Encryption Method Research Articles

A Holistic Approach to Cybersecurity Integration in Autonomous Vehicles

The breakthrough of the self-driving car technology is a double-edged sword that gives us incredible opportunities and at the same time, it is the source of huge cybersecurity challenges. This paper champions the creation of a complete legal system and worldwide standards, like the ISO 26262, to regulate different parts of the autonomous vehicle (AV) operations, such as safety testing, liability, data privacy, and cybersecurity. By highlighting the significance of cybersecurity from the very start, the stakeholders and the automakers have to incorporate the practices like secure programming, routine updates, intrusion detection systems and encryption methods which will be the effective shield against the unauthorized access and data breaches. People should be informed and educated about the cybersecurity risks of AVs so that they can take the steps to protect themselves. Nevertheless, the integration of AVs with the Internet of Things (IoT) and other devices makes the cybersecurity landscape even more complicated, which means that there is a constant need for research and development to deal with problems like adversarial attacks. Presently, the deep learning-driven AV systems do not have the explainability which complicates the safety-critical applications, hence the need to increase the resilience training and deepen the deep learning models against attacks. The paper goes through all the literature on AV cybersecurity which stresses the need for functional safety standards and cybersecurity engineering guidelines. Collaboration between the governments, the industry, and the researchers is a must to set the legal foundations and the technical details for the AV cybersecurity across all levels of automation. Finally, solid cybersecurity is the key to the success of the autonomous vehicle revolution, hence the road to transformative safety, efficiency, and sustainability is paved. INDEX TERMS: Autonomous vehicles, Cybersecurity, ISO 26262, Encryption methods, ISO 21434, EU regulations, Security and Privacy.

EMOJIFY

Emoticons have become a ubiquitous part of online communication, but their limitations hinder the expression of nuanced emotions. This project proposes "Emojify," a system that leverages deep learning for real-time emotion recognition through video input. By employing techniques like pre-processing, face detection, and Convolutional Neural Networks (CNNs), Emojify analyzes facial expressions and generates corresponding emojis. This approach goes beyond traditional emoticons, dynamically reflecting emotional intent and enriching virtual communication. The project explores the potential for entertainment applications and user-friendly emoji creation tools with encryption and decryption methods. Emojify holds promise for bridging the emotional gap in virtual interactions, fostering a more expressive and engaging online experience.

Optical Color Image Encryption Algorithm Based on Two-Dimensional Quantum Walking

The double random phase encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption thanks to its R, G, and B parallel encryption. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image’s robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk is a powerful tool for modern cryptography, offering robust resistance to classical and quantum attacks. Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walking (TDQW) with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by TDQW for phase modulation in DRPE and scrambles the encrypted image’s real and imaginary parts using the generalized Arnold transform. The 24-bit plane permutation helps reduce the R, G, and B correlation, while the generalized Arnold transform bolsters DRPE’s resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. The experimental results validate the effectiveness and security of the proposed method.

Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.

Chaos key enhanced physical layer secure transmission method based on the convolutional long short-term memory neural network

In this paper, we propose a method for training a key-enhanced chaotic sequence using the convolutional long short term memory neural network (CLSTM-NN) for secure transmission. This method can cope with the potential security risk posed by the degradation of chaotic dynamics when using chaotic model encryption in traditional secure transmissions. The simulation results show that the proposed method improves the key space by 1036 compared to traditional chaotic models, reaching 10241. The method was applied to orthogonal chirp division multiplexing (OCDM). To demonstrate the feasibility of the proposed scheme, we conducted transmission experiments of encrypted 16 quadrature amplitude modulation (QAM) OCDM signals at a speed of 53.25 Gb/s over a 2 km length of 7-core optical fiber and test different encryption schemes. After key enhancements, the overall number of keys in the system can increase from 18 to 105.The results show that there is no significant difference between the bit error rate (BER) performance of the encryption method proposed in this paper and the traditional encryption method. The maximum performance difference between the different systems does not exceed 1 dBm. This fact proves the feasibility of the proposed scheme and provides new ideas for the next generation of secure transmission.

Edge server enhanced secure and privacy preserving federated learning

Federated learning (FL) has been smoothly embedded into current IoT edge computing architecture, and hatching advanced IoT-FL applications. While so, sensitive messages generated on terminal sides brings privacy issues; and vulnerable terminal devices are easy targets for Byzantine adversaries, they inject malicious data to manipulate the IoT-FL system. Now an inherent dilemma is, the privacy preserving requires anonymous indistinguishable individual characteristics, while the malicious adversary detection requires transparent distinguishable results. Existing fragmented schemes are not able to handle both problems coherently. This time, inherit our previous study, based on the IoT edge computing architecture, we utilize Homomorphic Encryption (HE) and Threshold Secret Sharing (SS) methods, propose a joint scheme (namely, Sec-IoTFL) for anonymous adversary detection. Specifically, we batch encode the clients’ training result vectors (message) as polynomials, and split them as secret pieces with a unified SS key set; then edge server and cloud server joint deal with these secret pieces in a specialized flow, where Byzantine adversaries will be filtered out. The theoretical analysis and solid experimental results suggest, in our scheme, local sensitive data is well preserved, and malicious behavior is precisely screened. Comparing with traditional methods, our Sec-IoTFL scheme shows the superiority in accuracy and efficiency.

Cryptographic Application of Elliptic Curve Generated through Centered Hexadecagonal Numbers

Background/Objectives: Elliptic Curve Cryptography (ECC) is a public-key encryption method that is similar to RSA. ECC uses the mathematical concept of elliptic curves to achieve the same level of security with significantly smaller keys, whereas RSA's security depends on large prime numbers. Elliptic curves and their applications in cryptography will be discussed in this paper. The elliptic curve is formed by the extension of a Diophantine pair of Centered Hexadecagonal numbers to a Diophantine triple with property D(8). Method: The Diffie–Hellman key exchange, named for Whitfield Diffie and Martin Hellman, was developed by Ralph Merkle and is a mathematical technique for safely transferring cryptographic keys over a public channel. Based on the Diffie–Hellman key exchange, the ElGamal encryption system is an asymmetric key encryption algorithm for public-key cryptography. The generation of keys, encryption and decryption are the three main operations of the ElGamal cryptosystem. Findings: Given the relative modesty of our objectives, the fundamental algebraic and geometric characteristics of elliptic curves shall be delineated. Then the behaviour of elliptic curves modulo p: ultimately, there is a fairly strong analogy between the structure of the points on an elliptic curve modulo p and the integers modulo n will be studied. In the end, elliptic curve ElGamal encryption analogues of Diffie–Hellman key exchange will be created. Novelty: Elliptic curves are encountered in a multitude of mathematical contexts and have a varied and fascinating history. Elliptic curves are very significant in number theory and are a focus of much recent work. The earlier research works in Elliptic Curve Cryptography has concentrated on computer algorithms and pairing – based algorithms. In this paper, the concept of polygonal numbers and its extension from Diophantine pair to triples is encountered, thus forming an elliptic curve and perform the encryption-decryption process. MSC Classification Number: 11D09, 11D99,11T71,11G05. Keywords: Elliptic curves, Cryptography, Encryption, Decryption, Centered polygonal numbers

Development of Secure Cloud-Based Government Solutions

Government agencies face significant security and efficiency challenges when adopting cloud solutions. These challenges include data breaches, unauthorized access, and compliance with stringent regulatory standards. This paper explores the development of secure and efficient cloud-based solutions tailored specifically for government needs, aiming to address these critical issues. These solutions protect sensitive government data by focusing on robust security protocols, advanced encryption methods, multi-factor authentication, and continuous monitoring. Additionally, integrating technologies such as artificial intelligence and machine learning enhances the ability to predict and mitigate potential threats. Compliance with regulatory standards, such as those set by the National Institute of Standards and Technology (NIST) and ISO 27001, is emphasized to ensure global security adherence. Implementing "Security by Design" and Zero Trust Architecture further strengthens the security framework. This research highlights the importance of a multi-faceted approach, including collaboration with cloud service providers, regular security audits, and employee training programs. Developing secure cloud-based solutions enhances national security and improves public service delivery, making it a vital endeavor for government agencies. Future research should explore emerging technologies and international cooperation to stay ahead of evolving cyber threats.

IMPACT OF 5G TECHNOLOGY ON SATELLITE COMMUNICATION: A PARADIGM SHIFT IN TELECOMMUNICATIONS WITH A FOCUS ON CYBERSECURITY CHALLENGES AND SOLUTIONS

This review investigates the transformative impact of 5G technology on satellite communication, with a particular focus on the cybersecurity challenges introduced by this integration. An analysis of 50 rigorously selected scholarly articles demonstrates that 5G significantly enhances satellite communication by improving data transmission rates, reducing latency, and increasing network efficiency, thereby facilitating extended coverage that bridges the digital divide by delivering high-speed internet to remote areas. Additionally, 5G supports the deployment of new applications such as enhanced Mobile Broadband (eMBB) and extensive Internet of Things (IoT) connectivity. However, this integration also introduces new cybersecurity challenges, such as increased vulnerability to cyber-attacks and the need for robust encryption methods to secure data transmissions. Technical issues like signal interference, regulatory hurdles involving spectrum sharing, and substantial economic investments for infrastructure development also pose significant challenges. These complexities require ongoing research and strategic planning to mitigate risks and fully harness the potential of 5G-enhanced satellite networks. The study highlights a paradigm shift in satellite communications, propelled by 5G's capabilities to create more inclusive, efficient, and secure global communication networks.

Attack on optical cryptosystems by skip connection networks

Optical encryption methods, due to their efficient operation speed and parallel processing capabilities, hold significant importance in securing multidimensional and large-volume data. Enhancing the security of optical cryptosystems from the perspective of cryptanalysis holds significant importance currently. Presently, attack methods against optical encryption are complex, and the effectiveness of these attacks is insufficient. Security analysis solutions face limitations in both breadth and depth. Therefore, this paper proposes an attack on optical cryptosystems based on a skip connection network, demonstrating the susceptibility of optical cryptosystems to attacks based on neural network algorithms. The network model is trained on plaintext-ciphertext pairs, fitting equivalent keys without various additional conditions. It approximates plaintext information in high-dimensional space, directly obtaining corresponding plaintext through ciphertext information, expanding the applicability and enhancing the effectiveness of the attack scheme. Finally, the feasibility and effectiveness of the attack scheme were verified through computer simulations. The experiments indicate that the method proposed in this paper has low computational complexity, wide applicability, produces high-quality decrypted images, and high decipherment accuracy. This provides a universal approach for analyzing the security of various optical cryptosystems from the perspective of chosen plaintext attacks.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Multi-Task Scenario Encrypted Traffic Classification and Parameter Analysis.

The widespread use of encrypted traffic poses challenges to network management and network security. Traditional machine learning-based methods for encrypted traffic classification no longer meet the demands of management and security. The application of deep learning technology in encrypted traffic classification significantly improves the accuracy of models. This study focuses primarily on encrypted traffic classification in the fields of network analysis and network security. To address the shortcomings of existing deep learning-based encrypted traffic classification methods in terms of computational memory consumption and interpretability, we introduce a Parameter-Efficient Fine-Tuning method for efficiently tuning the parameters of an encrypted traffic classification model. Experimentation is conducted on various classification scenarios, including Tor traffic service classification and malicious traffic classification, using multiple public datasets. Fair comparisons are made with state-of-the-art deep learning model architectures. The results indicate that the proposed method significantly reduces the scale of fine-tuning parameters and computational resource usage while achieving performance comparable to that of the existing best models. Furthermore, we interpret the learning mechanism of encrypted traffic representation in the pre-training model by analyzing the parameters and structure of the model. This comparison validates the hypothesis that the model exhibits hierarchical structure, clear organization, and distinct features.

Encryption Method Based on Codes

This paper proposes an improvement of the McEliece asymmetric cryptosystem based on code-based cryptography by replacing the permutation matrix with a modulo operation and using a finite field GF(q) . This approach increases the complexity of the decryption process for potential attackers, providing a high level of cryptographic security without changing the length of the key. The article provides a diagram of the improved operation of the cryptosystem and describes examples of application. An analysis of the number of possible combinations of matrices has been carried out for different implementation options of code (7,4) based on different numerical systems. It has been shown that achieving cryptographic security comparable to the original McEliece cryptosystem requires the use of q≥5.

Security augmenting of optical cryptosystem based on linear canonical transform domain using a full phase encoding technique

For the purpose of alleviating the vulnerability of double random phase encryption system in the linear canonical transform domain, a novel approach for optical security and cryptographic systems is presented. This proposed system uses a fully phase encoding technique to augment the security of encryption system in the linear canonical transform. The first step in this system involves phase encoding of the initial amplitude image to be ciphered and then modulated by the phase masks. The decryption process of image is the reversal operation of the encryption method. The effectiveness and sensitivity of our proposed cryptosystem for the encryption secret keys are verified. The resistance of our method against occlusion attacks is investigated. Moreover, the results demonstrate that the fully phase-based optical cryptosystem is more secure and robust than the amplitude-based scheme in a linear canonical domain.

Temporal action segmentation for video encryption

Videos contain temporal information, enabling them to capture the dynamic changes of actions and provide richer visual effects. Traditional video encryption methods involve decomposing videos into frames and encrypting them frame by frame, which results in significant resource consumption. This paper proposes a video encryption method based on temporal action segmentation. This methodology involves the identification and extraction of pivotal frames from a video dataset, followed by the encryption of these significant key frames. This approach serves to enhance the efficacy of the video encryption algorithm. The method consists of three modules. The first module uses temporal action segmentation to classify video frames and extract important frames for the second module’s input. The second module encrypts the extracted key frames using a chaos-based encryption algorithm, thereby reducing the time cost of video encryption. The third module outputs the encrypted video. During the encryption process, a large amount of key stream is required. To address this, the paper introduces a new pseudo-random sequence generation method called two-dimensional Gramacy&Lee map (2D-GLM). Comprehensive comparative analysis clearly demonstrates that compared to other systems, 2D-GLM exhibits superior performance and can generate a large number of high-performance pseudo-random sequences. The proposed algorithm is tested on GTEA, and the simulation results demonstrate that it can accomplish video encryption tasks with high security.

Image ciphertexts classification method based on ghost imaging and intraclass-interclass difference

In this paper, based on ghost imaging encryption, the preservation of Manhattan distance feature in ciphertext compared with plaintext is analyzed by utilizing the intraclass-interclass difference of image classification, and a classification method for image ciphertexts is proposed. After calculating Manhattan distance for both plaintexts and ciphertexts, respectively, the intraclass-interclass difference can be determined. The image that minimizes the intraclass-interclass difference is taken as the centroid to verify the consistency of the classification for various plaintext-ciphertext pairs under the same operation. The feasibility of proposed method is verified by numerical simulations, that the values of ACC and Weighted-F2 can be up to 90% when the MNIST is adopted as the test dataset. The whole process can be regarded as a kind of classification process by homomorphic encryption, however, different from the traditional homomorphic encryption methods based on mathematical model, the proposed method is accomplished based on the optical theory, and it does not require a lot of pre-training through models such as deep learning and neural networks, that means, reducing the computational expenses.

Shielding Healthcare Data Strategies for Preventing Breaches

As the healthcare sector embraces digital transformation, the risk of cyberattacks on sensitive patient data escalates. This paper presents a comprehensive analysis of contemporary strategies and technologies employed to mitigate data breaches in healthcare. Through an extensive review of 70 research papers, we identify a strong focus on securing electronic health records, data storage systems, access controls, and personal health information. Noteworthy trends include the integration of advanced technologies such as Blockchain and Artificial Intelligence, coupled with robust encryption methods, to fortify data security. Our analysis underscores the urgent need for innovative approaches to combat prevalent cybersecurity threats, particularly targeting hacking incidents and unauthorized access. Furthermore, we emphasize the ongoing evolution of mitigation strategies and the imperative of continual technological advancements to safeguard healthcare data effectively. This paper also outlines future research directions, highlighting areas for further technological innovation and addressing existing gaps in data breach prevention measures.

Research on wheat image encryption based on different chaotic systems

To study the influence of chaotic systems on wheat image encryption, a method to determine the optimal chaotic system encryption based on a wheat image is proposed. Ten different chaotic system schemes were combined to encrypt the wheat images using 13 common chaotic maps. The best chaotic system scheme was obtained by considering the anti-attack capability of these encryption schemes, which analyzed eight commonly used image encryption performance evaluation indexes. The experimental results show that the new four-dimensional chaotic system has the best encryption effect and is suitable for wheat image encryption. The proposed scheme for wheat image encryption based on chaotic systems provides a reference for other crop image encryption methods.

Multi-dimension-precision chaotic encryption mechanism for Internet of Things

The massive amount of information processed in the Internet of Things(IoT) environment requires effective encryption schemes to ensure secure transmission. However, traditional encryption methods such as Elliptic Curve Cryptography (ECC) or Data Encryption Standard (DES) are not suitable for IoT environments where large amounts of data need to be quickly encrypted. In this paper, a multi-precision encryption mechanism based on multi-dimensional chaotic system is proposed. Firstly, the proposed scheme uses floating-point, 32-bit, 24-bit, and 16-bit data accuracy respectively, which can effectively reduce the complexity through fixed-point calculation. Secondly, aiming at the problem that traditional chaotic dynamic analysis methods such as the Lyapunov index cannot be applied, an analysis scheme based on complexity measure is proposed. Finally, a free dimension encryption mechanism based on Logistic, Tent, and PWLCM is proposed. By adding random disturbance to a one-dimensional chaotic map, the weak randomness of the single chaotic sequence is overcome effectively. Finally, the security analysis of the encryption scheme is carried out, and the result proves that the scheme can effectively resist the common attack means. In the 3D chaotic system, the encryption effect of 24-bit data accuracy is close to floating point accuracy, which is suitable for the IoT environment with large data volume, high encryption efficiency, and vulnerability.

TLS fingerprint for encrypted malicious traffic detection with attributed graph kernel

Recently, more and more applications have adopted security protocols like Transport Layer Security (TLS) for data encryption. However, these privacy-enhancing approaches have also been abused by the attackers to deliver malicious payloads. Many existing encrypted network classification methods suffer from the imbalanced volume of normal and malicious traffic, which leads to bad model robustness. In this paper, we propose a novel TLS fingerprinting approach to capture the characteristics of encrypted network traffic. The fingerprints are attributed graphs obtained from TLS sessions, which can simultaneously take into consideration the sequential and statistical features of these sessions. As the communication patterns of different applications differ considerably, the graphs representing TLS connections could be used to characterize the network with the help of the graph kernel method, which results in a model with high accuracy in malicious TLS session detection and application discrimination. Moreover, we adopt Locality-Sensitive Hashing (LSH) and filtering techniques to reduce the time cost of our model. Model evaluation on real-world datasets shows that our model is more robust than existing methods presented in this work when the malicious traffic takes up an extremely small portion of the whole traffic.