Optimized image processing and clustering to mitigate security threats in mobile ad hoc network

Since there are provisions of many attributes that are not possible or difficult to follow by networks conventionally, mobile ad-hoc networks are extensively deployed. This application starts through the defense sectors, the sensory node presents in the hostile territories down to the gadgets for congestion communication in traffic by general transportation when travelling for adequate provision of infrastructure during disaster recovery. As a lot of importance related to (mobile ad hoc network) MANET application, one important factor in ad-hoc networks is security. Using image processing for securing MANET is the area of focus of this research. Therefore, in this article, the security threats are assessed and representative proposals are summarized in ad-hoc network’s context. The study reviewed the current situation of the art for original to security provision called mobile ad hoc network for wireless networking. The threats to security are recognized while the present solution is observed. The study additionally summarized education erudite, talks on general issues and future instructions are recognized. Also, in this study, the forecast weighted clustering algorithm (FWCA) is employed as a cluster head over weighted clustering algorithm (WCA) is examined as quality in cluster-based routing, service is highly significant with MANET

Securing DICOM images based on adaptive pixel thresholding approach

This paper presents a novel efficient two-region Selective encryption approach that exploits medical images statistical properties to adaptively segment Digital Imaging and Communications in Medicine (DICOM) images into regions using thresholding technique in the spatial domain. This approach uses adaptive pixel thresholding, in which thresholds for same DICOM modality, anatomy part and pixel intensities' range were extracted off-line. Then, the extracted thresholds were objectively and subjectively evaluated to select the most accurate threshold for the correspondent pixel intensities' range. In the on-line stage, DICOM images were segmented into a Region Of Interest (ROI) and a Region Of Background (ROB) based on their pixels intensities using the adopted thresholds. After that, ROI was encrypted using Advanced Encryption Standard (AES), while ROB was encrypted using XXTEA. The main goal of the proposed approach is to reduce the encryption processing time overhead in comparison with the Naïve approach; where all image pixels are encrypted using AES. The proposed approach aims to achieve a trade-off between processing time and a high level of security. The encryption time of the proposed approach can save up to 60% of the Naïve encryption time for DICOM images with small-medium ROI

Application of Intelligent Multi Agent Based Systems For E-Healthcare Security

In recent years, availability and usage of extensive systems for Electronic Healthcare Record (EHR) is increased. In medical centers such hospitals and other laboratories, more health data sets were formed during the treatment process. In order to enhance the standard of the services provided in healthcare, these records where shared and can be used by various users depends on their requirements. As a result, notable issues in the security and privacy where obtained which should be monitored and removed in order to make the use of EHR more effectively. Various researches have been done in the past literature for improving the standards of the security and privacy in E-health systems. In spite of this, it is not completely enhanced. In this paper, a comprehensive analysis is done by selecting the existing approaches and models which were proposed for the security and privacy of the E-healthcare systems. Also, a novel Intelligent-based Access Control Security Model (IBAC) based on multi agents is proposed to maintain and support the security and privacy of E-healthcare systems. This system uses agents in order to maintain security and privacy while accessing the E-health data between the users.Comment: 6 pages, 3 figures, 1 table, journa

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Design a system for an approved video copyright over cloud based on biometric iris and random walk generator using watermark technique

Copyright is a tool for preventing anyone forged to copy an electronic work from another person and claim that electronic work is referred to him. Since the identity of the person is always determined by his name and biometrics, there is a concern to handle this information, to preserve the copyright. In this paper, a new idea for copyright technology is used to prove video copyright, by using blind watermarking technique, the ownership information is hidden inside video frames using linear congruential generator (LCG) for adapted the locations of vector features extracted from the name and biometric image of the owner instead of hidden the watermark in the Pseudo Noise sequences or any other feature extraction technique. When providing the watermarked vector, a statistical operation is used to increase randomization state for the amplifier factors of LCG function. LCG provides random positions where the owner's information is stored inside the video. The proposed method is not difficult to execute and can present an adaptable imperceptibility and robustness performance. The output results show the robustness of this approach based on the average PSNR of frames for the embedded in 50 frames is around 47.5 dB while the watermark remains undetectable. MSSIM values with range (0.83 to 0.99)

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa

IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century