Search Results (75)

With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and lower latency, which are widely applied in scenarios such as smart cities, the Internet of Things, and autonomous driving. The vast amounts of sensitive data generated by these applications become primary targets during the processes of collection and secure sharing, and unauthorized access or tampering could lead to severe data breaches and integrity issues. However, as 5G networks extensively employ encryption technologies to protect data transmission, attackers can hide malicious content within encrypted communication, rendering traditional content-based traffic detection methods ineffective for identifying malicious encrypted traffic. To address this challenge, this paper proposes a malicious encrypted traffic detection method based on reconstructive domain adaptation and adversarial hybrid neural networks. The proposed method integrates generative adversarial networks with ResNet, ResNeXt, and DenseNet to construct an adversarial hybrid neural network, aiming to tackle the challenges of encrypted traffic detection. On this basis, a reconstructive domain adaptation module is introduced to reduce the distribution discrepancy between the source domain and the target domain, thereby enhancing cross-domain detection capabilities. By preprocessing traffic data from public datasets, the proposed method is capable of extracting deep features from encrypted traffic without the need for decryption. The generator utilizes the adversarial hybrid neural network module to generate realistic malicious encrypted traffic samples, while the discriminator achieves sample classification through high-dimensional feature extraction. Additionally, the domain classifier within the reconstructive domain adaptation module further improves the model’s stability and generalization across different network environments and time periods. Experimental results demonstrate that the proposed method significantly improves the accuracy and efficiency of malicious encrypted traffic detection in 5G network environments, effectively enhancing the detection performance of malicious traffic in 5G networks. Full article

Existing generative image steganography methods typically encode secret information into latent vectors, which are transformed into the entangled features of generated images. This approach faces two main challenges: (1) Transmission can degrade the quality of stego-images, causing bit errors in information extraction. (2) High embedding capacity often reduces the accuracy of information extraction. To overcome these limitations, this paper presents a novel generative image steganography via encoding pose keypoints. This method employs an LSTM-based sequence generation model to embed secret information into the generation process of pose keypoint sequences. Each generated sequence is drawn as a keypoint connectivity graph, which serves as input with an original image to a trained pose-guided person image generation model (DPTN-TA) to generate an image with the target pose. The sender uploads the generated images to a public channel to transmit the secret information. On the receiver’s side, an improved YOLOv8 pose estimation model extracts the pose keypoints from the stego-images and decodes the embedded secret information using the sequence generation model. Extensive experiments on the DeepFashion dataset show that the proposed method significantly outperforms state-of-the-art methods in information extraction accuracy, achieving 99.94%. It also achieves an average hiding capacity of 178.4 bits per image. This method is robust against common image attacks, such as salt and pepper noise, median filtering, compression, and screenshots, with an average bit error rate of less than 0.87%. Additionally, the method is optimized for fast inference and lightweight deployment, enhancing its real-world applicability. Full article

An ecological understanding of threatened species provides the basis for their protection and recovery. This information must be used to analyze threats in order to propose conservation strategies for target species. River management projects, such as the construction of dikes, revetments, and dredging, are often undertaken to prevent flooding, and these activities affect fish communities and population dynamics. The critically endangered Pseudobagrus brevicorpus is highly vulnerable, but the causes of its decline are poorly understood. In this study, we assess the movements and habitat selection of P. brevicorpus to better understand its ecological characteristics and analyse the causes of its decline. We used radio telemetry to track the movements of the species and compared the effects of river-maintenance projects with data from a long-term study of the distribution of this endangered species. Total movements and home ranges were quite limited, with an average total distance traveled of 107.58 ± 66.01 m over an approximately 8-week monitoring period. The average MCP (minimum convex polygon) was 341.91 ± 776.35 m², the KDE (kernel density estimation) 50 was 76.01 ± 30.98 m², and the KDE 95 was 144.41 ± 58.86 m². The species is nocturnal, and during the day, individuals primarily hide among rocks and aquatic roots. The movement and habitat selection of P. brevicorpus indicated that the species could be directly or indirectly affected by river management. Acute population declines have been anticipated due to a lack of avoidance during management, and post-management habitat loss appears to have contributed to long-term population declines. Therefore, a strategic approach that considers ecological consequences is urgently needed to prevent the extinction of this species. Full article

Diffuse large B-cell lymphoma (DLBCL) is a malignancy of immense biological and clinical heterogeneity. Based on the transcriptomic or genomic approach, several different classification schemes have evolved over the years to subdivide DLBCL into clinically (prognostically) relevant subsets, but each leaves unclassified samples. Herein, we outline the DLBCL tumor biology behind the actual and potential drug targets and address the challenges and drawbacks coupled with their (potential) use. Therapeutic modalities are discussed, including small-molecule inhibitors, naked antibodies, antibody–drug conjugates, chimeric antigen receptors, bispecific antibodies and T-cell engagers, and immune checkpoint inhibitors. Candidate drugs explored in ongoing clinical trials are coupled with diverse toxicity issues and refractoriness to drugs. According to the literature on DLBCL, the promise for new therapeutic targets lies in epigenetic alterations, B-cell receptor and NF-κB pathways. Herein, we present putative targets hiding in lipid pathways, ferroptosis, and the gut microbiome that could be used in addition to immuno-chemotherapy to improve the general health status of DLBCL patients, thus increasing the chance of being cured. It may be time to devote more effort to exploring DLBCL metabolism to discover novel druggable targets. We also performed a bibliometric and knowledge-map analysis of the literature on DLBCL published from 2014–2023. Full article

Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure commonly investigated in related works is desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We investigate data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show that the proposed techniques work very well and improve the attack significantly, even for an order of magnitude. Full article

Community detection enables the discovery of more connected segments of complex networks. This capability is essential for effective network analysis. But, it raises a growing concern about the disclosure of user privacy since sensitive information may be over-mined by community detection algorithms. To address this issue, the problem of community detection attacks has emerged to subtly perturb the network structure so that the performance of community detection algorithms deteriorates. Three scales of this problem have been identified in the literature to achieve different levels of concealment, such as target node, target community, or global attack. A broad range of community detection attack algorithms has been proposed, utilizing various approaches to tackle the distinct requirements associated with each attack scale. However, existing surveys of the field usually concentrate on studies focusing on target community attacks. To be self-contained, this survey starts with an overview of community detection algorithms used on the other side, along with the performance measures employed to evaluate the effectiveness of the community detection attacks. The core of the survey is a systematic analysis of the algorithms proposed across all three scales of community detection attacks to provide a comprehensive overview. The survey wraps up with a detailed discussion related to the research opportunities of the field. Overall, the main objective of the survey is to provide a starting and diving point for scientists. Full article

Despite recent advancements in technology, breast cancer still poses a significant threat, often resulting in fatal consequences. While early detection and treatments have shown some promise, many breast cancer patients continue to struggle with the persistent fear of the disease returning. This fear is valid, as breast cancer cells can lay dormant for years before remerging, evading traditional treatments like a game of hide and seek. The biology of these dormant breast cancer cells presents a crucial yet poorly understood challenge in clinical settings. In this review, we aim to explore the mysterious world of dormant breast cancer cells and their significant impact on patient outcomes and prognosis. We shed light on the elusive role of the G9a enzyme and many other epigenetic factors in breast cancer recurrence, highlighting its potential as a target for eliminating dormant cancer cells and preventing disease relapse. Through this comprehensive review, we not only emphasise the urgency of unravelling the dynamics of dormant breast cancer cells to improve patient outcomes and advance personalised oncology but also provide a guide for fellow researchers. By clearly outlining the clinical and research gaps surrounding dormant breast cancer cells from a molecular perspective, we aim to inspire further exploration of this critical area, ultimately leading to improved patient care and treatment strategies. Full article

To address the vulnerability of the widely used block permutation and co-XOR (BPCX) encryption algorithm in reversible data-hiding in the encrypted domain (RDH-ED), which is susceptible to known-plaintext attacks (KPAs), and to enhance embedding capacity, we propose a novel technique of reversible data-hiding in encrypted images (RDH-EI). This method incorporates adaptive quadtree partitioning and most significant bit (MSB) prediction. To counteract KPAs, we introduce pixel modulation specifically targeting pixels within blocks of the same level during the encryption phase. During data embedding, we utilize tagging bits to indicate the state of the pixel blocks, capitalizing on pixel redundancy within those blocks to augment embedding capacity. Our experimental results demonstrate that our method not only achieves reversibility and separability but also significantly boosts embedding capacity and method security. Notably, the average embedding rate across the 10,000 images tested stands at 2.4731, surpassing previous methods by 0.2106 and 0.037, respectively. Full article

Human Immunodeficiency Virus (HIV) remains a significant global health challenge with approximately 38 million people currently having the virus worldwide. Despite advances in treatment development, the virus persists in the human population and still leads to new infections. The virus has a powerful ability to mutate and hide from the human immune system in reservoirs of the body. Current standard treatment with antiretroviral therapy effectively controls viral replication but requires lifelong adherence and does not eradicate the virus. This review explores the potential of Advanced Therapy Medicinal Products as novel therapeutic approaches to HIV, including cell therapy, immunisation strategies and gene therapy. Cell therapy, particularly chimeric antigen receptor T cell therapy, shows promise in preclinical studies for targeting and eliminating HIV-infected cells. Immunisation therapies, such as broadly neutralising antibodies are being investigated to control viral replication and reduce reservoirs. Despite setbacks in recent trials, vaccines remain a promising avenue for HIV therapy development. Gene therapy using technologies like CRISPR/Cas9 aims to modify cells to resist HIV infection or eliminate infected cells. Challenges such as off-target effects, delivery efficiency and ethical considerations persist in gene therapy for HIV. Future directions require further research to assess the safety and efficacy of emerging therapies in clinical trials. Combined approaches may be necessary to achieve complete elimination of the HIV reservoir. Overall, advanced therapies offer new hope for advancing HIV treatment and moving closer to a cure. Full article

To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected. Full article

Location privacy is an important parameter to be addressed in the case of vehicular ad hoc networks. Each vehicle frequently communicates with location-based services to find the nearest location of interest. The location messages communicated with the location server may contain sensitive information like vehicle identity, location, direction, and other headings. A Location-Based Services (LBS) server is not a trusted entity; it can interact with an adversary, compromising the location information of vehicles on the road and providing a way for an adversary to extract the future location tracks of a target vehicle. The existing works consider two or three neighboring vehicles as a virtual shadow to conceal location information. However, they did not fully utilize the semantic location information and pseudonym-changing process, which reduces the privacy protection level. Moreover, a lot of dummy location messages are generated that increase overheads in the network. To address these issues, we propose a Semantic Group Obfuscation (SGO) technique that utilizes both location semantics as well as an efficient pseudonym-changing scheme. SGO creates groups of similar status vehicles on the road and selects random position coordinates for communication with the LBS server. It hides the actual location of a target vehicle in a vicinity. The simulation results verify that the proposed scheme SGO improves the anonymization and entropy of vehicles, and it reduces the location traceability and overheads in the network in terms of computation cost and communication cost. The cost of overhead is reduced by 55% to 65% compared with existing schemes. We also formally model and specify SGO using High-Level Petri Nets (HLPNs), which show the correctness and appropriateness of the scheme. Full article

In this paper, we investigate deep-learning-based image inpainting techniques for emergency remote sensing mapping. Image inpainting can generate fabricated targets to conceal real-world private structures and ensure informational privacy. However, casual inpainting outputs may seem incongruous within original contexts. In addition, the residuals of original targets may persist in the hiding results. A Residual Attention Target-Hiding (RATH) model has been proposed to address these limitations for remote sensing target hiding. The RATH model introduces the residual attention mechanism to replace gated convolutions, thereby reducing parameters, mitigating gradient issues, and learning the distribution of targets present in the original images. Furthermore, this paper modifies the fusion module in the contextual attention layer to enlarge the fusion patch size. We extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset proved the efficiency of RATH for image inpainting and target hiding. RATH had the highest similarity, with a 90.44% structural similarity index metric (SSIM), for edge-guided target hiding. The training parameters had 1M fewer values than gated convolution (Gated Conv). Finally, we present two automated target-hiding techniques that integrate semantic segmentation with direct target hiding or edge-guided synthesis for remote sensing mapping applications. Full article

Innovations can offer key advantages to companies, but in some EU regions, the design and development of innovation measures are still relatively novel concepts. The aim of this study was to analyze the collaborations of innovative Bulgarian furniture manufacturers with external stakeholders and the used information channels as factors for the development and implementation of innovation and participation in global value chains over their innovation activities. Out of 3890 Bulgarian companies, the number of companies included in the target group was further reduced to 85 firms due to missing information on some variables. The data for the present study were collected using a large-scale questionnaire distributed on the spot during the months of March and April 2022. Logistic regression was used to reveal the real contribution of the collaborations and the information sources to the ability of companies to innovate. The research results indicated that in Bulgaria, the furniture sector is not considered very innovative, and Bulgarian furniture manufacturing companies do not rely on collaboration with the IT and mechatronics sectors. These companies do not want to participate in GVCs, as they refer to them in relation to supply chains. Therefore, they are less dependent on chain shocks. Companies prefer to hide their innovations for further protection, which might be the reason for the lack of cooperation between the furniture manufacturing companies and academia, NGOs, and other relevant institutions. The findings of the study contribute to new insights into the literature on the participation in GVCs as a factor for collaboration with different stakeholders and hence for product and process innovation development within the furniture industry companies. Full article

To represent the composition of small molecules circulating in HepG2 cells and the formation of the “core” of characteristic metabolites that often attract researchers’ attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets. Full article

Panax notoginseng (P. notoginseng) has excellent medicinal and food dual-use characteristics. However, P. notoginseng with a unique origin label has become the target of fraud because of people confusing or hiding its origin. In this study, an untargeted nuclear magnetic resonance (NMR)-based metabolomics approach was used to discriminate the geographical origins of P. notoginseng from four major producing areas in China. Fifty-two components, including various saccharides, amino acids, saponins, organic acids, and alcohols, were identified and quantified through the NMR spectrum, and the area-specific geographical identification components were further screened. P. notoginseng from Yunnan had strong hypoglycemic and cardiovascular protective effects due to its high acetic acid, dopamine, and serine content, while P. notoginseng from Sichuan was more beneficial for diseases of the nervous system because of its high content of fumarate. P. notoginseng from Guizhou and Tibet had high contents of malic acid, notoginsenoside R1, and amino acids. Our results can help to distinguish the geographical origin of P. notoginseng and are readily available for nutritional recommendations in human consumption. Full article