A Slice Escape Detection Model Based on Full Flow Adaptive Detection

The 5G power trading private network increases network flexibility and lowers building costs with the aid of 5G and Access Point Name (APN) technology. However, the private network is facing a series of security problems, such as the lack of effective isolation between slices and malicious terminal damage in slices, which result in a large consumption of slice resource failures and even slice escape attacks. To solve this problem, we propose a slice escape detection model based on full flow adaptive detection. Firstly, we improve the "six-tuple" flow table features detection technology, and creatively proposed a set of "eleven-tuple" features scheme, so as to realize the adaptive detection of intra-slice and inter-slice escape attacks. Secondly, we construct a two-level detection model based on long short-term memory network and self-attention mechanism to improve detection efficiency and reduce false alarm rate. Thirdly, we design an exception handling module to handle the abnormally detected traffic. Our model has a high detection accuracy and a low false alarm rate for the slice escape assault, according to a large number of experiments on the CIC-DDoS2019 dataset, and the detection delay complies with the requirements for online detection

A robust multi-watermarking algorithm for medical images based on DTCWT-DCT and Henon map

To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was pre-processed using the Henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks

Recent advances in the interface design of solid-state electrolytes for solid-state energy storage devices

High-ionic-conductivity solid-state electrolytes (SSEs) have been extensively explored for electrochemical energy storage technologies because these materials can enhance the safety of solid-state energy storage devices (SSESDs) and increase the energy density of these devices. In this review, an overview of SSEs based on their classification, including inorganic ceramics, organic solid polymers, and organic/inorganic hybrid materials, is outlined. Related challenges, such as low ionic conductivity, high interfacial resistance between electrodes and SSEs, poor wettability, and low thermal stability, are discussed. In particular, recent advances in properties of SSEs and interface design of high-performance SSESDs are highlighted. Several interface designs, including hybrid, interlayer, solid-liquid, quasi-solid-state gel, and in situ solidification interface, between electrodes and SSEs for alleviating interfacial resistance, stability, and compatibility in SSESDs are comprehensively reviewed to provide insights into the future design directions of SSEs and SSESDs. The rational designs of various SSESDs for flexible and wearable devices, electronic devices, electric vehicles, and smart grid systems are proposed in accordance with different practical application requirements

Robust watermarking algorithm for medical volume data in internet of medical things

The advancement of 5G technology, big data and cloud storage has promoted the rapid development of the Internet of Medical Things (IoMT). Based on the strict security requirements and high level of accuracy required for disease diagnosis and pathological analysis, 3D medical volume data have been created in large numbers. The IoMT facilitates the rapid transfer of medical information and also makes the protection of pathological information and privacy information of patients increasingly prominent. To solve the security problem, a robust zero-watermarking algorithm based on 3D hyperchaos and 3D dual-tree complex wavelet transform is proposed according to the selected feature of medical volume data. The feature combines human visual features with improved perceptual hashing techniques. It is a robust and efficient binary sequence. When implementing the proposed algorithm, the watermark is first scrambled with 3D hyperchaos to enhance security. Then, 3D DTCWT-DCT transformation is applied to medical volume data, and the low-frequency coefficients that can represent the features are selected and binarized to generate the secret key to complete the watermark embedding and extraction. Zero embedding and blind extraction ensure that the original medical volume data is not altered in any form, which meets the special requirements for diagnosis. Simulation results show that the algorithm is robust and can effectively resist common attacks and geometric attacks. It used fewer robust features to effectively bound medical volume data and watermark information, saved bandwidth, and satisfied the security of transmission and storage of medical volume data in the Internet of medical things. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value by 46.67% under geometric attacks

Robust zero watermarking algorithm for medical images based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. *e algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. *e algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm

A novel robust watermarking algorithm for encrypted medical image based on DTCWT-DCT and chaotic map

In order to solve the problem of patient information security protection in medical images, whilst also taking into consideration the unchangeable particularity of medical images to the lesion area and the need for medical images themselves to be protected, a novel robust watermarking algorithm for encrypted medical images based on dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) and chaotic map is proposed in this paper. First, DTCWT-DCT transformation was performed on medical images, and dot product was per-formed in relation to the transformation matrix and logistic map. Inverse transformation was undertaken to obtain encrypted medical images. Then, in the low-frequency part of the DTCWT-DCT transformation coefficient of the encrypted medical image, a set of 32 bits visual feature vectors that can effectively resist geometric attacks are found to be the feature vector of the encrypted medical image by using perceptual hashing. After that, different logistic initial values and growth parameters were set to encrypt the watermark, and zero-watermark technology was used to embed and extract the encrypted medical images by combining cryptography and third-party concepts. The proposed watermarking algorithm does not change the region of interest of medical images thus it does not affect the judgment of doctors. Additionally, the security of the algorithm is enhanced by using chaotic mapping, which is sensitive to the initial value in order to encrypt the medical image and the watermark. The simulation results show that the pro-posed algorithm has good homomorphism, which can not only protect the original medical image and the watermark information, but can also embed and extract the watermark directly in the encrypted image, eliminating the potential risk of decrypting the embedded watermark and extracting watermark. Compared with the recent related research, the proposed algorithm solves the contradiction between robustness and invisibility of the watermarking algorithm for encrypted medical images, and it has good results against both conventional attacks and geometric attacks. Under geometric attacks in particular, the proposed algorithm performs much better than existing algorithms

The Preparation of Monomer Casting Polyamide 6/Thermotropic Liquid Crystalline Polymer Composite Materials with Satisfactory Miscibility

It is highly expected to develop a simple and effective method to reinforce polyamide 6 (PA6) to enlarge its application potential. This is challenging because of frequently encountered multi-component phase separations. In this paper, we propose a novel method to solve this issue, essentially comprising two steps. Firstly, a kind of poly (amide-block-aramid) block copolymers, i.e., thermotropic liquid crystalline polymer (TLCP)-polyamide 6 (TLCP-PA6), that contains both rigid aromatic liquid crystal blocks, and flexible alkyl blocks were synthesized. It is unique in that TLCP is chemically linked with PA6, which is advantageous in excellent chemical and physical miscibility with the precursors of monomer casting polyamide 6 (MCPA6), i.e., ε-caprolactam. Secondly, such newly synthesized block copolymer TLCP-PA6 was dissolved in the melting ε-caprolactam, and followed by in situ polymerization to obtain composite polymer blends, i.e., MCPA6/TLCP-PA6. The thermodynamic, morphological, and crystalline properties of MCPA6/TLCP-PA6 can be easily manipulated by tailoring the loading ratios between TLCP-PA6 and ε-caprolactam. Especially, at the optimized condition, such MCPA6/TLCP-PA6 blends show an excellent miscibility. Systematic characterizations, including nuclear magnetic resonance (NMR), Fourier-transform infrared spectroscopy (FT-IR), differential scanning calorimeter (DSC), and polarizing optical microscope (POM), were performed to confirm these statements. In view of these results, it is anticipated that the overall mechanical properties of such PA6-based polymer composites will be satisfactory, which should enable applications in the modern plastic industry and other emerging areas, such as wearable fabrics

Robust zero‐watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

Abstract In the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks

Attitudes of COVID-19 vaccination among college students: A systematic review and meta-analysis of willingness, associated determinants, and reasons for hesitancy

The significance of COVID-19 vaccine has been declared and this study synthesizes the attitudes and determinants in vaccination hesitancy of college students. We searched in PubMed, Web of Science, Cochrane Library and CNKI to enroll the related studies. The modified NOS was used for quality evaluation. Proportion and OR with 95% CI were pooled to estimate the acceptance rates and determinants of COVID-19 vaccination. Data of 34 studies involving 42 countries were pooled. The pooled acceptance rate of COVID-19 vaccination among all the college students was 69% and varies between countries, while medical students have a slightly higher acceptancy rate. Knowledge, trust conception, social behavior, and information sources were important for their decision. Most of the college students intended to COVID-19 vaccination, but the proportion varied among countries. Governments should strengthen credibility, convey trusted information with media influences and improve vaccination services in urging students to be vaccinated