Search Results (4,252)

There is currently no effective therapeutic capable of arresting or inducing regression of primary or metastatic brain cancers. This article presents both pre-clinical and clinical studies supportive that a new bioengineered technology could induce regression and/or elimination of primary and metastatic brain cancers through three disease-modifying mechanisms. Transcranial Radiofrequency Wave Treatment (TRFT) is non-thermal, non-invasive and self-administered in-home to safely provide radiofrequency waves to the entire human brain. Since TRFT has already been shown to stop and reverse the cognitive decline of Alzheimer’s Disease in small studies, evidence is provided that three key mechanisms of TRFT action, alone or in synergy, could effectively treat brain cancers: (1) enhancement of brain meningeal lymph flow to increase immune trafficking between the brain cancer and cervical lymph nodes, resulting in a robust immune attack on the brain cancer; (2) rebalancing of the immune system’s cytokines within the brain or brain cancer environment to decrease inflammation therein and thus make for an inhospitable environment for brain cancer growth; (3) direct anti-proliferation/antigrowth affects within the brain tumor microenvironment. Importantly, these mechanisms of TRFT action could be effective against both visualized brain tumors and those that are yet too small to be identified through brain imaging. The existing animal and human clinical evidence presented in this perspective article justifies TRFT to be clinically tested immediately against both primary and metastatic brain cancers as monotherapy or possibly in combination with immune checkpoint inhibitors. Full article

Advanced Persistent Threats (APTs) pose significant cybersecurity challenges due to their multi-stage complexity. Knowledge graphs (KGs) effectively model APT attack processes through node-link architectures; however, the scarcity of high-quality, annotated datasets limits research progress. The primary challenge lies in balancing annotation cost and quality, particularly due to the lack of quality assessment methods for graph annotation data. This study addresses these issues by extending existing APT ontology definitions and developing a dynamic, trustworthy annotation framework for APT knowledge graphs. The framework introduces a self-verification mechanism utilizing large language model (LLM) annotation consistency and establishes a comprehensive graph data metric system for problem localization in annotated data. This metric system, based on structural properties, logical consistency, and APT attack chain characteristics, comprehensively evaluates annotation quality across representation, syntax semantics, and topological structure. Experimental results show that this framework significantly reduces annotation costs while maintaining quality. Using this framework, we constructed LAPTKG, a reliable dataset containing over 10,000 entities and relations. Baseline evaluations show substantial improvements in entity and relation extraction performance after metric correction, validating the framework’s effectiveness in reliable APT knowledge graph dataset construction. Full article

Due to the rapid expansion of Internet of Things devices and their associated network, security has become a critical concern, necessitating the development of reliable security mechanisms. Anomaly-based NIDS leveraging machine learning and deep learning have emerged as key solutions in detecting abnormal network traffic patterns. However, one challenge that affects the detection rate of machine learning or deep learning-based anomaly NIDS is the class data imbalance present in the existing dataset. Datasets are crucial for the development and evaluation of anomaly-based NIDS for IoT systems. In this study, we introduce EmuIoT-VT, a dataset generated by creating virtual replicas of IoT devices implementing a novel emulation-based method, enabling realistic network traffic generation without relying on any external network emulators. The data was collected in an isolated offline environment to capture clean, uncontaminated network traffic. The EmuIoT-VT is balanced-by-design, containing 28,000 labeled records that are evenly distributed across devices, classes, and subclasses, and supports both binary and multiclass classification tasks. It includes 82 features extracted from raw PCAP data and includes attack categories such as DoS, brute force, reconnaissance, and exploitation. This article presents the novel method and creation of the EmuIoT-VT dataset, detailing data collection, balancing strategy, and details of the dataset structure, and proposes directions for future work. Full article

As Industrial Control Systems (ICSs) increasingly adopt cloud–edge collaborative architectures, they face escalating risks from complex cross-domain cyber threats. To address this challenge, we propose a novel threat assessment framework specifically designed for cloud–edge-integrated ICSs. Our approach systematically identifies and evaluates security risks across cyber and physical boundaries by building a structured dataset of ICS assets, attack entry points, techniques, and impacts. We introduce a unique set of evaluation indicators spanning four key dimensions—system modules, attack paths, attack methods, and potential impacts—providing a holistic view of cyber threats. Through simulation experiments on a representative ICS scenario inspired by real-world attacks, we demonstrate the framework’s effectiveness in detecting vulnerabilities and quantifying security posture improvements. Our results underscore the framework’s practical utility in guiding targeted defense strategies and its potential to advance research on cloud–edge ICS security. This work not only fills gaps in the existing methodologies but also provides new insights and tools applicable to sectors such as smart grids, intelligent manufacturing, and critical infrastructure protection. Full article

As the Internet of Things (IoT) expands, ensuring secure and efficient image transmission in resource-limited environments has become crucial and important. In this paper, we propose a lightweight image encryption scheme based on Elliptic Curve Cryptography (ECC), tailored for embedded and IoT applications. In this scheme, the image data blocks are mapped into elliptic curve points using a decimal embedding algorithm and shuffled to improve resistance to tampering and noise. Moreover, an OTP-like operation is applied to enhance the security while avoiding expensive point multiplications. The proposed scheme meets privacy and cybersecurity requirements with low computational costs. Classical security metrics such as entropy, correlation, NPCR, UACI, and key sensitivity confirm its strong robustness. Rather than relying solely on direct comparisons with existing benchmarks, we employ rigorous statistical analyses to objectively validate the encryption scheme’s robustness and security. Furthermore, we propose a formal security analysis that demonstrates the resistance of the new scheme to chosen-plaintext attacks and noise and cropping attacks, while the GLCM analysis confirms the visual encryption quality. Our scheme performs the encryption of a $512\times 512$ image in only 0.23 s on a 1 GB RAM virtual machine, showing its efficiency and suitability for real-time IoT systems. Our method can be easily applied to guarantee the security and the protection of lightweight data in future smart environments. Full article

This paper presents a cross-layer intrusion detection framework leveraging explainable artificial intelligence (XAI) and interpretability methods to enhance transparency and robustness in attack detection within the Internet of Medical Things (IoMT) domain. By addressing the dual challenges of compromised data integrity, which span both biosensor and network-layer data, this study combines advanced techniques to enhance interpretability, accuracy, and trust. Unlike conventional flow-based intrusion detection systems that primarily rely on transport-layer statistics, the proposed framework operates directly on raw packet-level features and application-layer semantics, including MQTT message types, payload entropy, and topic structures. The key contributions of this research include the application of K-Means clustering combined with the principal component analysis (PCA) algorthim for initial categorization of attack types, the use of SHapley Additive exPlanations (SHAP) for feature prioritization to identify the most influential factors in model predictions, and the employment of Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) to elucidate feature interactions across layers. These methods enhance the system’s interpretability, making data-driven decisions more accessible to nontechnical stakeholders. Evaluation on a realistic healthcare IoMT testbed demonstrates significant improvements in detection accuracy and decision-making transparency. Furthermore, the proposed approach highlights the effectiveness of explainable and cross-layer intrusion detection for secure and trustworthy medical IoT environments that are tailored for cybersecurity analysts and healthcare stakeholders. Full article

Accelerations and decelerations are critical components of soccer performance, reflecting mechanical load and injury risk, with understanding positional and temporal variations essential for optimizing training prescription. This study analyzed acceleration and deceleration demands in professional soccer players across playing positions and training microcycle phases. Twenty-five professional soccer players (26.6 ± 4.50 years) from a Spanish Second Division team were monitored using 18 Hz GPS STATSports (Newry, UK) devices during 16 training sessions and 4 official matches over four weeks. Accelerations and decelerations were categorized into six intensity zones (Z1–Z6, 0.5–1 to 5–10 m/s²), with players grouped by position: central defenders (CD), full-backs (FB), central midfielders (CM), attacking midfielders (AM) and forwards (FW). Match day (MD) significantly affected all variables (F > 4.75; p < 0.001, ω_p² = 0.13–0.42), with accelerations showing higher values at MD-2 for Z1, MD for Z2, MD-4 and MD for Z3–Z4, consistently reaching lowest values at MD-1. Decelerations peaked at MD across Z2–Z6, with MD-1 showing minimal preparation values. Positionally, FB exceeded other positions in low-intensity accelerations and decelerations (Z1–Z2), while CM dominated high-intensity decelerations (Z4–Z6). Total accelerations differed significantly by position (FB: 579 ± 163 vs. AM: 494 ± 184 events, p < 0.05). Training acceleration loads adequately replicate match demands, but deceleration preparation remains insufficient, representing a potential injury risk. Position-specific protocols should emphasize deceleration conditioning, particularly for CM and FB. Full article

Plant density influences interspecific interactions such as pollination and herbivory. In denser populations, pollinators find flowers more easily, increasing reproductive success and population growth. However, the same floral attractiveness also favors floral herbivory, a relationship described by Janzen and Connell as negative density dependence, considered an important mechanism for maintaining tropical diversity. This study analyzed the reproduction of Peixotoa tomentosa A. Juss. (Malpighiaceae) and Byrsonima intermedia A. Juss. (Malpighiaceae), considering population density and its influence on pollinator and herbivore attraction. The central hypothesis was that density affects fruit production. We conducted two treatments with both species: high density and low density in a preserved Brazilian savanna. We investigated fruit production, reproductive system, floral visitation rates, and the florivory rates of each species on each treatment. Our results showed that fruiting increased with density in both species. Peixotoa tomentosa is an agamospermous species, while B. intermedia is self-incompatible and relies exclusively on pollinators. Bees visited only B. intermedia, and the high-density treatment received more visits. Herbivores attacked more isolated P. tomentosa flowers. We concluded that density influences both pollination and herbivory, affecting plant reproduction, with effects mediated by the plant’s attractiveness in denser populations and by the size and quantity of flowers in single individuals. Full article

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. Full article

Digital cash is coming, and it could be programmed to behave in novel ways. In 2020, the People’s Bank of China ran an experiment during which they distributed free digital cash to 50,000 citizens. But it had a twist: they programmed that digital cash to expire if not spent within a few days. This fascinating and somewhat paradoxical experiment opens many questions. If the cash expires, why would anyone accept it as payment? If it is intended to expire, can the recipient find ways to make it not expire? We explore a variety of possible attacks on expiring cash, countermeasures to those attacks, and alternative implementations, one based on CBDC and another on a public blockchain. We also discuss the more philosophical question of whether expiring cash is still cash: we argue it cannot be. Full article

The industrialization of cybercrime, principally through Malware-as-a-Service (MaaS), has elevated HTTP cookie theft to a critical cybersecurity challenge, enabling attackers to bypass multi-factor authentication and perpetrate large-scale account takeovers. Employing a Holistic and Integrative Review methodology, this paper dissects the intricate, adaptive ecosystem of MaaS-driven cookie theft. We systematically characterize the co-evolving arms race between offensive and defensive strategies (2020–2025), revealing a critical strategic asymmetry where attackers optimize for speed and low cost, while effective defenses demand significant resources. To shift security from a reactive to an anticipatory posture, a multi-dimensional predictive framework is not only proposed but is also detailed as a formalized, testable algorithm, integrating technical, economic, and behavioral indicators to forecast emerging threat trajectories. Our findings conclude that long-term security hinges on disrupting the underlying cybercriminal economic model; we therefore reframe proactive countermeasures like Zero-Trust principles and ephemeral tokens as economic weapons designed to devalue the stolen asset. Finally, the paper provides a prioritized, multi-year research roadmap and a practical decision-tree framework to guide the implementation of these advanced, collaborative cybersecurity strategies to counter this pervasive and evolving threat. Full article

With the growing demand for secure image communication, effective encryption solutions are critical for safeguarding visual data from unauthorized access. The substitution box (S-box) in AES (Advanced Encryption Standard) is critical for ensuring nonlinearity and security. However, the static S-box used in AES is vulnerable to algebraic attacks, side-channel attacks, and so on. This study offers a novel Lorenz key and Chua key-based Reversible Substitution Box (LCK-SB) for image encryption, which takes advantage of the chaotic behavior of the Lorenz and Chua key systems to improve security due to nonlinear jumps and mixed chaotic behavior while maintaining optimal quantum cost, area, and power. The suggested method uses a hybrid Lorenz and Chua key generator to create a highly nonlinear and reversible S-box, which ensures strong confusion and diffusion features. The performance of the LCK-SB approach is examined on field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) platforms, and the findings show that quantum cost, delay, and power are decreased by 97%, 74.6%, and 35%, respectively. Furthermore, the formal security analysis shows that the suggested technique efficiently resists threats. The theoretical analysis and experimental assessment show that the suggested system is more secure for picture encryption, making it suitable for real-time and high-security applications. Full article

To address the limitations of the Red-billed Blue Magpie Optimization algorithm (RBMO), such as its tendency to get trapped in local optima and its slow convergence rate, an enhanced version called MRBMO was proposed. MRBMO was improved by integrating Good Nodes Set Initialization, an Enhanced Search-for-food Strategy, a newly designed Siege-style Attacking-prey Strategy, and Lens-Imaging Opposition-Based Learning (LIOBL). The experimental results showed that MRBMO demonstrated strong competitiveness on the CEC2005 benchmark. Among a series of advanced metaheuristic algorithms, MRBMO exhibited significant advantages in terms of convergence speed and solution accuracy. On benchmark functions with 30, 50, and 100 dimensions, the average Friedman values of MRBMO were 1.6029, 1.6601, and 1.8775, respectively, significantly outperforming other algorithms. The overall effectiveness of MRBMO on benchmark functions with 30, 50, and 100 dimensions was 95.65%, which confirmed the effectiveness of MRBMO in handling problems of different dimensions. This paper designed two types of simulation experiments to test the practicability of MRBMO. First, MRBMO was used along with other heuristic algorithms to solve four engineering design optimization problems, aiming to verify the applicability of MRBMO in engineering design optimization. Then, to overcome the shortcomings of metaheuristic algorithms in antenna S-parameter optimization problems—such as time-consuming verification processes, cumbersome operations, and complex modes—this paper adopted a test suite specifically designed for antenna S-parameter optimization, with the goal of efficiently validating the effectiveness of metaheuristic algorithms in this domain. The results demonstrated that MRBMO had significant advantages in both engineering design optimization and antenna S-parameter optimization. Full article

The convergence of operational technology (OT) and information technology (IT) in industrial environments, such as water treatment plants, has significantly increased the attack surface of Supervisory Control and Data Acquisition (SCADA) systems. Traditional intrusion detection systems (IDS), which focus solely on network traffic, often fail to detect stealthy, process-level attacks. This paper proposes a Digital Twin-driven Intrusion Detection (DT-ID) framework that integrates high-fidelity process simulation, real-time sensor modeling, adversarial attack injection, and hybrid anomaly detection using both physical residuals and machine learning. We evaluate the DT-ID framework using a simulated water treatment plant environment, testing against false data injection (FDI), denial-of-service (DoS), and command injection attacks. The system achieves a detection F1-score of 96.3%, a false positive rate below 2.5%, and an average detection latency under 500 ms, demonstrating substantial improvement over conventional rule-based and physics-only IDS in identifying stealthy anomalies. Our findings highlight the potential of cyber-physical digital twins to enhance SCADA security in critical infrastructure. In the following sections, we present the motivation and approach underlying this framework. Full article

With the continuous advancement of side-channel attacks (SCA), deep learning-based methods have emerged as a prominent research focus due to their powerful feature extraction and nonlinear modeling capabilities. Traditional convolutional neural networks (CNNs) excel at capturing local temporal dependencies but struggle to model long-range sequential information effectively, limiting attack efficiency and generalization. In this paper, we propose a hybrid deep neural network architecture that integrates Residual Mamba blocks with multi-layer perceptrons (MLP) to enhance the modeling of side-channel information from AES implementations. The Residual Mamba module leverages state-space modeling to capture long-range dependencies, improving the model’s global temporal perception, while the MLP module further fuses high-dimensional features. Experiments conducted on the publicly available ASCAD dataset targeting the second byte of AES demonstrate that our model achieves guessing entropy (GE) rank 1 with fewer than 100 attack traces, significantly outperforming traditional CNNs and recent Transformer-based models. The proposed approach exhibits fast convergence and high attack efficiency, offering an effective new paradigm for deep learning in side-channel analysis with important theoretical and practical implications. Full article