Development Of Secure Systems Research Articles

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.

IDSUML: Intrusion Detection System Using Machine Learning

Abstract: Network and system security is essential to modern digital communication. There will be multiple successful attempts by hackers and other intruders to obtain illegal access to networks and internet services. The development of secure systems is continuously accompanied by new dangers and the corresponding responses. Using intrusion detection systems (IDS) is one choice. An intrusion detection system. Main objective is to safeguard its resources from possible dangers. These acts are categorized as either potential or conventional attacks since it is capable of analysing and forecasting user behaviour. To find network intrusions, we typically employ Support Vector Machines (SVM) and Rough Pure Mathematics (RST). RST is used to preprocess and decrease the amount of data when packets are retrieved from the network. The RST-selected options provided to the SVM model enable it to provide many views and an explanation. A good application of the approach is to reduce the density of knowledge in a certain field. By comparing the data with Principal element analysis, the experiments show how RST and SVM schema may lower the false positive rate and increase accuracy (PCA)

Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey

The integration of blockchain and machine learning technologies has the potential to enable the development of more secure, reliable, and efficient autonomous car systems. Blockchain can be used to store, manage, and share the large amounts of data generated by autonomous vehicle various sensors and cameras, ensuring the integrity and security of these data. Machine learning algorithms can be used to analyze and fuse these data in real time, allowing the vehicle to make informed decisions about how to navigate its environment and respond to changing conditions. Thus, the combination of these technologies has the potential to improve the safety, performance, and scalability of autonomous car systems, making them a more applicable and attractive option for consumers and industry stakeholders. In this paper, all relevant technologies, such as machine learning, blockchain and autonomous cars, were explored. Various techniques of machine learning were investigated, including reinforcement learning strategies, the evolution of artificial neural networks and main deep learning algorithms. The main features of the blockchain technology, as well as its different types and consensus mechanisms, were discussed briefly. Autonomous cars, their different types of sensors, potential vulnerabilities, sensor data fusion techniques, and decision-making models were addressed, and main problem domains and trends were underlined. Furthermore, relevant research discussing blockchain for intelligent transportation systems and internet of vehicles was examined. Subsequently, papers related to the integration of blockchain with machine learning for autonomous cars and vehicles were compared and summarized. Finally, the main applications, challenges and future trends of this integration were highlighted.

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Facial recognition technology, a cornerstone in modern software security, has seen significant advancements through the integration of Artificial Intelligence (AI). This research focuses on enhancing facial recognition systems by incorporating sophisticated machine learning algorithms and deep neural networks. By doing so, the goal is to increase the accuracy and reliability of these systems in security applications. The study uses a variety of facial datasets to train AI models that are adept at extracting facial features and recognizing patterns. These models are subjected to rigorous testing to evaluate their performance in terms of identification accuracy, processing speed, and adaptability to different environmental conditions. One of the key challenges addressed in the research is the system's vulnerability to errors and potential misuse. Ethical considerations and privacy concerns are at the forefront of the study. The research highlights the importance of designing AI-based facial recognition systems that respect user privacy and are resistant to biases, thus fostering trust and acceptance among users. The results of the study show a marked improvement in system performance, demonstrating enhanced recognition accuracy and speed, while maintaining robustness across different conditions. By offering practical recommendations for the development of secure, ethical, and privacy-aware facial recognition systems, this research contributes valuable insights into the integration of AI in software security. It underscores the importance of continuous innovation and ethical responsibility in the deployment of facial recognition technologies, shaping the future landscape of technological security measures

Energy efficiency security in urban areas: Challenges and implementation

In this paper, we explore the transformative potential of data-driven methodologies in the intricate landscape of urban energy transactions, with a keen focus on enhancing decision-making intelligence, amplifying the integration of renewable energy sources, and fostering active community engagement. A pivotal contribution to this research lies in the introduction of an innovative Intrusion Detection System (IDS) fortified by the robust capabilities of deep evolving convolutional Generative Adversarial Networks (GANs). This novel security measure is designed to fortify urban energy transactions against potential cyber threats, ensuring the integrity and reliability of the energy infrastructure. Furthermore, our study introduces a groundbreaking hybridization of the Bat Algorithm (BA) and Teaching-Learning-Based Optimization (TLBO) for the optimization of GAN model training. This fusion of optimization techniques enhances the efficiency and accuracy of the training process, elevating the reliability of the generated models within the context of urban energy management. To validate and demonstrate the practical applicability of these innovations, we deploy them on the real-time dataset of a smart city's digital twin. This implementation serves as a tangible proof of concept, offering valuable insights into the performance and adaptability of the proposed approaches within real-world urban energy scenarios. As the findings unfold, it becomes evident that our research significantly contributes to the evolution of urban energy management, providing a robust foundation for the development of secure, resilient, and community-centric smart city energy systems. Through these advancements, we pave the way for a sustainable urban future where energy transactions are not only efficient but also secure and inclusive, aligning with the broader goals of smart city development.

PH sensitive adriamycin-incorporated nanoparticles self-assembled from amphiphilic chitosan derivatives with enhanced antioxidant and antitumor activities

Development of secure and efficient drug delivery systems to overcome physicochemical challenges in cancer clinical treatment is one of the most active fields, and novel drug carriers are actively sought. In this work, the successful synthesis of amphiphilic chitosan derivatives containing pyridine cations and folic acid (TMACMCF) was confirmed by FTIR and 1H NMR spectroscopy. The critical micelle concentration (CMC) of TMACMCF was studied, and the results indicated that it possessed a relatively low CMC (0.186 mg/mL). Self-assembled nanoparticles (TMACMCF-ADM NPs) for the entrapment and delivery of adriamycin (ADM) were synthesized based on TMACMCF, and the morphology, particle size, zeta potential, sustained release performance, antioxidant and antitumor activities, and cytotoxicity were investigated. The particle size of TMACMCF-ADM NPs was 163.79 ± 7.31 nm, and the morphology observed by SEM and TEM showed spherical or oval particles. TMACMCF-ADM NPs showed excellent entrapment of ADM with entrapment efficiency and drug loading efficiency of 65.04 ± 1.20 % and 22.73 ± 0.40 %, respectively. The release performance results showed that in vitro release profiles of ADM from NPs exhibited pH-sensitive controlled and sustained release properties. The assay of antioxidant activity exhibited that TMACMCF-ADM NPs possessed significantly improved scavenging ability against three kinds of radicals (Superoxide-radical, DPPH radical, and ABTS radical). Moreover, the assay of antitumor activity by CCK-8 test exhibited that nanoparticles led to statistically significant enhancement in inhibition index for four kinds of cancer cells (BGC-823, MCF-7, HEPG-2, and A549). This work provides a promising carrier for secure and effective entrapment and delivery of ADM.

Enhancing Security in Vehicle-to-Vehicle Communication: A Comprehensive Review of Protocols and Techniques

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

Ontological Augmentation and Analytical Paradigms for Elevating Security in Healthcare Web Applications

The 4th and 5th industrial revelations provided huge access to a high volume of information in healthcare sectors. However, the vast majority of web applications are under attack constantly. Due to the huge volume of attack vectors in recent years, technical breaches to the web applications of healthcare sectors are becoming a common issue. Hence, it is essential to develop an effective framework that would help experts and healthcare practitioners in web application security management. In this research, we proposed an ontology-based technique for developing secure online web applications in healthcare sectors. This study presents a conceptual framework with 5 stages namely: idea understanding, requirement identification, design and code, threat classification, and facilitate. The proposed methodology involves several various advantage features such as providing a unified path for future professional applications. Also, the proposed solution provides a clear pathway for implementing the easy-to-use secure web application development in short term in the healthcare sector. Finally, the study used a multi-criteria decision-making methodology to undertake a performance simulation assessment. Out of the evaluated process, Fuzzy-AHP has shown better performance for scheduling each step of the final developed security system better. In the future, the proposed method will be evaluated using more databases in the healthcare sector to improve the applications of the proposed method.

Privacy and security issues in smart grids: A survey

Smart grids have emerged as a transformative technology in the energy sector, enabling efficient electricity management, improved reliability, and integration of renewable energy sources. The necessity to promote smart grid (SG) has been recognized with a strong consensus. The SG integrates electrical grids and communication infrastructures and forms an intelligent electricity network working with all connected components to deliver sustainable electricity supplies. Many advanced communication technologies have been identified for SG applications with a potential to significantly enhance the overall efficiency of power grids. However, the widespread deployment of smart grids raises concerns about the privacy and security of the data collected and transmitted by these systems. To address these concerns, this paper proposes a comprehensive framework for ensuring privacy and security in smart grid systems. This framework includes encryption techniques, access control mechanisms, and robust authentication protocols. Additionally, this paper discusses the importance of user awareness and education in mitigating privacy and security risks. The research contributes to the existing literature on smart grid privacy and security by providing insights specific to the information technology security domain. The findings of this manuscript will be valuable for policymakers, energy providers, and researchers working towards the development of secure and privacy-preserving smart grid systems.

Cybersecurity and Confidentiality in Smart Grid for Enhancing Sustainability and Reliability

Ensuring cybersecurity and confidentiality in smart grids is crucial for enhancing sustainability and reliability in today's technology-driven world. With the increasing reliance on smart grid technologies, it is imperative to address the potential cybersecurity risks and protect the confidentiality of sensitive data. This research focuses on exploring the challenges and strategies associated with cybersecurity and confidentiality in smart grids. It examines the importance of safeguarding smart grid infrastructure from cyber threats to maintain sustainable and reliable energy delivery systems. The study investigates various techniques and technologies, including encryption, authentication, intrusion detection, and secure communication protocols, that can be employed to enhance the cybersecurity and confidentiality of smart grids. By highlighting the significance of a robust cybersecurity framework and the integration of privacy-preserving measures, this research aims to contribute to the development of secure and resilient smart grid systems. The findings and recommendations presented in this work provide valuable insights for policymakers, industry professionals, and researchers involved in the design and implementation of secure smart grid solutions, ultimately leading to the advancement of sustainable and reliable energy infrastructures.

Insights from Evidence-Based Medicine Method for Building Security Systems Against Terrorist Attacks in Hospitals.

Hospitals have become soft targets for terrorism, with increasing terrorist attacks observed in the last two decades. Not only the counter-terrorism and law enforcement agencies but the hospitals also need to be actively involved and undertake the responsibility of safeguarding hospital security by establishing an effective security system. Evidence-based medicine emphasizes the significance of scientific evidence, and in practice, research methods for the generation, collection, and utilization of evidence may offer new insights to overcome the issue of terrorism. The present study discusses the potential application of methods in evidence-based medicine for developing security systems against terrorist attacks in hospitals. First, hospitals can use simulated scenarios of terrorist attacks to generate evidence. For example, combining virtual technology with virtual reality technology and selecting appropriate algorithms or models to generate evidence. Second, they can apply tabletop exercises to propose measures, as a means of collecting evidence. The collection of data can be obtained through questionnaires or interviews. Some biological methods, such as collecting salivary markers of acute stress (α-amylase and secretory immunoglobulin A), can also be used to assess emotional states. Third, they can use the collected evidence to practically test and refine the implemented measures. To verify the effectiveness and feasibility of the formulated solutions in real-life scenarios, simulations, models, drills, and similar methods can be used. This approach may provide new perspectives for the development of hospital security systems.

An Exploration of Blockchain Protocols for Trusted Vote Aggregation: A Consensus Algorithm Approach

The application of blockchain technology in electronic voting (e-voting) systems represents a promising solution to the perennial challenges of trust, transparency, and security in electoral processes. This study aims to identify a suitable blockchain protocol that supports trustworthy vote aggregation and has a suitable consensus algorithm to validate vote counting. Our research methodology includes an extensive literature review and a comparative analysis of different blockchain protocols. Considering this, we examine various consensus algorithms such as Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance among others, each of which presents unique strengths and challenges. In addition, this study enriches the existing body of knowledge by proposing a novel algorithm that works at the edge of the network to validate and aggregate votes on the blockchain. This newly proposed algorithm is designed to provide maximum security, reliability, and accuracy while minimizing computational resources and network overhead. Our comprehensive research and innovative proposal serve to strengthen the potential of blockchain protocols and their consensus algorithms in the field of electronic voting systems. The results of this research could significantly influence the development and implementation of secure, transparent, and reliable e-voting systems based on blockchain technology, paving the way for more democratic and accountable voting mechanisms.

Exploring the Development of a User-Friendly Home Management System with ESP Rainmaker Firmware

With the rapid development of the Internet of Things (IoT), managing living spaces has become more intuitive than ever. This research illustrates the implementation of a user-friendly home management system utilizing the advanced ESP Rainmaker firmware from Espressif Systems. The study focuses on creating a seamless user experience through the efficient integration of ESP Rainmaker's features. Various hardware components are carefully selected to facilitate the creation of user-friendly smart devices, including intelligent lighting systems, switches, and climate control units. The specialized firmware tailored to the unique attributes of each device ensures secure connections with the ESP Rainmaker cloud services. The research delves into comprehensive provisioning techniques, including Bluetooth and SoftAP, to simplify device onboarding and ensure secure connectivity. Leveraging the Rainmaker cloud services, the system emphasizes the enhanced user control and synchronization of device states, contributing to an average success rate of 88% and an average response time of 2.1 seconds. This study provides valuable insights into the development of intuitive and secure home automation systems, underscoring the significance of integrated platforms like ESP Rainmaker in shaping the future of IoT-enabled living environments.

Ensuring Resilience in Ocean Energy Power Plants: A Survey of Cybersecurity Measures

Offshore Renewable Energy (ORE) is a promising solution to address the challenges of climate change and the depletion of fossil fuels [1]. Wave power, a form of ORE, is considered one of the purest energy sources with significant growth potential [2]. In addition to investing in these energy sources, nations are also working to enhance the protection of Critical Infrastructure (CI). CI encompasses all services crucial to the functioning of society and the economy, including electric power systems and their various forms of generation, such as renewable energy sources. Hence, in addition to exploring various forms of power generation, the cybersecurity of the networks connecting the devices in these systems is a crucial aspect to consider to prevent attacks and minimize the risk of cyber threats to suppliers and customers [3]. For instance, the European Commission states that reducing CI vulnerability and increasing its resilience is one of the main objectives of the European Union.
 However, to date, a comprehensive review that synthesizes the various approaches to cybersecurity in ocean energy is yet to be published. The objective of this study is to present a comprehensive survey of the application of cybersecurity measures to renewable energy sources, with a specific focus on ocean energy. A systematic review of the literature was carried out, following the steps outlined by Kitchenham [4]. The methodology steps are illustrated in the flowchart (see Figure 1). Of the 49 articles selected, three main study topics emerged: i) smart ocean, ii) cybersecurity for renewable energy systems, and iii) marine data security. These three topics are interrelated as a smart ocean can be considered as an integrated sensing, communication, and computing ecosystem that connects marine objects in surface and underwater environments [5]. Once the wave energy converters (WECs) are installed, it is also essential to develop safety systems for these devices, as demonstrated in the first report on cybersecurity guidance for MRE (Marine Renewable Energy) systems [6] prepared by the Pacific Northwest National Laboratory (PNNL). In preparation for this report, researchers reviewed the cyber threats and vulnerabilities of information technology (IT) and operational technology (OT) equipment used in various WEC models. Figure 2 presents an example of the possible threats and attacks on WEC devices.
 In conclusion, this article provides a comprehensive survey of the application of cybersecurity measures in ocean energy, highlighting the importance of reducing vulnerability in the cybersecurity of power plants in this sector. Through a systematic review of the literature, three main study topics were identified and analysed, providing a valuable resource for future research in this area. The findings of this study can inform and guide the development of more secure and resilient systems, contributing to the overall improvement of critical infrastructure in the field of ocean energy. As such, this article offers a significant contribution to the ongoing effort to address the challenges posed by the changing energy landscape and the need to protect critical infrastructure from cyber threats.
 Please refer to uploaded PDF to see references and figures.
 

PPDA-FAF: Maintaining Data Security and Privacy in Green IoT-Based Agriculture

Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results.

A DNA‐Gated and Self‐Protected Bioorthogonal Catalyst for Nanozyme‐Assisted Safe Cancer Therapy

AbstractTransition metal catalysts (TMCs) mediated bioorthogonal uncaging catalysis has sparked increasing interest in prodrug activation. However, due to their “always‐on” catalytic activity as well as the complex and catalytic‐detrimental intracellular environment, the biosafety and therapeutic efficiency of TMCs are unsatisfactory. Herein, a DNA‐gated and self‐protected bioorthogonal catalyst has been designed by modifying nanozyme‐Pd0 with highly programmable nucleic acid (DNA) molecules to achieve efficient intracellular drug synthesis for cancer therapy. Monolayer DNA molecules could endow the catalyst with targeting and perform as a gatekeeper to achieve selective prodrug activation within cancer cells. Meanwhile, the prepared graphitic nitrogen‐doped carbon nanozyme with glutathione peroxidase (GPx) and catalase (CAT)‐like activities could improve the catalytic‐detrimental intracellular environment to prevent the catalyst from being inactivated and sensitize the subsequent chemotherapy. Overall, we believe that our work will promote the development of secure and efficient bioorthogonal catalytic systems and provide new insights into novel antineoplastic platforms.

A DNA-Gated and Self-Protected Bioorthogonal Catalyst for Nanozyme-Assisted Safe Cancer Therapy.

Transition metal catalysts (TMCs) mediated bioorthogonal uncaging catalysis has sparked increasing interest in prodrug activation. However, due to their "always-on" catalytic activity as well as the complex and catalytic-detrimental intracellular environment, the biosafety and therapeutic efficiency of TMCs are unsatisfactory. Herein, a DNA-gated and self-protected bioorthogonal catalyst has been designed by modifying nanozyme-Pd0 with highly programmable nucleic acid (DNA) molecules to achieve efficient intracellular drug synthesis for cancer therapy. Monolayer DNA molecules could endow the catalyst with targeting and perform as a gatekeeper to achieve selective prodrug activation within cancer cells. Meanwhile, the prepared graphitic nitrogen-doped carbon nanozyme with glutathione peroxidase (GPx) and catalase (CAT)-like activities could improve the catalytic-detrimental intracellular environment to prevent the catalyst from being inactivated and sensitize the subsequent chemotherapy. Overall, we believe that our work will promote the development of secure and efficient bioorthogonal catalytic systems and provide new insights into novel antineoplastic platforms.

Multi-harvesting smart solution for self-powered wearable objects: System-level model and transistor-level design

Internet of Things and growing popularity of connected devices are increasing the demand for wearables applications, which is driving the development of ultra-low power secure systems. Thus, having a system running at low voltage with low power consumption introduces the use of ambient energy harvesting. Besides, batteries have several limitations reducing their compatibility with wearables, including periodical need for recharge, limited life-time, heavy and large form factor and non-eco-friendly life-cycle. Therefore ambient energy harvesting technology gives a promising alternative for self-powered wearables. The integration of these solutions on the same silicon support is a real technological challenge. This paper proposes a multi-harvesting fully-integrated system architecture combining AC and DC sources. Based on a MATLAB/Simulink model, the proposed system allows the calibration of an optimal solution. It is designed and simulated in 40 nm CMOS process, demonstrating the use of an AC source as secondary input to start a harvesting system with very low DC source as main source.

Fire alarm systems construction on artificial intelligence principles

Coverage of modern views on security trends and directions for developing security systems in the context of global automation and integration into unified automated control systems. An overview of the prospects for using artificial intelligence in security systems to identify difficult to classify situations using signs obtained from monitoring data. Assessment of the prospects for the use of fire alarm systems based on the principles of artificial intelligence based on neural networks (building on the principles of combinatorics of methods of neural networks and odd logic systems). Construction of a mathematical model that describes the process of generating a reliable formation of a signal about the transition of the system to the "fire" state and a description of the problem of correctly setting the system response threshold for signal generation, solving the problem of minimizing the number of false positives by introducing an additional channel that receives initial information about the state of the object in the optical range. Investigation of the reliability of the characteristics of gas fire detectors declared by the manufacturer to determine the possibility of using them as initial elements of fire alarm systems built on the principles of neural networks.

Enhanced design and hardware implementation of a chaos-based block cipher for image protection

Nowadays chaos-based cryptography systems are widely used to protect sensitive data. A variety of chaos-based cryptosystems with software implementation has been published in the literature. However, due to the rapid growth of connected devices, the development of hardware secure and low-resource cryptographic systems is necessary. In this paper, we enhance the design of one of our software cryptosystems and then we address its FPGA-based implementation. The improvement in the design consists essentially to introduce an Initial Vector (IV) entry to the Chaotic Pseudo Random Number Generator (CPRNG) thus allowing to generate a new dynamic keys for the confusion layer (using a modified 2-D cat map) and diffusion layer (using a logistic map) with each new execution of the system. The CPRNG is formed by XORred a discrete Skew Tent Map (STM) with a discrete Piecewise Linear Chaotic Map (PWLCM). The proposed cryptosystem, which operates in Cipher Block Chaining (CBC) mode, is controlled by a Finite State Machine (FSM) to ensure that both the encryption and decryption are performed by the same reconfigurable architecture. The system is implemented on a Xilinx XC7Z020 ZYNQ FPGA platform. The results obtained in terms of hardware implementation costs (logic resources, throughput, and efficiency) and in terms of security against cryptographic attacks demonstrate the effectiveness of the proposed chaos-based cryptosystem.