A Novel and Robust Hybrid Blockchain and Steganography Scheme

Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.

Insights into Artificial Neural Network techniques, and its Application in Steganography

Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions.

Image Steganography using 3 ways of Encryption for Highly Secured Data Transmission

Steganography exists from many years in a variety of forms and has been used in a variety of domains. There are various types of steganographic techniques that are used to hide data in various file formats. The proposed model uses three layers of security using three algorithms namely, LSB, DES and AES. The software allows the user to encrypt the secret message which gets protected by undergoing three layers of encryption. The output of the encryption process which is the steno image can be sent across through any medium along with the secret key to decrypt. The end user then uses a unique key to extract and decrypt the secret message from the image. There are many reasons to hide data but the simplest is the need to prevent unauthorized persons from accessing the secret information.

Steganographic Analysis of Blockchains

Steganography is one of the ways to hide data between parties. Its use can be worrisome, e.g., to hide illegal communications. Researchers found that public blockchains can be an attractive place to hide communications; however, there is not much evidence of actual use in blockchains. Besides, previous work showed a lack of steganalysis methods for blockchains. In this context, we present a steganalysis approach for blockchains, evaluating it in Bitcoin and Ethereum, both popular cryptocurrencies. The main objective is to answer if one can find steganography in real case scenarios, focusing on LSB of addresses and nonces. Our sequential analysis included 253 GiB and 107 GiB of bitcoin and ethereum, respectively. We also analyzed up to 98 million bitcoin clusters. We found that bitcoin clusters could carry up to 360 KiB of hidden data if used for such a purpose. We have not found any concrete evidence of hidden data in the blockchains. The sequential analysis may not capture the perspective of the users of the blockchain network. In this case, we recommend clustering analysis, but it depends on the clustering method’s accuracy. Steganalysis is an essential aspect of blockchain security.

A systematic overview of secure image steganography

<p>Information is a vitalthing that needs to be secured and well protected during transmission between two or more parties over the internet. This can be achieved by steganography technology. Steganography is the concealing science in which the information is concealed inside other information in a way that the concealed information cannot be detectable by the human eye. Many ways are available to hide data inside a cover media for example text, image, and audio steganography, but image steganography is the most utilized technique among the others. Secure image steganography has a high-security level than traditional technique by combining steganography with cryptography due to encrypting secret information by cryptography algorithm before embedding it into the cover media by steganography algorithm. In this paper,a systematic review has been presented about secure image steganography and its renowned types. Many researchers proposed secure image steganography by using various cryptography and steganography algorithms which have been reviewed. The least significant bit ‘LSB’ was the renowned steganography algorithm which has been used by researchers due to its simplicity, while various cryptography algorithms like advanced encryption standard (AES) and blowfishhave been used to propose secure image steganography in the reviewed papers. The comparison among the reviewed papers indicated that the LSB with hash-RSA gave a greater peak signal-noise ratio ‘PSNR’ value than the others which was 74.0189 dB.</p>

New text steganography method using the arabic letters dots

<span>With the increasing technological and electronic development, methods have been developed to hide important information using text steganography as a new technology, since it is not noticeable and easy to send and receive. The use of the Arabic language is one of the new methods used to hide data. In this work, we preview our method that depends to use the part of Arabic language properties to embed the secret English message in to cover text to create text steganography. More than half of the Arabic characters contain dots. Several characters have upper dots and others have lower dots. Some have one dot others have two dots. Few have even three dots. In this new idea, we will use the dots of charters to embed the English secret message. First, we will compress the secret message by using the 5-Bit Encoding (T-5BE) to make the cover text able to embed more bits of the secret message by 37.5%. Then we start using the Arabic semantic dictionary to correct the hiding path and enhancement the stego-cover text to eliminate errors caused by switching words. In this research, we were able to extract experimental results that show that the proposed model achieves high masking accuracy in addition to the storage capacity of the cover text.</span>

Data Protection for Files and Logs in Fog Cloud Storage Using 3DES

Nowadays cloud computing is being used extensively in daily life. With these new computing and communication technologies, new data security challenges arise. Privacy, integrity, and availability are the primary goals of the security structure. Many authentication schemes have been introduced over the years to ensure better security and to provide a wider range of services with a faster perspective. Each type of data is stored in the cloud and can be easily accessed anytime, anywhere. But, it lags due to location awareness when it comes to privacy in cloud computing. Improved cloud computing uses a fog-centric secure plan to ensure logs and information against unapproved access, change, and extinction. To obstruct unauthorized entry, the current scheme uses a technology called Xor-Combination to hide data. Besides, Xor-Combination results in preventing malicious recovery and ensuring better recovery capacity in case of data loss and Intermediate Fog does not provide server security. Cloud server and log files have been proposed and improved to provide higher security and intermediate security differently compared to the existing system for storing data on the fog server. Log files also contain personal information with equally critical protection and confidentiality of log data as an alternative scheme for storing logs in a cloud environment. Our proposed security to protect log files and data files on the main server on the Intermediate Fog server. We use an enhanced 3DES security method that provides better security than the Xor-combination technique.

CoroBase

Data stalls are a major overhead in main-memory database engines due to the use of pointer-rich data structures. Lightweight coroutines ease the implementation of software prefetching to hide data stalls by overlapping computation and asynchronous data prefetching. Prior solutions, however, mainly focused on (1) individual components and operations and (2) intra-transaction batching that requires interface changes, breaking backward compatibility. It was not clear how they apply to a full database engine and how much end-to-end benefit they bring under various workloads. This paper presents CoroBase, a main-memory database engine that tackles these challenges with a new coroutine-to-transaction paradigm. Coroutine-to-transaction models transactions as coroutines and thus enables inter-transaction batching, avoiding application changes but retaining the benefits of prefetching. We show that on a 48-core server, CoroBase can perform close to 2x better for read-intensive workloads and remain competitive for workloads that inherently do not benefit from software prefetching.