An embedding traid-bit method to improve the performance of Arabic text steganography

The enormous development in the utilization of the Internet has driven by continuous improvements in the region of security. The enhanced security techniques are applied to save the intellectual property. There are numerous sorts of security mechanisms. Steganography is the art and science of concealing secret information inside a cover media without drawing any suspicion to the eavesdropper so that the secret information can only be detected by its proposed recipient. This is done along with the other steganography methods such as image, audio, video, various text steganography methods that are being presented. The text is ideal for steganography due to its ubiquity. There are many steganography methods used several languages such as English, Chines and Arabic language to embed the hidden message in the cover text. Kashida, shifting point and sharp_edges are Arabic steganography methods with high capacity. However, kashida, shifting point and sharp_edges techniques have lack of capability to embed the hidden message into the cover text. This study proposed new method called Traid-bit method by integrating three several types of methods such us kashida, shifting point and sharp_edges to evaluate the proposed method in improving the performance of embedding process. The study presents the process design of proposed method including the algorithms and the system design. The study found that the evaluation of the proposed method provides good knowledge to steganographer to improve the performance of embedding process when the Arabic text steganography method is developed

Character property method for Arabic text steganography with biometric multifactor authentication using liveness detection

Arabic text steganography (ATS) offers a potential opportunity in hiding secret information in characters and features. The combination with any other security sub discipline such as cryptography usually will enhance its level of security. However, it is limited in its ability to optimize embedded data capacity with a high perceptual transparency level that will also not raise suspicion when written. Besides that, other concerns are active attacks by intruders which are a crucial security issue in the transmission of the shared secret key that enables the receiver to extract the secret information. Also, such attacks can be affected through a fake identity that allows the receiver to modify the secret information thus degrading its integrity. To overcome these drawbacks, we propose a hybrid ATS with biometric multi factor authentication (BMA), which uses liveness detection using fingerprints and heartbeat sensors as the authentication factors. We propose a new ATS method, the Character Property method (CPM) which uses the basic properties of the Arabic Text such as dots, calligraphy typographical proportions, and sharp-edges to hide the secret message using a table index mapping technique to optimize data capacity with high perceptual transparency to avert suspicion. The results for the biometric authentication showed that the proposed method correctly authenticates users, having a false rejection rate of only 4%, and a 0% false acceptance rate. As for liveness detection, the results were significant where the proposed method correctly detected live subjects compared to a fingerprint only biometric authentication approach, which had a high acceptance of fake inputs. BMA was implemented through a custom Arduino smartwatch with a fingerprint and heartbeat sensor as a ‘proof-of-concept’ device which increased the capacity in hiding the secret message up to 23.5% compared to the previous methods. Given our Arabic Character Properties method (CPM) did not affect the stego-text appearance, its 1.0 Jaro Similarity score was compared to the other methods proving high transparency of the stego-text, in addition to higher security regarding user authentication using BMA with liveness detection

Dynamic colour text steganography model using rgb coding and character spacing to improve capacity, invisibility and security

Protecting sensitive information transmitted via public channels is a significant issue faced by governments, militaries, organizations, and individuals. Steganography protects the secret information by concealing it in a transferred object such as video, audio, image, and text. Text is an ideal object for steganography as it uses low bandwidth and is commonly used. Exploit of text features such as font attributes in text steganography has been proposed, however, many existing feature-based text steganography methods suffer from low capacity, weak invisibility and poor security: low capacity is caused by embedding fewer bits per location, utilizing less usable characters, and low compression efficiency; weak invisibility is due to increased colour differences between the cover and the stego text; while poor security resulted from constant mapping and the permanent sequence selection of embedding positions for the hidden message and stego key. To overcome these problems this study proposed the Colour-spacing Text Stego (DCTS) model, which includes four new techniques: a Secret-block & Colour-spacing Matrices Generation (SCMG) technique to achieve high capacity; the Colour Spacing Normalization (CSN) technique to enhance invisibility; and proposed two techniques for two security layers, i.e. the first security layer, the Dynamic Selection of Embedding Positions (DSEP) technique, which hides the secret message and stego key in dynamic positions; and the second security layer, the Dynamic Colour Spacing Mapping (DCSM), which maps the secret message change dynamically. The results of the study found that the DCTS model produces better performance with a high capacity of 98.85% in a small used space by 5.79%, as well as increases the bits per location by 16 bits. Also, it maintains high invisibility by 5.07% when applying black or coloured cover text. With two security layers, the proposed DCTS achieves high security compared to the existing methods. To conclude, the Dynamic Colour-spacing Text Stego-model (DCTS) embeds a high secret data capacity while maintaining invisibility and security. DCTS model offers a new perspective on feature-based text steganography to protect against visual and statistical attack issues

Traid-bit embedding process on Arabic text steganography method

The enormous development in the utilization of the Internet has driven by a continuous improvement in the region of security. The enhancement of the security embedded techniques is applied to save the intellectual property. There are numerous types of security mechanisms. Steganography is the art and science of concealing secret information inside a cover media such as image, audio, video and text, without drawing any suspicion to the eavesdropper. The text is ideal for steganography due to its ubiquity. There are many steganography embedded techniques used Arabic language to embed the hidden message in the cover text. Kashida, Shifting Point and Sharp-edges are the three Arabic steganography embedded techniques with high capacity. However, these three techniques have lack of performance to embed the hidden message into the cover text. This paper present about traid-bit method by integrating these three Arabic text steganography embedded techniques. It is an effective way to evaluate many embedded techniques at the same time, and introduced one solution for various cases

CSNTSteg: color spacing normalization text steganography model to improve capacity and invisibility of hidden data

The rapid growth of online communication has increased the demand for secure communication. Most government entities, healthcare providers, the legal sector, financial and banking, and other industries are vulnerable to information security issues. Text steganography is one way to protect secure communication by hiding secret messages in the cover text. Hiding a large amount of secret information without raising the attacker’s suspicion is the main challenge in steganography. This paper proposes the Color and Spacing Normalization stego (CSNTSteg) model to resolve the low capacity and invisibility problems in text steganography. CSNTSteg consists of two stages: the pre-embedding stage, which achieves high capacity by utilizing RGB coding and character spacing. It is designed to increase the number of bits per location and usable characters. Besides, it applies the Huffman coding technique to compress the secret message to add more capacity enhancement. The second stage is color and space normalization, which accomplishes high invisibility by normalizing the RGB coding and character spacing of the cover and stego text. CSNTSteg overcomes the color difference issue between the cover and stego texts regardless of the color of the cover text. To assess the quality of CSNTSteg, the experimental results are compared with existing works. CSNTSteg shows superior capacity over the existing studies with a percentage of 98.85%. CSNTSteg also achieves high invisibility by reducing the color difference with a percentage of 4.7% and 5.07% for black and colored cover text, respectively. Furthermore, CSNTSteg improves robustness by 94.22% by reducing the distortion in stego text. Overall, the CSNTSteg model embeds a high capacity of secret data while maintaining invisibility and security, offering a new perspective on text steganography to protect against visual and statistical attack issues

A secure edge computing model using machine learning and IDS to detect and isolate intruders.

The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. [Abstract copyright: © 2024 The Author(s).