Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.Comment: 6 pages, 9 figure

A systematic literature review of cloud computing in eHealth

Cloud computing in eHealth is an emerging area for only few years. There needs to identify the state of the art and pinpoint challenges and possible directions for researchers and applications developers. Based on this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1) cloud-based eHealth framework design (n=13); (2) applications of cloud computing (n=17); and (3) security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the review were about designs and concept-proof. Only very few studies have evaluated their research in the real world, which may indicate that the application of cloud computing in eHealth is still very immature. However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and security protection mechanisms will be a main research area for developing citizen centred home-based healthcare applications

Security of Electronic Patient Record using Imperceptible DCT-SVD based Audio Watermarking Technique

Abstract—A robust and highly imperceptible audio watermarkingtechnique is presented to secure the electronic patientrecord of Parkinson’s Disease (PD) affected patient. The proposedDCT-SVD based watermarking technique introduces minimalchanges in speech such that the accuracy in classification of PDaffected person’s speech and healthy person’s speech is retained.To achieve high imperceptibility the voiced part of the speech isconsidered for embedding the watermark. It is shown that theproposed watermarking technique is robust to common signalprocessing attacks. The practicability of the proposed technique istested: by creating an android application to record & watermarkthe speech signal. The classification of PD affected speech is doneusing Support Vector Machine (SVM) classifier in cloud server

Watermarking Based Image Authentication for Secure Color Image Retrieval in Large Scale Image Databases

An important facet of traditional retrieval models is that they retrieve images and videos and consider their content and context reliable. Nevertheless, this consideration is no longer valid since they can be faked for many reasons and at different degrees thanks to powerful multimedia manipulation software. Our goal is to investigate new ways detecting possible fake in social network platforms. In this paper, we propose an approach that assets identification faked images by combining standard content-based image retrieval (CBIR) techniques and watermarking. We have prepared the wartermarked image database of all images using LSB based watermarking. Using gabor features and trained KNN, user is able to retrieve the matching query image. The retrieved image is authenticated by extracting the watermark and matching it again with the test image

Comparative Study and Design Light Weight Data Security System for Secure Data Transmission in Internet of Things

Internet of things is shortened as IoT. Today IoT is a key and abrogating subject of the specialized and social importance. Results of buyers, things and vehicles, industry based and fundamental segments, sensors, and other everyday items are converged with network of internet and the solid information abilities which guarantee to change the sort in which we work and live. The proposed work demonstrates the implementation of symmetric key lightweight algorithm for secured data transmission of images and text using image encryption system as well as reversible data hiding system. In this paper, implemented symmetric key cryptography for various formats of images, as well as real time image acquisition system has been designed in the form of graphical user interface. Reversible data hiding system has also been designed for secure data transmission system

Biometric Security Through Visual Encryption for Fog Edge Computing

Fog and mobile edge computing have gained considerable attention from the research and development community. The problems related to security and privacy of biometric content are simpler to solve through edge computing resulting in improved security and privacy of biometric and other critically private information. Zero-watermarking has been proposed as a solution to help protect the ownership of multimedia content that is easy to copy and distribute. Visual cryptography is another approach to secure data that is to be shared through generating multiple shares. This paper is concerned with developing a biometric security solution for face images, using visual cryptography and zero-watermarking, that does not adversely impact the visual quality of the image. The original face image is not modified through the zero-watermarking and visual encryption procedures and this in turn does not adversely impact the recognition rate

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)