Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such retrieval involves certain limitations, such as manual image annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production of less accurate results. To overcome these issues, an effective image retrieval method is proposed in this study. This work intends to effectively retrieve images using a best feature extraction process. In the preprocessing of this study, a Gaussian filtering technique is used to remove the unwanted data present in the dataset. After preprocessing, feature extraction is applied to extract features, such as texture and color. Here, the texture feature is categorized as a gray level cooccurrence matrix, whereas the novel statistical and color features are considered image intensity-based color features. These features are clustered by k-means clustering for label formation. A modified genetic algorithm is used to optimize the features, and these features are classified using a novel SVM-based convolutional neural network (NSVMBCNN). Then, the performance is evaluated in terms of sensitivity, specificity, precision, recall, retrieval and recognition rate. The proposed feature extraction and modified genetic algorithm-based optimization technique outperforms existing techniques in experiments, with four different datasets used to test the proposed model. The performance of the proposed method is also better than those of the existing (RVM) regression vector machine, DSCOP, as well as the local directional order pattern (LDOP) and color co-occurrence feature + bit pattern feature (CCF + BPF) methods, in terms of the precision, recall, accuracy, sensitivity and specificity of the NSVMBCNN.

Data security can involve embedding hidden images, text, audio, or video files within other media to prevent hackers from stealing encrypted data. Existing mechanisms suffer from a high risk of security breaches or large computational costs, however. The method proposed in this work incorporates low-complexity encryption and steganography mechanisms to enhance security during transmission while lowering computational complexity. In message encryption, it is recommended that text file data slicing in binary representation, to achieve different lengths of string, be conducted before text file data masking based on the lightweight Lucas series and mod function to ensure the retrieval of text messages is impossible. The steganography algorithm starts by generating a random key stream using a hybrid of two low-complexity chaotic maps, the Tent map and the Ikeda map. By finding a position vector parallel to the input image vector, these keys are used based on the previously generated position vector to randomly select input image data and create four vectors that can be later used as input for the Lah transform. In this paper, we present an approach for hiding encrypted text files using LSB colour image steganography by applying a low-complexity XOR operation to the most significant bits in 24-bit colour cover images. It is necessary to perform inverse Lah transformation to recover the image pixels and ensure that invisible data cannot be retrieved in a particular sequence. Evaluation of the quality of the resulting stego-images and comparison with other ways of performing encryption and message concealment shows that the stego-image has a higher PSNR, a lower MSE, and an SSIM value close to one, illustrating the suitability of the proposed method. It is also considered lightweight in terms of having lower computational overhead.

The continuous increase in the use of smart devices and the need for E2E smart2smart (S2S) services in IoT systems play effective and contemporary roles in the field of communication, and a large amount of resources is required. Thus, IoTs and cloud computing must be integrated. One of the results of this integration is the increase in the number of attacks and vulnerabilities in the E2E S2S message delivery service of such an IoT-cloud system. However, none of the traditional security solutions can be sufficiently regarded as a secure and lightweight mechanism for ensuring that the security requirements for E2E S2S message transmission in the IoT-cloud system are fulfilled. This work aims to provide an efficient and secure, lightweight E2E S2S message delivery function, which includes the E2E S2S secure key and biometric parameter exchange function, a bio-shared parameter and bio-key generation function, secure lightweight E2E S2S communication negotiation and secure E2E S2S lightweight message delivery. The secure, lightweight cryptographic communication procedure is negotiated between a pair of smart devices during each E2E session to minimize the power consumption required of limited-energy devices. Such a negotiation process prevents known attacks by providing responsive mutual authentication. Lightweight message delivery by the two smart devices can satisfy the basic security requirements of E2E communication and ensure that the computational cost required for a real-time system is as low as possible.

Data security pressing issue, particularly in terms of ensuring secure and reliable data transfer over a network. Encryption and seganography play a fundamental role in the task of securing data exchanging. In this article, both steganography and cryptography were combined to produce a powerful hybrid securing stego-system. Firstly, a text message is encrypted with a new method using a bits cycling operation to give a cipher text. In the second stage, an enhanced LSB method is used to hide the text bits randomly in an audio file of a wav format. This hybrid method can provide effectually secure data. Peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structural similarity (SSIM) were employed to evaluate the performance of the proposed system. A PSNR was in range (60-65) dB with the enhanced least significant bit (LSB) and the SSIM had been invested to calculate the signal quality, which scored 0.999. The experimental results demonstrated that our algorithm is highly effective in securing data and the capacity size of the secured text. Furthermore, the time consumption was considerably low, at less than 0.3 seconds.

Wireless networks have continued to evolve to offer connectivity between users and smart devices such as drones and wireless sensor nodes. In this environment, insecure public channels are deployed to link the users to their remote smart devices. Some of the application areas of these smart devices include military surveillance and healthcare monitoring. Since the data collected and transmitted to the users are highly sensitive and private, any leakages can have adverse effects. As such, strong entity authentication should be implemented before any access is granted in these wireless networks. Although numerous protocols have been developed for this purpose, the simultaneous attainment of robust security and privacy at low latencies, execution time and bandwidth remains a mirage. In this paper, a session-dependent token-based payload enciphering scheme for integrity enhancements in wireless networks is presented. This protocol amalgamates fuzzy extraction with extended Chebyshev chaotic maps to boost the integrity of the exchanged payload. The security analysis shows that this scheme offers entity anonymity and backward and forward key secrecy. In addition, it is demonstrated to be robust against secret ephemeral leakage, side-channeling, man-in-the-middle and impersonation attacks, among other security threats. From the performance perspective, the proposed scheme requires the least communication overheads and a relatively low execution time during the authentication process.