RESUMEN
Due to the large computational overhead, underutilization of features, and high bandwidth consumption in traditional SDN environments for DDoS attack detection and mitigation methods, this paper proposes a two-stage detection and mitigation method for DDoS attacks in SDN based on multi-dimensional characteristics. Firstly, an analysis of the traffic statistics from the SDN switch ports is performed, which aids in conducting a coarse-grained detection of DDoS attacks within the network. Subsequently, a Multi-Dimensional Deep Convolutional Classifier (MDDCC) is constructed using wavelet decomposition and convolutional neural networks to extract multi-dimensional characteristics from the traffic data passing through suspicious switches. Based on these extracted multi-dimensional characteristics, a simple classifier can be employed to accurately detect attack samples. Finally, by integrating graph theory with restrictive strategies, the source of attacks in SDN networks can be effectively traced and isolated. The experimental results indicate that the proposed method, which utilizes a minimal amount of statistical information, can quickly and accurately detect attacks within the SDN network. It demonstrates superior accuracy and generalization capabilities compared to traditional detection methods, especially when tested on both simulated and public datasets. Furthermore, by isolating the affected nodes, the method effectively mitigates the impact of the attacks, ensuring the normal transmission of legitimate traffic during network attacks. This approach not only enhances the detection capabilities but also provides a robust mechanism for containing the spread of cyber threats, thereby safeguarding the integrity and performance of the network.
RESUMEN
Cancer is one of the serious threats to public life and health. Early diagnosis, real-time monitoring, and individualized treatment are the keys to improve the survival rate and prolong the survival time of cancer patients. Liquid biopsy is a potential technique for cancer early diagnosis due to its non-invasive and continuous monitoring properties. However, most current liquid biopsy techniques lack the ability to detect cancers at the early stage. Therefore, effective detection of a variety of cancers is expected through the combination of various techniques. Recently, DNA frameworks with tailorable functionality and precise addressability have attracted wide spread attention in biomedical applications, especially in detecting cancer biomarkers such as circulating tumor cells (CTCs), exosomes and circulating tumor nucleic acid (ctNA). Encouragingly, DNA frameworks perform outstanding in detecting these cancer markers, but also face some challenges and opportunities. In this review, we first briefly introduced the development of DNA frameworks and its typical structural characteristics and advantages. Then, we mainly focus on the recent progress of DNA frameworks in detecting commonly used cancer markers in liquid-biopsy. We summarize the advantages and applications of DNA frameworks for detecting CTCs, exosomes and ctNA. Furthermore, we provide an outlook on the possible opportunities and challenges for exploiting the structural advantages of DNA frameworks in the field of cancer diagnosis. Finally, we envision the marriage of DNA frameworks with other emerging materials and technologies to develop the next generation of disease diagnostic biosensors.