Biblio

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.

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.

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.

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

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.

Physical Unclonable Functions (PUFs) are considered as an attractive low-cost security anchor. The unique features of PUFs are dependent on the Nanoscale variations introduced during the manufacturing variations. Most PUFs exhibit an unreliability problem due to aging and inherent sensitivity to the environmental conditions. As a remedy to the reliability issue, helper data algorithms are used in practice. A helper data algorithm generates and stores the helper data in the enrollment phase in a secure environment. The generated helper data are used then for error correction, which can transform the unique feature of PUFs into a reproducible key. The key can be used to encrypt secret data in the security scheme. In contrast, this work shows that the fuzzy PUFs can be used to secret important data directly by an error-tolerant protocol without the enrollment phase and error-correction algorithm. In our proposal, the secret data is locked in a vault leveraging the unique fuzzy pattern of PUF. Although the noise exists, the data can then be released only by this unique PUF. The evaluation was performed on the most prominent intrinsic PUF - DRAM PUF. The test results demonstrate that our proposal can reach an acceptable reconstruction rate in various environment. Finally, the security analysis of the new proposal is discussed.

Ransomware is currently one of the most significant cyberthreats to both national infrastructure and the individual, often requiring severe treatment as an antidote. Triaging ran-somware based on its similarity with well-known ransomware samples is an imperative preliminary step in preventing a ransomware pandemic. Selecting the most appropriate triaging method can improve the precision of further static and dynamic analysis in addition to saving significant t ime a nd e ffort. Currently, the most popular and proven triaging methods are fuzzy hashing, import hashing and YARA rules, which can ascertain whether, or to what degree, two ransomware samples are similar to each other. However, the mechanisms of these three methods are quite different and their comparative assessment is difficult. Therefore, this paper presents an evaluation of these three methods for triaging the four most pertinent ransomware categories WannaCry, Locky, Cerber and CryptoWall. It evaluates their triaging performance and run-time system performance, highlighting the limitations of each method.

Data privacy is a very important problem to address while sharing data among multiple organizations and has become very crucial in the health sectors since multiple organizations such as hospitals are storing data of patients in the form of Electronic Health Records. Stored data is used with other organizations or research analysts to improve the health care of patients. However, the data records contain sensitive information such as age, sex, and date of birth of the patients. Revealing sensitive data can cause a privacy breach of the individuals. This has triggered research that has led to many different privacy preserving techniques being introduced. Thus, we designed a technique that not only encrypts / hides the sensitive information but also sends the data to different organizations securely. To encrypt sensitive data we use different fuzzy logic membership functions. We then use an autoencoder neural network to send the modified data. The output data of the autoencoder can then be used by different organizations for research analysis.

Threat actors are constantly seeking new attack surfaces, with ransomeware being one the most successful attack vectors that have been used for financial gain. This has been achieved through the dispersion of unlimited polymorphic samples of ransomware whilst those responsible evade detection and hide their identity. Nonetheless, every ransomware threat actor adopts some similar style or uses some common patterns in their malicious code writing, which can be significant evidence contributing to their identification. he first step in attempting to identify the source of the attack is to cluster a large number of ransomware samples based on very little or no information about the samples, accordingly, their traits and signatures can be analysed and identified. T herefore, this paper proposes an efficient fuzzy analysis approach to cluster ransomware samples based on the combination of two fuzzy techniques fuzzy hashing and fuzzy c-means (FCM) clustering. Unlike other clustering techniques, FCM can directly utilise similarity scores generated by a fuzzy hashing method and cluster them into similar groups without requiring additional transformational steps to obtain distance among objects for clustering. Thus, it reduces the computational overheads by utilising fuzzy similarity scores obtained at the time of initial triaging of whether the sample is known or unknown ransomware. The performance of the proposed fuzzy method is compared against k-means clustering and the two fuzzy hashing methods SSDEEP and SDHASH which are evaluated based on their FCM clustering results to understand how the similarity score affects the clustering results.

The emergence of the Internet of Things with its constraints obliges researchers in this field to find light and accurate solutions to secure the data exchange. This document presents secure authentication using biometrics coupled with an effective key agreement scheme to save time and energy. In our scheme, the agreed key is used to encrypt transmission data between different IoT actors. While the fuzzy extractor based on the fuzzy vault principle, is used as authentication and as key agreement scheme. Besides, our system incorporates the Reed Solomon and Hamming codes to give some tolerance to errors. The experimental results have been discussed according to several recognition rates and computation times. Indeed, the recognition rate results have been compared to other works to validate our system. Also, we clarify how our system resists to specific transmission attacks without affecting lightness and accuracy.

Malware has remained a consistent threat since its emergence, growing into a plethora of types and in large numbers. In recent years, numerous new malware variants have enabled the identification of new attack surfaces and vectors, and have become a major challenge to security experts, driving the enhancement and development of new malware analysis techniques to contain the contagion. One of the preliminary steps of malware analysis is to remove the abundance of counterfeit malware samples from the large collection of suspicious samples. This process assists in the management of man and machine resources effectively in the analysis of both unknown and likely malware samples. Hashing techniques are one of the fastest and efficient techniques for performing this preliminary analysis such as fuzzy hashing and import hashing. However, both hashing methods have their limitations and they may not be effective on their own, instead the combination of two distinctive methods may assist in improving the detection accuracy and overall performance of the analysis. This paper proposes a Fuzzy-Import hashing technique which is the combination of fuzzy hashing and import hashing to improve the detection accuracy and overall performance of malware analysis. This proposed Fuzzy-Import hashing offers several benefits which are demonstrated through the experimentation performed on the collected malware samples and compared against stand-alone techniques of fuzzy hashing and import hashing.

Cipher Text Policy Attribute Based Encryption which is a form of Public Key Encryption has become a renowned approach as a Data access control scheme for data security and confidentiality. It not only provides the flexibility and scalability in the access control mechanisms but also enhances security by fuzzy fined-grained access control. However, schemes are there which for more security increases the key size which ultimately leads to high encryption and decryption time. Also, there is no provision for handling the middle man attacks during data transfer. In this paper, a light-weight and more scalable encryption mechanism is provided which not only uses fewer resources for encoding and decoding but also improves the security along with faster encryption and decryption time. Moreover, this scheme provides an efficient key sharing mechanism for providing secure transfer to avoid any man-in-the-middle attacks. Also, due to fuzzy policies inclusion, chances are there to get approximation of user attributes available which makes the process fast and reliable and improves the performance of legitimate users.

Blockchain technology is attracting attention as an innovative system for decentralized payments in fields such as financial area. On the other hand, in a decentralized environment, management of a secret key used for user authentication and digital signature becomes a big issue because if a user loses his/her secret key, he/she will also lose assets on the blockchain. This paper describes the secret key management issues in blockchain systems and proposes a solution using a biometrics-based digital signature scheme. In our proposed system, a secret key to be used for digital signature is generated from the user's biometric information each time and immediately deleted from the memory after using it. Therefore, our blockchain system has the advantage that there is no need for storage for storing secret keys throughout the system. As a result, the user does not have a risk of losing the key management devices and can prevent attacks from malware that steals the secret key.

We are answering the question whenever systems with convolutional neural network classifier trained over plain and encrypted data keep the ordering according to accuracy. Our motivation is need for designing convolutional neural network classifiers when data in their plain form are not accessible because of private company policy or sensitive data gathered by police. We propose to use a combination of fully connected autoencoder together with a convolutional neural network classifier. The autoencoder transforms the data info form that allows the convolutional classifier to be trained. We present three experiments that show the ordering of systems over plain and encrypted data. The results show that the systems indeed keep the ordering, and thus a NN designer can select appropriate architecture over encrypted data and later let data owner train or fine-tune the system/CNN classifier on the plain data.

Due to the decentralization, irreversibility, and traceability, blockchain has attracted significant attention and has been deployed in many critical industries such as banking and logistics. However, the micro-architecture characteristics of blockchain programs still remain unclear. What's worse, the large number of micro-architecture events make understanding the characteristics extremely difficult. We even lack a systematic approach to identify the important events to focus on. In this paper, we propose a novel benchmarking methodology dubbed BBS to characterize blockchain programs at micro-architecture level. The key is to leverage fuzzy set theory to identify important micro-architecture events after the significance of them is quantified by a machine learning based approach. The important events for single programs are employed to characterize the programs while the common important events for multiple programs form an importance vector which is used to measure the similarity between benchmarks. We leverage BBS to characterize seven and six benchmarks from Blockbench and Caliper, respectively. The results show that BBS can reveal interesting findings. Moreover, by leveraging the importance characterization results, we improve that the transaction throughput of Smallbank from Fabric by 70% while reduce the transaction latency by 55%. In addition, we find that three of seven and two of six benchmarks from Blockbench and Caliper are redundant, respectively.

Recently, new perspective areas of chaotic encryption have evolved, including fuzzy logic encryption. The presented work proposes an image encryption system based on two chaotic mapping that uses fuzzy logic. The paper also presents numerical calculations of some parameters of statistical analysis, such as, histogram, entropy of information and correlation coefficient, which confirm the efficiency of the proposed algorithm.

YARA rules utilises string or pattern matching to perform malware analysis and is one of the most effective methods in use today. However, its effectiveness is dependent on the quality and quantity of YARA rules employed in the analysis. This can be managed through the rule optimisation process, although, this may not necessarily guarantee effective utilisation of YARA rules and its generated findings during its execution phase, as the main focus of YARA rules is in determining whether to trigger a rule or not, for a suspect sample after examining its rule condition. YARA rule conditions are Boolean expressions, mostly focused on the binary outcome of the malware analysis, which may limit the optimised use of YARA rules and its findings despite generating significant information during the execution phase. Therefore, this paper proposes embedding fuzzy rules with YARA rules to optimise its performance during the execution phase. Fuzzy rules can manage imprecise and incomplete data and encompass a broad range of conditions, which may not be possible in Boolean logic. This embedding may be more advantageous when the YARA rules become more complex, resulting in multiple complex conditions, which may not be processed efficiently utilising Boolean expressions alone, thus compromising effective decision-making. This proposed embedded approach is applied on a collected malware corpus and is tested against the standard and enhanced YARA rules to demonstrate its success.

This paper proposes a novel extension of blockchain systems with fuzzy ontologies. The main advantage is to let the users have flexible restrictions, represented using fuzzy sets, and to develop smart contracts where there is a partial agreement among the involved parts. We propose a general architecture based on four fuzzy ontologies and a process to develop and run the smart contracts, based on a reduction to a well-known fuzzy ontology reasoning task (Best Satisfiability Degree). We also investigate different operators to compute Pareto-optimal solutions and implement our approach in the Ethereum blockchain.

The internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme's performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.

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.

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.

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.

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.