not specialized and has insufficient cooperation ability, which can not effectively realize the effective integration of resources and mutual benefit and win-win results. To solve this problem, we propose a comprehensive evaluation model of cooperative enterprises based on entropy weight method and grey correlation analysis. Firstly, the multi-level evaluation index system of the enterprise is established, and the entropy weight method is used to objectively weight the index. After that, the grey weighted correlation degree between each enterprise and the virtual optimal enterprise is calculated by grey correlation analysis to compare the advantages and disadvantages of enterprises. Through the example analysis, it is proved that our method is effective and reliable, eliminating subjective factors, and providing a certain reference value for the construction of school enterprise joint cryptographic laboratory.

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

Cloud computing is preferred because of its numerous improvements, such as data security, low maintenance cost, unlimited storage capacity and consistent backups. However, legitimate users take advantage of cloud storage services for storing a considerable amount of sensitive data. After storing data on the cloud, data users pass on control over data to cloud administrators. Although for assuring data security, sensitive information needs to be encrypted before deploying it on the cloud server. In traditional searchable encryption, encrypted data can be searched using keywords on a cloud server without knowing data details, and users can retrieve certain specific files of interest after authentication. However, the results are only related to the exact matching keyword searches. This drawback affects system usability and efficiency, due to which existing encryption methods are unsuitable in cloud computing. To avoid the above problems, this study includes as follows: Firstly, we analyze all fuzzy keyword search techniques that are wildcard based, gram based and trie-traverse. Secondly, we briefly describe certificateless cryptography and suggest a certificateless searchable encryption scheme. Finally, this study gives easy access to developing a fuzzy keyword searchable system for a new researcher to combine the above two points. It provides easy access and efficient search results.

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having color image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data.

The WSN nodes are arranged uniformly or randomly on the area of need for gathering the required data. The admin utilizes wireless broadband networks to connect to the Internet and acquire the required data from the base station (BS). However, these sensor nodes play a significant role in a variety of professional and industrial domains, but some of the concerns stop the growth of WSN, such as memory, transmission, battery power and processing power. The most significant issue with these restrictions is to increase the energy efficiency for WSN with rapid and trustworthy data transfer. In this designed model, the sensor nodes are clustered using the FCM (Fuzzy C-Means) clustering algorithm with the Reptile Search Optimization (RSO) for finding the centre of the cluster. The cluster head is determined by using African vulture optimization (AVO). For selecting the path of data transmission from the cluster head to the base station, the adaptive relay nodes are selected using the Fuzzy rule. These data from the base station are given to the server with a DNA cryptography encryption algorithm for secure data transmission. The performance of the designed model is evaluated with specific parameters such as average residual energy, throughput, end-to-end delay, information loss and execution time for a secure and energy-efficient routing protocol. These evaluated values for the proposed model are 0.91 %, 1.17Mbps, 1.76 ms, 0.14 % and 0.225 s respectively. Thus, the resultant values of the proposed model show that the designed clustering-based routing protocol using FCM-RSOA and DNA cryptography for smart building performs better compared to the existing techniques.

With the development of the digital development transformation of the power grid, the classification of power unstructured encrypted data is an important basis for data security protection. However, most studies focus on exact match classification or single-keyword fuzzy match classification. This paper proposes a fuzzy matching classification method for power unstructured encrypted data. The data owner generates an index vector based on the power unstructured file, and the data user generates a query vector by querying the file through the same process. The index and query vector are uploaded to the cloud server in encrypted form, and the cloud server calculates the relevance score and sorts it, and returns the classification result with the highest score to the user. This method realizes the multi-keyword fuzzy matching classification of unstructured encrypted data of electric power, and through the experimental simulation of a large number of data sets, the effect and feasibility of the method are proved.

Identity based digital signature is an important research topic of public key cryptography, which can effectively guarantee the authentication, integrity and unforgeability of data. In this paper, a new fuzzy multi-identity based signature scheme is proposed. It is proved that the scheme is existentially unforgeable against adaptively chosen message attack, and the security of the signature scheme can be reduced to CDH assumption. The storage cost and the communication overhead are small, therefore the new fuzzy multi-identity based signature (FMIBS) scheme can be implemented efficiently.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s \textbackslashrho and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

Currently, air pollution is still a problem that requires special attention, especially in big cities. Air pollution can come from motor vehicle fumes, factory smoke or other particles. To overcome these problems, a system is made that can monitor environmental conditions in order to know the good and bad of air quality in an environment and is expected to be a solution to reduce air pollution that occurs. The system created will utilize the Wireless Sensor Network (WSN) combined with Waspmote Smart Environment PRO, so that later data will be obtained in the form of temperature, humidity, CO levels and CO2 levels. From the sensor data that has been processed on Waspmote, it will then be used as input for data processing using a fuzzy algorithm. The classification obtained from sensor data processing using fuzzy to monitor environmental conditions there are 5 classifications, namely Very Good, Good, Average, Bad and Dangerous. Later the data that has been collected will be distributed to Meshlium as a gateway and will be stored in the database. The process of sending information between one party to another needs to pay attention to the confidentiality of data and information. The final result of the implementation of this research is that the system is able to classify values using fuzzy algorithms and is able to secure text data that will be sent to the database via Meshlium, and is able to display data sent to the website in real time.

Secrete message protection has become a focal point of the network security domain due to the problems of violating the network use policies and unauthorized access of the public network. These problems have led to data protection techniques such as cryptography, and steganography. Cryptography consists of encrypting secrete message to a ciphertext format and steganography consists of concealing the secrete message in codes that make up a digital file, such as an image, audio, and video. Steganography, which is different from cryptography, ensures hiding a secret message for secure transmission over the public network. This paper presents a steganographic approach using digital images for data hiding that aims to providing higher performance by combining fuzzy logic type I to pre-process the cover image and difference expansion techniques. The previous methods have used the original cover image to embed the secrete message. This paper provides a new method that first identifies the edges of a cover image and then proceeds with a difference expansion to embed the secrete message. The experimental results of this work identified an improvement of 10% of the existing method based on increased payload capacity and the visibility of the stego image.

Biometrics as a means of personal authentication has demonstrated strong viability in the past decade. However, directly deriving a unique cryptographic key from biometric data is a non-trivial task due to the fact that biometric data is usually noisy and presents large intra-class variations. Moreover, biometric data is permanently associated with the user, which leads to security and privacy issues. Cancellable biometrics and bio-cryptosystem are two main branches to address those issues, yet both approaches fall short in terms of accuracy performance, security, and privacy. In this paper, we propose a Bio-Crypto system on fingerprint Cancellable template (Bio-CanCrypto), which bridges cancellable biometrics and bio-cryptosystem to achieve a middle-ground for alleviating the limitations of both. Specifically, a cancellable transformation is applied on a fixed-length fingerprint feature vector to generate cancellable templates. Next, an LDPC coding mechanism is introduced into a reusable fuzzy extractor scheme and used to extract the stable cryptographic key from the generated cancellable templates. The proposed system can achieve both cancellability and reusability in one scheme. Experiments are conducted on a public fingerprint dataset, i.e., FVC2002. The results demonstrate that the proposed LDPC coded reusable fuzzy extractor is effective and promising.

Nowadays, biometric is a most technique to authenticate /identify human been, because its resistance against theft, loss or forgetfulness. However, biometric is subject to different transmission attacks. Today, the protection of the sensitive biometric information is a big challenge, especially in current wireless networks such as internet of things where the transmitted data is easy to sniffer. For that, this paper proposes an Eigens-Fingerprint-based biometric cryptosystem, where the biometric feature vectors are extracted by the Principal Component Analysis technique with an appropriate quantification. The key-binding principle incorporated with bit-wise and byte-wise correcting code is used for encrypting data and sharing key. Several recognition rates and computation time are used to evaluate the proposed system. The findings show that the proposed cryptosystem achieves a high security without decreasing the accuracy.

Modern malware indicators utilized by the current top threat feeds are easily bypassed and generated through enigmatic methods, leading to a lack of detection capabilities for cyber defenders. Static hash-based algorithms such as MD5 or SHA generate indicators that are rendered obsolete by modifying a single byte of the source file. Conversely, fuzzy hash-based algorithms such as SSDEEP and TLSH are more robust to alterations of source information; however, these methods often utilize context boundaries that are hard to define or not based on meaningful information. In previous work, a custom binary analysis tool was created called @DisCo. In this study, four current ransomware campaigns were analyzed using TLSH fuzzy hashing and the @DisCo tool. While TLSH works on the binary level of the entire program, @DisCo works at an intermediate function level. The results from each analysis method were compared to provide validation between the two as well as introduce a narrative for using combinations of these types of methods for the creation of stronger indicators of compromise.

Nowadays, biometric-based authentication systems are widely used. This fact has led to increased attacks on biometric data of users. Therefore, biometric template protection is sure to keep the attention of researchers for the security of the authentication systems. Many previous works proposed the biometric template protection schemes by transforming the original biometric data into a secure domain, or establishing a cryptographic key with the use of biometric data. The main purpose was that fulfill the all three requirements: cancelability, security, and performance as many as possible. In this paper, using random projection merged with fuzzy commitment, we will introduce a hybrid scheme of biometric template protection. We try to limit their own drawbacks and take full advantages of these techniques at the same time. In addition, an analysis of non-invertibility property will be exercised with regards to the use of random projection aiming at enhancing the security of the system while preserving the discriminability of the original biometric template.

Numerous prevalent techniques build a Multi-Modal Biometric (MMB) system that struggles in offering security and also revocability onto the templates. This work proffered a MMB system centred on the Modulus Fuzzy Vault (MFV) aimed at resolving these issues. The methodology proposed includes Fingerprint (FP), Palmprint (PP), Ear and also Retina images. Utilizing the Boosted Double Plateau Histogram Equalization (BDPHE) technique, all images are improved. Aimed at removing the unnecessary things as of the ear and the blood vessels are segmented as of the retina images utilizing the Modified Balanced Iterative Reducing and Clustering using Hierarchy (MBIRCH) technique. Next, the input traits features are extracted; then the essential features are chosen as of the features extracted utilizing the Bidirectional Deer Hunting optimization Algorithm (BDHOA). The features chosen are merged utilizing the Normalized Feature Level and Score Level (NFLSL) fusion. The features fused are saved securely utilizing Modulus Fuzzy Vault. Upto fusion, the procedure is repeated aimed at the query image template. Next, the de-Fuzzy Vault procedure is executed aimed at the query template, and then the key is detached by matching the query template’s and input biometric template features. The key separated is analogized with the threshold that categorizes the user as genuine or else imposter. The proposed BDPHE and also MFV techniques function efficiently than the existent techniques.

Physical unclonable functions (PUFs) are used to create unique device identifiers from their inherent fabrication variability. Unstable readings and variation of the PUF response over time are key issues that limit the applicability of PUFs in real-world systems. In this project, we developed a fuzzy extractor (FE) to generate robust cryptographic keys from ReRAM-based PUFs. We tested the efficiency of the proposed FE using BCH and Polar error correction codes. We use ReRAM-based PUFs operating in pre-forming range to generate binary cryptographic keys at ultra-low power with an objective of tamper sensitivity. We investigate the performance of the proposed FE with real data using the reading of the resistance of pre-formed ReRAM cells under various noise conditions. The results show a bit error rate (BER) in the range of 10−5 for the Polar-codes based method when 10% of the ReRAM cell array is erroneous at Signal to Noise Ratio (SNR) of 20dB.This error rate is achieved by using helper data length of 512 bits for a 256 bit cryptographic key. Our method uses a 2:1 ratio for helper data and key, much lower than the majority of previously reported methods. This property makes our method more robust against helper data attacks.

The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use. Out of fifteen criteria, security soundness, robustness and hardware failure distinguished as significant ones.

The ternary interval number contains more comprehensive information than the exact number, and the prediction of the ternary interval number is more conducive to intelligent decision-making. In order to reduce the overfitting problem of the neural network model, a combination prediction method of the BP neural network and the matrix GM (1, 1) model for the ternary interval number sequence is proposed in the paper, and based on the proposed method to predict the passenger volume. The matrix grey model for the ternary interval number sequence (MTIGM (1, 1)) can stably predict the overall development trend of a time series. Considering the integrity of interval numbers, the BP neural network model is established by combining the lower, middle and upper boundary points of the ternary interval numbers. The combined weights of MTIGM (1, 1) and the BP neural network are determined based on the grey relational degree. The combined method is used to predict the total passenger volume and railway passenger volume of China, and the prediction effect is better than MTIGM (1, 1) and BP neural network.

Emerging technologies change the qualities of modern healthcare by employing smart systems for patient monitoring. To well use the data surrounding the patient, tiny sensing devices and smart gateways are involved. These sensing systems have been used to collect and analyze the real-time data remotely in Internet of Medical Thinks (IoM). Since the patient sensed information is so sensitive, the security and privacy of medical data are becoming challenging problem in IoM. It is then important to ensure the security, privacy and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for the IoM. In this paper, in order to improve the authentication and communications in health care applications, we present a novel secure and anonymous authentication scheme. We will use elliptic curve cryptography (ECC) with random numbers generated by fuzzy logic. We simulate IoM scheme using network simulator 3 (NS3) and we employ optimized link state routing protocol (OLSR) algorithm and ECC at each node of the network. We apply some attack algorithms such as Pollard’s ρ and Baby-step Giant-step to evaluate the vulnerability of the proposed scheme.

The mechanism of Fog computing is a distributed infrastructure to provide the computations as same as cloud computing. The fog computing environment provides the storage and processing of data in a distributed manner based on the locality. Fog servicing is better than cloud service for working with smart devices and users in a same locale. However the fog computing will inherit the features of the cloud, it also suffers from many security issues as cloud. One such security issue is authentication with efficient key management between the communicating entities. In this paper, we propose a secured two-way authentication scheme with efficient management of keys between the user mobile device and smart devices under the control of the fog server. We made use of operations such as one-way hash (SHA-512) functions, bitwise XOR, and fuzzy extractor function to make the authentication system to be better. We have verified the proposed scheme for its security effectiveness by using a well-used analysis tool ProVerif. We also proved that it can resist multiple attacks and the security overhead is reduced in terms of computation and communication cost as compared to the existing methods.

As a collection of innovative technologies, blockchain has solved the problem of reliable transmission and exchange of information on untrusted networks. The underlying implementation is the basis for the reliability of blockchain, which consists of various cryptographic algorithms for the use of identity authentication and privacy protection of distributed ledgers. The cryptographic algorithm plays a vital role in the blockchain, which guarantees the confidentiality, integrity, verifiability and non-repudiation of the blockchain. In order to get the most suitable cryptographic algorithm for the blockchain system, this paper proposed a method using Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and score the comprehensive performance of the three types of cryptographic algorithms applied in the blockchain, including symmetric cryptographic algorithms, asymmetric cryptographic algorithms and hash algorithms. This paper weighs the performance differences of cryptographic algorithms considering the aspects of security, operational efficiency, language and hardware support and resource consumption. Finally, three cryptographic algorithms are selected that are considered to be the most suitable ones for block-chain systems, namely ECDSA, sha256 and AES. This result is also consistent with the most commonly used cryptographic algorithms in the current blockchain development direction. Therefore, the reliability and practicability of the algorithm evaluation pro-posed in this paper has been proved.

The fuzzy query scheme based on vector index uses Bloom filter to construct vector index for key words. Then the statistical attack based on the deviation of frequency distribution of the vector index brings out the sensitive information disclosure. Using the noise vector, a fuzzy query scheme resistant to the statistical attack serving for encrypted database, i.e. S-BF, is introduced. With the noise vector to clear up the deviation of frequency distribution of vector index, the statistical attacks to the vector index are resolved. Demonstrated by lab experiment, S-BF scheme can achieve the secure fuzzy query with the powerful privation protection capability for encrypted cloud database without the loss of fuzzy query efficiency.

Nowadays, physical tampering and counterfeiting of electronic devices are still an important security problem and have a great impact on large-scale and distributed applications, such as Internet-of-Things. Physical Unclonable Functions (PUFs) have the potential to be a fundamental means to guarantee intrinsic hardware security, since they promise immunity against most of known attack models. However, inner nature of PUF circuits hinders a wider adoption since responses turn out to be noisy and not stable during time. To overcome this issue, most of PUF implementations require a fuzzy extraction scheme, able to recover responses stability by exploiting error correction codes (ECCs). In this paper, we propose a Reed-Muller (RM) ECC design, meant to be embedded into a fuzzy extractor, that can be efficiently configured in terms of area/delay constraints in order to get reliable responses from PUFs. We provide implementation details and experimental evidences of area/delay efficiency through syntheses on medium-range FPGA device.

When building the architecture of cyber defense systems, one of the important tasks is to create a methodology for current diagnostics of cybersecurity status of information systems and objects of information activity. The complexity of this procedure is that having a strong security level of the object at the software level does not mean that such power is available at the hardware level or at the cryptographic level. There are always weaknesses in all levels of information security that criminals are constantly looking for. Therefore, the task of promptly calculating the likelihood of possible negative consequences from the successful implementation of cyberattacks is an urgent task today. This paper proposes an approach of obtaining an instantaneous calculation of the probabilities of negative consequences from the successful implementation of cyberattacks on objects of information activity on the basis of delayed differential equation theory and the mechanism of constructing a logical Fuzzy function. This makes it possible to diagnose the security status of the information system.

Research and Development, and information management professionals routinely employ simple keyword searches or more complex Boolean queries when using databases such as PubMed and Ovid and search engines like Google to find the information they need. While satisfying the basic needs of the researcher, basic search is limited which can adversely affect both precision and recall, decreasing productivity and damaging the researchers' ability to discover new insights. The cloud service providers who store user's data may access sensitive information without any proper authority. A basic approach to save the data confidentiality is to encrypt the data. Data encryption also demands the protection of keyword privacy since those usually contain very vital information related to the files. Encryption of keywords protects keyword safety. Fuzzy keyword search enhances system usability by matching the files perfectly or to the nearest possible files against the keywords entered by the user based on similar semantics. Encrypted keyword search in cloud using this logic provides the user, on entering keywords, to receive best possible files in a more secured manner, by protecting the user's documents.