Medical Image Compression and Encryption using Chaos based DNA Cryptography

Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personality characteristics, the authors use the same method as Liang et al. (2016), which extracted these characteristics based on a keyword-characteristic dictionary. For different types of malicious attacks, two raters rated each case based on criteria modified from criminology research to determine the degree of expressiveness and instrumentality. Findings The results show that malicious insiders who are manipulative or seeking personal gain tend to carry out instrumental attacks. Malicious insiders who are arrogant tend to conduct expressive attacks. Research limitations/implications This study uses third party articles to identify the personality characteristics of known malicious insiders. As such, not all personality characteristics may have been reported. Data availability was an issue. Practical implications Understanding if different personality characteristics lead different types of attacks can help managers identify employees who exhibit them and mitigate an attack before it occurs. Social implications Malicious insider attacks can have devastating results on businesses and employees. Help to identify potential malicious insiders before they act, may prevent undue harm. Originality/value This study used 132 cases of none malicious insiders to examine their attack objectives. No other study that the authors know of used that many cases.

The modern healthcare systems consider the secure transmission and storage of medical images as vital because of the sensitivity of patient data and the strict privacy regulations. This paper presents a secure and reversible RGB medical image scrambling and encryption scheme that aims to defend diagnostic images from unauthorized access, while, guaranteeing the integrity of the data. A 512- bit master key is generated through the memory-hard Argon2i algorithm which makes the system extremely resistant to brute-force and dictionary attacks. Domain-separated subkeys are generated using master key via AES in counter (CTR) mode. These subkeys are utilized at confusion and diffusion stages of the encryption and decryption process. The image confusion is performed with the use of a Fisher-Yates permutation which is controlled by cryptographically secure keystreams thereby disrupting spatial pixel relationships. The diffusion is done at both single colour channel and total colour channel levels utilizing a feedback-based mechanism that combines XOR operation, modular addition, and bit-level permutation. Both confusion and diffusion process ensures a strong avalanche effect across the entire image. Performance analysis shows that the proposed method provides an effective balance between security and computational efficiency, making it suitable for secure storage and transmission of medical images in telemedicine and healthcare information systems.

Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

The compression and transmission of medical image are the two key technology to realize the remote consultation system. It takes much energy in compression and transmission of medical image. Image collection, the ROI compression algorithm and JPEG2000, color image processing, and the network transmission technology based on TCP/IP are discussed in detail. It can realize the conversation function using HDP protocol. Using the technology of TCP/IP protocol and multi-thread it finished the image transmission function. It is difficult to make the image both seen by sender and receiver same in general transmission. So it takes all those technologies to send the whole image after compression the collection image, in order to avoid the different phenomenon. And it can transmit rapidly and steadily in some local region image. The remote consultation system called RDS2000 is developed successfully based those technologies. The system integrates the functions of video communication, character communication, image communication and remote control, and meanwhile it also verifies the validity and dependability of those technologies.

A new method for compact storage and transmission of medical images with concealed patient information in noisy environment is evinced. Digital watermarking is the technique adapted here for interleaving patient information with medical images. The patient information, which comprises of text data and signal graph, is encrypted to prevent unauthorized access of data. The latest encryption algorithm (Rijndael) is used for encrypting the text information. Signal graphs (ECG, EEG EMG etc.) are compressed using DPCM technique. To enhance the robustness of the embedded information, the patient information is coded by error correcting codes (ECC) such as (7,4) Hamming, Bose-Chaudhuri-Hocquenghem (BCH) and Reed Solomon (RS) codes .The noisy scenario is simulated by adding salt and pepper (S&P) noise to the embedded image. For different signal to noise ratio (SNR) of the image, bit error rate (BER) and number of character altered (NOCA) for text data and percentage distortion (PDIST) for the signal graph are evaluated. The performance comparison based on the above parameters is conducted for three types of ECC. It is elicited that coded systems can perform better than the uncoded systems.

Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some applications, lossless compression techniques are highly preferable against preferring lossy techniques in some applications. In other applications, near-lossless compression techniques are preferable to lossless and lossy compression techniques, where lossy ones may ‎lose significant details, and the lossless ones produce less compression ratios than near-lossless ones. Previous works dealt with Fluoroscopy images as individual images or using ‎video compression techniques. This work tends to handle the whole series of ‎images as an integrated object. This paper considers subtracting successive ‎images to detect ROI areas producing zero overall values over similar ‎areas and non-zero ones within ROI ones. The double coding technique and near-lossless concept of compression increase the compression ratio. ‎Conducted experiments showed encouraging results benchmarking the other published ‎works in medical image compression.‎

Digital Imaging and Communications in Medicine (DICOM) communication mainly focuses on the transmission of medical images, storing the information in medical image and also for printing and securing the image. A medical image communication is mainly for secured medical facilities for the physicians and the patient. The medical image is compressed under the JPEG 2000 format. The hash value is found out using additive hash function (AHF) and it is encrypted using RSA to form the digital signature. Combination of digital signature and text will be the watermark. This text consists of patient information, doctor information, disease information, and prescription. Reversible watermarking is a technique in which watermark is embedded and watermarked images passes through the authentication process, the original image is extracted along with the watermark. Strict authentication is provided in order to have high security for accessing the secure medical images by implementing Kerberos technique.

Medical images are of high volume and contain valuable diagnostic information, which have to be preserved against any archival or transmission error. Progressive transmission of data is favourable due to the fact that the region of interest (RoI) within the image can be coded without any visible error and that it speeds up the transmission of desired information due to the hierarchical region relation & transmission. In addition, this modality of communication has been modified to be interactive so the physicians can select the RoI to prioritize transmission of various regions. Wireless transmission of medical images involves construction of an effective source-channel coding to not only preserve the diagnostic information but also to enable progressively streaming the data from host to receiver [1]. A number of large medical images have been compressed and progressively transmitted with low bitrate. The blocking effect, as can be seen in JPEG coded images with the same compression ratio, has been highly reduced and the quality of the decoded images enhanced. A MATLAB-based TCP/IP connection has been established to demonstrate the proposed interactive progressive transmission system.

There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task. Moreover, there is an intrinsic bias in these adversarial attacks and defences to make matters worse. Here, we organise the problems faced: a) Model Dependence, b) Insufficient Evaluation, c) False Adversarial Samples, and d) Perturbation Dependent Results. Based on this, we propose a model agnostic adversarial robustness assessment method based on L0 and L∞ distance-based norms and the concept of robustness levels to tackle the problems. We validate our robustness assessment on several neural network architectures (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) and adversarial defences for image classification problem. The proposed robustness assessment reveals that the robustness may vary significantly depending on the metric used (i.e., L0 or L∞). Hence, the duality should be taken into account for a correct evaluation. Moreover, a mathematical derivation and a counter-example suggest that L1 and L2 metrics alone are not sufficient to avoid spurious adversarial samples. Interestingly, the threshold attack of the proposed assessment is a novel L∞ black-box adversarial method which requires even more minor perturbation than the One-Pixel Attack (only 12% of One-Pixel Attack’s amount of perturbation) to achieve similar results. We further show that all current networks and defences are vulnerable at all levels of robustness, suggesting that current networks and defences are only effective against a few attacks keeping the models vulnerable to different types of attacks.

Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

<p>Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of Pixel Change Rate) and Compression Ratio (CR). This Paper also introduces new mechanism for open network security for medical images. This lossless watermarking is responsible for recovering the altered medical image content of the system.</p>

In a present-day world there are different types of attacks being launched on computing devices. World is experiencing more and more cyber-attacks and types of attacks are also increasing. For example, an IoT device in a home network can act as a botnet attacking other devices or there could be man in the middle attack. As time goes by more and more devices are being connected within any given network. All these devices will be vulnerable to attacks if any one of the devices is compromised within the network. This complicates Intrusion Detection in any given network. Manual detection and intervention is nearly impossible. Hence it is quintessential to detect different types of attacks with more confidence with less computation complexity and time. A lot of research work has already been done in this area where the attacks have been studied separately. In this paper we focus on detecting intrusions including IoT botnet attacks and other types of network attacks. To achieve this, we build a multiclass classification using supervised learning models along with the dimensionality reduction technique. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD dataset. In this study we used a new dataset, IoT network Intrusion Detection dataset.

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.

The Internet of Medical Things (IoMT) is reforming healthcare by enabling interconnected medical devices and systems to facilitate efficient data collection, transmission, and analysis. While IoMT has significantly improved real-time monitoring and personalized care, the transmission of high-resolution medical images remains a challenge due to bandwidth constraints, latency issues, data loss, and computational overhead. Efficient and secure medical image transmission is critical to ensuring reliable diagnostics and timely patient care in this ecosystem. This research presents an optimized Deep Learning (DL) architecture developed to overcome the limitations of medical image transmission in IoMT environments. The proposed solution incorporates Convolutional Neural Networks (CNNs) for spatial feature extraction and dimensionality reduction while preserving diagnostic-critical information, and Long Short-Term Memory (LSTM) networks to manage sequential data and mitigate transmission issues such as packet loss and latency. The framework incorporates robust encryption mechanisms to ensure data security without significantly increasing computational overhead. Once predictions are made, the data is securely transferred to the cloud for further analysis and storage. Furthermore, Hippopotamus Optimization is utilised to enhance the model's performance and fine-tune hyperparameters, improving both efficiency and accuracy. Performance evaluations were conducted using real-world medical image datasets under varying IoMT network conditions. The results demonstrate that the proposed CNN-LSTM framework delivers superior performance across key metrics, like Peak Signal-to-Noise Ratio (PSNR), accuracy, F1 score, specificity, and sensitivity. Additionally, the framework optimizes encryption and decryption times and reduces bandwidth consumption, ensuring efficient and secure data transmission. This approach showcases a significant advancement in IoMT-based medical imaging, paving the way for enhanced reliability and efficiency in healthcare delivery systems.