In today's digital age, sensitive multimedia informations are transmitted over public networks that are vulnerable to unauthorized access and data tampering. This motivates more robust encryption methods to combat such security threats. In this paper, a chaotic map-based encryption technique is presented as a solution to these issues. The proposed algorithm termed as OptiSecure-3D presents optimized parameter-based 3D chaotic maps for image encryption. The method integrates three primary components: stacked autoencoder (SAE), optimized parameter-based chaotic mapping, and encryption/decryption module, to ensure robust and secure encryption of images. The result evaluated the proposed OptiSecure-3D image encryption algorithm with a randomness test, pixel adjacency correlation test, and differential analysis. The mean entropy was approx. 7.9 and the mean number of pixels changing rate (NPCR) was approx. 99.8, unified average changing intensity (UACI) was approx. 33.46. Moreover, the OptiSecure-3D algorithm also investigated the result under noise attacks and shows better cryptanalysis results as compared to comparative state-of-art models. The findings suggest that our chaotic map-based encryption technique not only provides an effective solution to the security vulnerabilities of digital image transmission but also enhances the overall reliability of multimedia communication systems. This paper presents a significant advancement in the field of secure image encryption to meets the increasing demands for data security in modern digital communication networks.
- Keywords
- Chaotic maps, Compressed encryption, Image encryption, Optimization, Secure communication,
- Publication type
- Journal Article MeSH
Malware is a common word in modern era. Everyone using computer is aware of it. Some users have to face the problem known as Cyber crimes. Nobody can survive without use of modern technologies based on computer networking. To avoid threat of malware, different companies provide antivirus strategies on a high cost. To prevent the data and keep privacy, companies using computers have to buy these antivirus programs (software). Software varies due to types of malware and is developed on structure of malware with a deep insight on behavior of nodes. We selected a mathematical malware propagation model having variable infection rate. We were interested in examining the impact of memory effects in this dynamical system in the sense of fractal fractional (FF) derivatives. In this paper, theoretical analysis is performed by concepts of fixed point theory. Existence, uniqueness and stability conditions are investigated for FF model. Numerical algorithm based on Lagrange two points interpolation polynomial is formed and simulation is done using Matlab R2016a on the deterministic model. We see the impact of different FF orders using power law kernel. Sensitivity analysis of different parameters such as initial infection rate, variable adjustment to sensitivity of infected nodes, immune rate of antivirus strategies and loss rate of immunity of removed nodes is investigated under FF model and is compared with classical. On investigation, we find that FF model describes the effects of memory on nodes in detail. Antivirus software can be developed considering the effect of FF orders and parameters to reduce persistence and eradication of infection. Small changes cause significant perturbation in infected nodes and malware can be driven into passive mode by understanding its propagation by FF derivatives and may take necessary actions to prevent the disaster caused by cyber crimes.
- MeSH
- Algorithms * MeSH
- Fractals * MeSH
- Humans MeSH
- Computer Simulation MeSH
- Software MeSH
- Models, Theoretical MeSH
- Computer Security * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols.
- Keywords
- Chaotic optical communication, Hybrid optical feedback system, Network security, Protocols,
- Publication type
- Journal Article MeSH
In therapeutic diagnostics, early diagnosis and monitoring of heart disease is dependent on fast time-series MRI data processing. Robust encryption techniques are necessary to guarantee patient confidentiality. While deep learning (DL) algorithm have improved medical imaging, privacy and performance are still hard to balance. In this study, a novel approach for analyzing homomorphivally-encrypted (HE) time-series MRI data is introduced: The Multi-Faceted Long Short-Term Memory (MF-LSTM). This method includes privacy protection. The MF-LSTM architecture protects patient's privacy while accurately categorizing and forecasting cardiac disease, with accuracy (97.5%), precision (96.5%), recall (98.3%), and F1-score (97.4%). While segmentation methods help to improve interpretability by identifying important region in encrypted MRI images, Generalized Histogram Equalization (GHE) improves image quality. Extensive testing on selected dataset if encrypted time-series MRI images proves the method's stability and efficacy, outperforming previous approaches. The finding shows that the suggested technique can decode medical image to expose visual representation as well as sequential movement while protecting privacy and providing accurate medical image evaluation.
- Keywords
- Encryption, Heart Disease, MRI Images, Multi-faceted long short-term memory (MF-LSTM),
- MeSH
- Algorithms MeSH
- Deep Learning MeSH
- Confidentiality MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Heart Diseases * diagnostic imaging MeSH
- Neural Networks, Computer MeSH
- Image Processing, Computer-Assisted methods MeSH
- Privacy * MeSH
- Computer Security MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Telemedicine, defined as the practice of delivering healthcare services remotely using information and communications technologies, raises a plethora of ethical considerations. As telemedicine evolves, its ethical dimensions play an increasingly pivotal role in balancing the benefits of advanced technologies, ensuring responsible healthcare practices within telemedicine environments, and safeguarding patient rights. Healthcare providers, patients, policymakers, and technology developers involved in telemedicine encounter numerous ethical challenges that need to be addressed. Key ethical topics include prioritizing the protection of patient rights and privacy, which entails ensuring equitable access to remote healthcare services and maintaining the doctor-patient relationship in virtual settings. Additional areas of focus encompass data security concerns and the quality of healthcare delivery, underscoring the importance of upholding ethical standards in the digital realm. A critical examination of these ethical dimensions highlights the necessity of establishing binding ethical guidelines and legal regulations. These measures could assist stakeholders in formulating effective strategies and methodologies to navigate the complex telemedicine landscape, ensuring adherence to the highest ethical standards and promoting patient welfare. A balanced approach to telemedicine ethics should integrate the benefits of telemedicine with proactive measures to address emerging ethical challenges and should be grounded in a well-prepared and respected ethical framework.
- Keywords
- data security, ethical aspects, ethical guidelines, patient privacy, patient welfare, regulations in telemedicine, telemedicine,
- MeSH
- Confidentiality ethics MeSH
- Humans MeSH
- Patient Rights ethics MeSH
- Telemedicine * ethics MeSH
- Physician-Patient Relations ethics MeSH
- Computer Security ethics MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians' reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft's ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security.
- Keywords
- Blockchain, Consensus algorithm, Electronic healthcare system, Security, Transparency,
- MeSH
- Algorithms * MeSH
- Blockchain * MeSH
- Electronic Health Records MeSH
- Consensus MeSH
- Physicians MeSH
- Humans MeSH
- Delivery of Health Care MeSH
- Computer Security MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Quantum key distribution (QKD) enables two remote parties to share encryption keys with security based on the laws of physics. Continuous-variable (CV) QKD with coherent states and coherent detection integrates well with existing telecommunication networks. Thus far, long-distance CV-QKD has only been demonstrated using a highly complex scheme where the local oscillator is transmitted, opening security loopholes for eavesdroppers and limiting potential applications. Here, we report a long-distance CV-QKD experiment with a locally generated local oscillator over a 100-kilometer fiber channel with a total loss of 15.4 decibels. This record-breaking distance is achieved by controlling the phase noise-induced excess noise through a machine learning framework for carrier recovery and optimizing the modulation variance. We implement the full CV-QKD protocol and demonstrate the generation of keys secure against collective attacks in the finite-size regime. Our results mark a substantial milestone for realizing CV quantum access networks with a high loss budget and pave the way for large-scale deployment of secure QKD.
- Publication type
- Journal Article MeSH
We describe the development and provision of a digital mental health intervention and trauma support platform for victims of political and social repression in Belarus. The Samopomoch platform provides secure and effective support tailored to the needs of such victims, and individuals are provided with access to the service via a modern, encrypted, and protected communication platform. The service involves personal health tracking (e-mental health self-screening), targeted and untargeted client communication (psychoeducation and self-help information), and psychological counselling sessions. The Samopomoch platform is also collecting evidence to show the effectiveness of the service and proposes a model for replication in similar settings. To our knowledge, this is the first immediate digital mental health-care response to a political crisis, and the high needs and increasing demand for this service within the targeted population indicate the necessity for its continuation and scaling-up. We urge policy makers to provide immediate responses for establishing digital mental health interventions and psychological trauma support.
In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark's LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication.
- Keywords
- Class-B, IoT, LoRa, LoRaWAN, dataset, network sniffer, time synchronization, traffic monitoring,
- Publication type
- Journal Article MeSH
Most of the video content on the Internet today is distributed through online streaming platforms. To ensure user privacy, data transmissions are often encrypted using cryptographic protocols. In previous research, we first experimentally validated the idea that the amount of transmitted data belonging to a particular video stream is not constant over time or that it changes periodically and forms a specific fingerprint. Based on the knowledge of the fingerprint of a specific video stream, this video stream can be subsequently identified. Over several months of intensive work, our team has created a large dataset containing a large number of video streams that were captured by network traffic probes during their playback by end users. The video streams were deliberately chosen to fall thematically into pre-selected categories. We selected two primary platforms for streaming - PeerTube and YouTube The first platform was chosen because of the possibility of modifying any streaming parameters, while the second one was chosen because it is used by many people worldwide. Our dataset can be used to create and train machine learning models or heuristic algorithms, allowing encrypted video stream identification according to their content resp. type category or specifically.
- Keywords
- Encrypted, Identification, Machine learning, Video stream,
- Publication type
- Journal Article MeSH
