Encryption Method Research Articles

Encryption techniques for financial data security in fintech applications

In the dynamic world of financial technology (Fintech), securing financial data is a key priority. Increasing digital connectivity, adoption of cloud-based services requiring complex measures to protect the integrity, privacy and availability of sensitive information. Encryption techniques are emerging as a key tool to achieve these goals about itself by converting plaintext into ciphertext, protected from unauthorized access and probability violations. This review paper examines the various encryption techniques required to secure financial information in fintech applications. The main methods described include symmetric encryption, asymmetric and hybrid encryption techniques. Additionally, the function of end-to-end encryption (E2EE) is discussed in terms of protecting data privacy while it is being sent, which is essential for safeguarding sensitive financial activities such as mobile banking and digital payments. With its sophisticated method of permitting calculations on encrypted data without the need for decryption, homomorphic encryption shows promise for facilitating safe data analysis in Fintech settings while preserving data confidentiality. Each encryption method is scrutinized in terms of its strengths, weaknesses and practical applications in Fintech. Considerations such as computing efficiency, scalability, and regulatory compliance are addressed to provide insights for optimizing data protection strategies while adhering to industry standards and regulatory frameworks. The future of Fintech security is expected to be shaped by new developments in encryption technology, including post-quantum cryptography, artificial intelligence integration for adaptive security measures, and privacy-preserving solutions. The goal of these advancements is to strengthen the robustness of financial data security techniques in an increasingly linked digital world while mitigating changing cyber risks.

Design of Protection Software Using Some Cryptosystems for Cloud Database Files

The security of cloud databases may be a basic concern for organizations as they store and manage expansive volumes of delicate information within the cloud. Cryptosystems played an essential part in securing information privacy and integrity in these situations. This paper gives an in-depth investigation of the utilization of cryptosystems for cloud database security. The paper starts by talking about the foundation and importance of cloud database security, highlighting the expanding requirement for strong information security measures. A comprehensive study reviewed and investigated the current state of research in this space, distinguishing key progressions and challenges. The paper at that point digs into the execution of cryptosystems in cloud databases, looking at their execution and trade-offs. Besides, rising patterns and advances for improving cloud database security, such as multi-factor confirmation, homomorphic encryption, and blockchain, are discussed. The paper moreover recognizes different inquiries about openings and future headings within the field, counting various encryption methods, execution optimization, key administration, convenience enhancements, compliance contemplations, and integration with rising advances. Furthermore, we proposed encryption software for file protection, the software designed based on symmetric and asymmetric cryptosystems. The proposed software can be adapted for file protection in cloud database security. In conclusion, the paper emphasizes the significance of cryptosystems for cloud database security and underscores the requirement for continuous inquiry about and collaboration to address challenges and drive development in this zone.

QR Code-Based Attendance System for Contact Tracking Post-Pandemic

The COVID-19 pandemic has brought about profound changes in daily life and introduced new challenges to public health maintenance. Despite ongoing uncertainties, with certain regions easing or completely lifting community activity restrictions, persistent concerns about the virus's continued threat prompt the need for robust health monitoring measures. In this context, the use of contact tracing apps becomes pivotal for organizing individuals' movements, monitoring social distancing, and ensuring adherence to health protocols. This study introduces a QR code-based attendance system, a meticulously designed web and Android application aimed at efficiently and accurately tracking individuals' whereabouts. The system leverages QR code scanning technology, and to enhance security, employs the Advanced Encryption Standard (AES) method for data encryption. This ensures the safe encryption of sensitive data, preserving its confidentiality and integrity during transmission and storage. The research outcome is a versatile application facilitating seamless access across diverse locations, allowing real-time tracking of individuals' presence. This capability proves crucial for contact tracing efforts in the event of positive cases, contributing to the implementation of post-pandemic security and surveillance policies. The system's design and features align with the evolving landscape of the pandemic, emphasizing adaptability and comprehensive support for public health initiatives.

The Evolution of Ransomware: Tactics, Techniques, and Mitigation Strategies

Ransomware has developed into one of the most dangerous cyber threats. It is developed for the purpose of encrypting the data, and the owner is to pay the specified amount of money for the decoder. From simple rudimentary to developed sort with advanced encryption methods and modern distribution techniques, this research article is a complete historical journey of ransomware. Some of the specific aspects of ransomware development include the process of how the attackers act, starting from the traditional approach of spraying the malware to another strategic approach of singling out industries and organizations for attacks.The article also analyzes how the world came to know about ransomware-as-a-service (RaaS). The analysts have attributed the docket to the fact that it has also made it easy for inexperienced and unskilled hackers to embrace the commodity space by merely launching ransomware attacks. Current ransomware strategies that hackers employ include double extortion, where they steal information and threaten to publish the stolen data publicly, besides demanding a ransom for the encrypted information. The use of coins in the payment of ransom is examined with regard to their anonymity and the emergence of targeting of key infrastructure and other giant entities.Tactics that can allow for minimizing the attacks’ impact are equally crucial in the fight against ransomware. Some of the extensive strategies highlighted in this article are basic cyber hygiene measures, backup and restore methodologies, and endpoint detection and prevention measures. Implementing frequent training exercises to sensitize users to the risks of ransomware attacks is underlined, as is the need to have a clearly outlined incident response procedure in case of infections to enable the speedy handling of the attacks.This paper argues that an ability to comprehend the complexity of today’s ransomware, along with multiple layers of a defense system, will help organizations improve their readiness against this adaptive and continuous threat. This paper contains an analysis of the current state of ransomware and measures that need to be taken to curb the threat posed by this cyber threat; thus, it can serve as a valuable resource to cybersecurity experts.

Audio encryption framework based on chaotic map and DNA encoding

In this work, an audio encryption framework is proposed based on the chaos theory and DNA encoding. Traditional encryption algorithms designed for text data might not be efficient for handling the bulk data of audio files, leading to slow processing times and increased storage requirements. Striking the right balance between robust encryption and efficient processing is crucial. Additionally, some encryption methods can introduce noise or artifacts into the audio signal, impacting its clarity. Maintaining high-fidelity audio quality while ensuring strong encryption remains an ongoing area of research. Despite these challenges, the need for secure audio communication is undeniable. The RCM chaotic map is used to introduce chaotic properties in the encryption framework. A novel pseudorandom bit generator approach is proposed and used for encryption purposes. The proposed work is a symmetric encryption approach and is completely lossless. The proposed approach uses some lightweight computational procedures that make the algorithm simple and computationally less expensive which makes it suitable for real-time applications. Furthermore, the proposed approach shows significant performance and resilience against various cryptographic attacks. A new scrambling algorithm is proposed to add a layer of security. The proposed scramble algorithm uses logistic map beside the RCM map. The proposed approach achieved 98.28 % NSCR value and 33.63 % UACI values on average. The experimental outcomes are encouraging for practical applications of the proposed approach.

Ransomware Early Detection using Machine Learning Approach and Pre-Encryption Boundary Identification

The escalating ransomware threat has catalysed the formation of a sophisticated network of cybercriminal enterprises. Addressing this issue, our research provides a detailed exploration of the ransomware menace and an evaluation of contemporary detection methodologies. A successful ransomware attack leverages many factors: robust encryption methods that defy decryption, the anonymity of cyber currencies, and the widespread availability of ransomware kits that enable even inexperienced actors to launch attacks. Such dynamics have cultivated a niche for cybercriminal specialists in the digital underworld. In response to these challenges, our study proposes a detection framework based on machine learning, a domain where regression algorithms have gained popularity without yielding a definitive protective model. We employ API call analysis as the foundation to assess various machine learning classifiers' efficiency in identifying ransomware. The evaluation demonstrates that the Naive Bayes classifier underperforms due to suboptimal accuracy, making it unsuitable for this application. Conversely, Logistic Regression, with an AUC of 0.951, minimal training time, and substantial efficacy gains, emerges as a strong contender. The Decision Tree and Random Forest classifiers exhibit comparable proficiency; however, the Decision Tree's interpretability and Random Forest's computational swiftness present unique advantages. Superior still, SVM and Gradient Boosted Trees command the highest AUC and gains, albeit at the cost of increased training duration. Our findings affirm the pivotal role of API call analysis in ransomware detection and the potency of machine learning approaches in learning from extensive datasets to identify novel malware strains. Given the continual evolution of malware, detection methodologies must adapt correspondingly. This study's comparative analysis elucidates the trade-offs between accuracy, computational speed, and training time, guiding the selection of the optimal machine learning algorithm for robust ransomware detection.

Securing Biomedical Audio Data in IoT Healthcare Systems: An Evaluation of Encryption Methods for Enhanced Privacy

Communication technology have advanced quickly since the COVID-19 epidemic started, providing consumers with additional benefits and conveniences. Concerns over the privacy and confidentiality of this data have grown in importance as initiatives that promote the use of audio and video to enhance interpersonal interactions become more common. In the context of the Internet of Things (IoT), audio communications security is essential in the biomedical domain. Sensitive medical data may be compromised in these connections, which include exchanges between patients and doctors and broadcasts of vital signs. To protect patient privacy and reduce cybersecurity threats, strong security measures such as data encryption must be put in place. Our study attempts to address these issues in this environment. Comparative examination of the Chacha20, Salsa20, and Camellia encryption algorithms enabled us to ascertain that Chacha20 performs exceptionally well when it comes to audio file decryption and encryption speed. The results of our trials attest to this encryption method's astounding effectiveness and efficacy. We have also used the noise reduction technique, which is frequently used in audio security to enhance the quality of recordings and make it easier to identify significant information in audio signals. Then, Fourier transform technique, which is also used to analyze audio files and can be used to identify changes, extract hidden information, and authenticate audio files. By doing this, the audio files security and integrity are strengthened.

New method of colour image encryption using triple chaotic maps

AbstractA new image encryption algorithm based on the triple chaotic maps is proposed to deal with the issues of inadequate security and low encryption efficiency. Coloured images consist of three linked channels used in the scheme. This method uses different keys to break the correlations between adjacent pixels in each channel. The triple chaotic maps are Lorenz, 2D‐Logistic, and Henon. First, the plain image is split into RGB channels to encrypt each channel separately. Second, the triple chaotic maps generate two groups of keys. The first group of keys performs a pixel permutation, resulting in scrambled channels used as input for the following step. Finally, the second group of keys is used to diffuse the scrambled channels independently, resulting in diffused channels, which are then merged to obtain a cipher image. The triple chaotic maps of different orders generate the cipher image with great unpredictability and security. The security is evaluated using various measures. The results demonstrated a high level of security attained by successfully encrypting coloured images. Recent encryption algorithms are compared in terms of entropy, correlation coefficients, and attack robustness. The proposed method provided outstanding security and outperformed existing image encryption algorithms.

FTPE-BC: Fast thumbnail-preserving image encryption using block-churning

With the boom in personal images, cloud services are beginning to take on a significant role in image storage. However, images uploaded to the cloud will face new privacy threats. Image privacy can be effectively protected using traditional encryption methods, but the encrypted images are snowflake-like noisy images, which makes the images unusable. Lately, Thumbnail Preserving Encryption (TPE) is proposed, which relieves this conflict between image availability and privacy in cloud platforms by retaining thumbnails unchanged after encryption. Existing TPE schemes for intra-block grouping usually utilize a lot of pseudo-random functions with high computational complexity, which makes encryption and decryption time too long. We propose a new model, block-churning, and propose a new TPE scheme based on this model. In this scheme, all pixels in the block are churned as a whole. This fundamentally solves the problem of low encryption efficiency caused by intra-block grouping, and greatly improves the encryption speed, while being able to change pixel values more significantly in blocks. The experiment shows that the encrypted image achieves a good balance between availability and privacy.

Hyperchaotic color image encryption scheme based on simultaneous color channel confusion-diffusion operations

Shortcomings have been identified in current color image encryption methods. Firstly, these methods encrypt each color channel separately, resulting in a time-consuming process and independent encrypted channels, which can make hacking easier. Secondly, the use of XOR operations between image pixel values and code values during encryption can be vulnerable. To address these issues, a novel algorithm is introduced that incorporates a new XOR operation and simultaneous encryption of color channels. This approach creates interdependence between the encrypted channels, reduces encryption time, and enhances security by introducing a more complex XOR operation. The proposed method employs a substitution technique that involves XOR operations between groups of pixels and codes, inspired by the principles of the fast Walsh-Hadamard transform algorithm. The encryption process involves several key phases that enhance the security and efficiency of the system. In the initial phase, line processing involves mixing lines from different channels and application of chaotic substitution permutation operations. Subsequently, a similar operation is applied to columns, and finally, the channels are divided into overlapping squared sub-blocks, with a newly XOR proposed chaos-based confusion operation simultaneously applied to the three-channel sub-blocks. These phases are designed to ensure interdependence between color channels and reduce encryption time, resulting in a more robust encryption method. With this method, the RGB cipher channels become mutually dependent, rendering decryption of one channel impossible without the others. The approach has been evaluated using appropriate metrics and found to be robust, efficient, and resistant to various attacks, outperforming recently published methods. It is suitable for modern image encryption applications, including those related to the Internet of Medical Things (IoMT).

Cryptographic Protocols Resilient to Quantum Attacks: Advancements in Post-Quantum Cryptography

New developments in quantum computing have made people worry that current security methods could be broken by quantum attacks. Some mathematical problems are hard to solve, which is how traditional encryption methods like RSA and ECC keep their secrets safe. However, quantum computers might be able to solve these issues a lot faster than regular computers, which makes these methods less safe. Because of this threat, experts have been working hard to make post-quantum cryptography methods that quantum computers can't break. These methods are made to stand up to the strength of quantum algorithms and protect the privacy, integrity, and validity of private data even when quantum attackers are present. Lattice-based cryptography is a potential method for post-quantum encryption. Its security comes from the difficulty of certain lattice problems. Lattice-based methods are thought to be safe from threats from both traditional and quantum computers because they offer good security promises. Lattice-based cryptography is a flexible way to build different types of cryptographic primitives, like encryption, digital signatures, and key sharing protocols. The study of code-based cryptography, which is based on how hard it is to decode some error-correcting codes, is another important advance. Code-based methods have been around for a long time and have been studied a lot, which makes them a good choice for security after quantum computing. Code-based cryptography is also easy to use and has strong security features, which makes it a good choice for real-world situations. Multivariate polynomial cryptography is another possible choice for cryptography after quantum computing. The safety of this method depends on how hard it is to solve systems of multivariate polynomial problems. Multivariate polynomial methods might not be as secure as lattice-based or code-based cryptography, but they are interesting options for some situations and places with limited resources.

Research on Variable Parameter Color Image Encryption Based on Five-Dimensional Tri-Valued Memristor Chaotic System.

To construct a chaotic system with complex characteristics and to improve the security of image data, a five-dimensional tri-valued memristor chaotic system with high complexity is innovatively constructed. Firstly, a pressure-controlled tri-valued memristor on Liu's pseudo-four-wing chaotic system is introduced. Through analytical methods, such as Lyapunov exponential map, bifurcation map and attractor phase diagram, it is demonstrated that the new system has rich dynamical behaviors with periodic limit rings varying with the coupling parameter of the system, variable airfoil phenomenon as well as transient chaotic phenomenon of chaos-periodic depending on the system parameter and chaos-quasi-periodic depending on the memristor parameter. The system is simulated with dynamic circuits based on Simulink. Secondly, the differently structured synchronous controls of chaotic systems are realized using a nonlinear feedback control method. Finally, based on the newly constructed five-dimensional chaotic system, a variable parameter color image encryption scheme is proposed to iteratively generate varying chaotic pseudo-random sequences by varying the system parameters, which will be used for repetition-free disambiguation, additive modulo left-shift diffusion and DNA encryption for the three components of RGB of the color image after chunking. The simulation results are analyzed by histogram, information entropy, adjacent pixel correlation, etc., and the images are tested using differential attack, noise attack and geometric attack, as well as analyzing the PSNR and SSIM of the decrypted image quality. The results show that the encryption method has a certain degree of security and can be applied to medical, military and financial fields with more complex environmental requirements.

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext.

Improved RSA with Enhanced Security on STM32 @ 84MHz

Enhanced computations resulted in the evolution of cyber-physical systems (CPS). These systems are highly vulnerable to security threats. This work addresses the security concerns for a reliable CPS in the Internet of Things (IoT) through an improvised RSA with enhanced security features. The proposed method renders data throughput encryption at 11.67[Formula: see text]Kbps with an overall average execution time of 0.02[Formula: see text]s. The proposed algorithm exhibits the unique feature of being asymmetric, lightweight & enhanced security owing to randomization (average entropy of 0.98). The performance metrics of the proposed method are compared with other state-of-art techniques. This algorithm is implemented on commercially competent architecture STM32F401RET6 with cortex M4 processor running @ 84[Formula: see text]MHz. The proposed encryption method exhibits great features thus making it a potential real-world solution.

A hybrid encryption approach for efficient and secure data transmission in IoT devices

Security is a crucial concern in the Internet of Things (IoT) ecosystem. Due to IoT devices' constrained processing and storage resources, providing reliable security solutions is challenging. Encryption is one of the most commonly used techniques to secure user data against unauthorized access. Therefore, it is essential to develop encryption solutions that have minimal impact on the performance of IoT devices. This study introduces a hybrid encryption approach that combines symmetric blowfish encryption with asymmetric elliptic curves. Blowfish encryption is used to encrypt large volumes of data, which could otherwise affect the execution time.In contrast, elliptic curve cryptography is utilized to ensure the security of the private key, which has a small size and does not increase the execution time significantly. The suggested approach provides advantages of both asymmetric and symmetric encryption methods, leading to an improvement in throughput and a reduction in execution time. The proposed approach was evaluated, yielding promising results in comparison to other cryptographic algorithms. The results show the optimization of more than 15% in the execution time and the efficiency increase by the proposed solution. This improvement represents security with the least impact on processing resources.

A novel and effective encrypted traffic classification method based on channel attention and deformable convolution

With the rapid development of the Internet and 5G communication technologies, encrypted traffic classification, recognized as a fundamental task in network management, has gained increasing attention from researchers. An accurate traffic classification is crucial for improving network service quality, optimizing network operations, and ensuring information security. However, most of the existing neural network-based methods lack attention to key information and have insufficient traffic feature extraction capability. Motivated by the above limitations, we proposed a novel and effective end-to-end model based on channel attention and deformable convolution, called CAD-Net. Specifically, a novel channel attention mechanism and a powerful deformable convolution technique are introduced in the residual network, which provides the model with the capability of focusing on the most relevant features and capturing the implicit features among the traffic bytes. Furthermore, our class-aware weighted cross-entropy loss function significantly improves the performance of CAD-Net under class-imbalanced conditions. We comprehensively evaluated the effectiveness of CAD-Net on two public datasets, and the experimental results demonstrate that our model achieved the best performance compared with existing methods on both encrypted traffic service and application classification tasks.

Medical image encryption using novel sine-tangent chaotic map

Medical images play a crucial role in the diagnostic, planning, and monitoring of various types of diseases and physical problems. These images contain confidential details about the patient's condition. This confidential data can be safeguarded by using techniques like image encryption that transforms a given image into an unidentifiable format. This research paper proposes a medical image cryptosystem that uses a one-dimensional (1-D) novel Sine-Tangent Chaotic (STC) map and a shared key. Firstly, a private shared key is generated using the XOR operations, on which permutation and substitution operations are carried out. Secondly, a chaotic sequence is generated using the novel STC map. Finally, the permuted-substituted private shared key, the chaotic sequence, and one of the plain image components (i.e. Red, blue or Green) are XORed to construct the cipher image. To test the randomness of the proposed novel 1-D STC, we have used bifurcation diagram, Lyapunov Exponent (LE) and Shannon entropy (SE) parameters. The results show that the proposed mathematical chaotic equation has a wider chaotic range, and is more complex than some of the existing chaotic maps. Also, the observations prove that the proposed medical image encryption method performs well in comparison to the existing state of the art approaches, by obtaining values 7.99977, 99.66023 and 33.80065 for parameters Entropy, Number of Changing Pixel Rates (NPCR) and Unified Averaged Changed Intensity (UACI), respectively.

An novel dynamic compressed sensing method for image encryption based on a new coupled map lattices model

An novel dynamic compressed sensing method for image encryption based on a new coupled map lattices model

Enhancing Cyber Security Using Quantum Computing and Artificial Intelligence: A Review

This article examines the transformative potential of quantum computing in addressing the growing challenge of cyber threats. With traditional encryption methods becoming increasingly ineffective against sophisticated cyber attacks, quantum computing emerges as a promising solution, offering unparalleled computational capabilities for enhancing cyber security. This technology is poised to revolutionize how we protect sensitive data by developing quantum-resistant encryption algorithms and quantum-based machine learning modules to safeguard critical infrastructures. By exploring the intersection between quantum computing and cyber security, this article highlights the opportunities, challenges, and prospects of leveraging quantum advancements to strengthen our defenses against the evolving landscape of cyber threats

Unmanned ship image encryption method based on a new four-wing three-dimensional chaotic system and compressed sensing

This paper presents a three-dimensional chaotic map with five equilibrium points, which, unlike the classic Lorenz system, possesses a symmetric four-wing structure. Through detailed dynamical analysis and verification, the excellent chaotic properties of this map have been proven. Based on this system, an improved lightweight unmanned surface vehicle (USV) image information encryption system is designed. Given the constraints of USV equipment, the system first compresses images to enhance their storage capacity and transmission efficiency. Subsequently, an enhanced lightweight Feistel network encryption method is developed, utilizing a diagonal interpolation permutation model to ensure the security of the encryption process and reduce its complexity through a block structure. Simulation experiments and data analysis demonstrate that this algorithm effectively ensures the security of image information within the USV system. The use of compressed sensing and a block encryption structure reduces the complexity of the encryption process, thereby enhancing both the encryption efficiency and the transmission efficiency.