Enhancing Network Visibility and Security with Advanced Port Scanning Techniques.

Network security is paramount in today's digital landscape, where cyberthreats continue to evolve and pose significant risks. We propose a DPDK-based scanner based on a study on advanced port scanning techniques to improve network visibility and security. The traditional port scanning methods suffer from speed, accuracy, and efficiency limitations, hindering effective threat detection and mitigation. In this paper, we develop and implement advanced techniques such as protocol-specific probes and evasive scan techniques to enhance the visibility and security of networks. We also evaluate network scanning performance and scalability using programmable hardware, including smart NICs and DPDK-based frameworks, along with in-network processing, data parallelization, and hardware acceleration. Additionally, we leverage application-level protocol parsing to accelerate network discovery and mapping, analyzing protocol-specific information. In our experimental evaluation, our proposed DPDK-based scanner demonstrated a significant improvement in target scanning speed, achieving a 2× speedup compared to other scanners in a target scanning environment. Furthermore, our scanner achieved a high accuracy rate of 99.5% in identifying open ports. Notably, our solution also exhibited a lower CPU and memory utilization, with an approximately 40% reduction compared to alternative scanners. These results highlight the effectiveness and efficiency of our proposed scanning techniques in enhancing network visibility and security. The outcomes of this research contribute to the field by providing insights and innovations to improve network security, identify vulnerabilities, and optimize network performance.

Extraction and Calculation of Roadway Area from Satellite Images Using Improved Deep Learning Model and Post-Processing.

Roadway area calculation is a novel problem in remote sensing and urban planning. This paper models this problem as a two-step problem, roadway extraction, and area calculation. Roadway extraction from satellite images is a problem that has been tackled many times before. This paper proposes a method using pixel resolution to calculate the area of the roads covered in satellite images. The proposed approach uses novel U-net and Resnet architectures called U-net++ and ResNeXt. The state-of-the-art model is combined with the proposed efficient post-processing approach to improve the overlap with ground truth labels. The performance of the proposed road extraction algorithm is evaluated on the Massachusetts dataset and it is shown that the proposed approach outperforms the existing solutions which use models from the U-net family.

Improved image classification explainability with high-accuracy heatmaps.

Deep learning models have become increasingly used for image-based classification. In critical applications such as medical imaging, it is important to convey the reasoning behind the models' decisions in human-understandable forms. In this work, we propose Pyramid Localization Network (PYLON), a deep learning model that delivers precise location explanation by increasing the resolution of heatmaps produced by class activation map (CAM). PYLON substantially improves the quality of CAM's heatmaps in both general image and medical image domains and excels at pinpointing the locations of small objects. Most importantly, PYLON does not require expert annotation of the object location but instead can be trained using only image-level label. This capability is especially important for domain where expert annotation is often unavailable or costly to obtain. We also demonstrate an effective transfer learning approach for applying PYLON on small datasets and summarize technical guidelines that would facilitate wider adoption of the technique.

A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.

Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.

The application of artificial neural networks for phenotypic drug resistance prediction: evaluation and comparison with other interpretation systems.

Although phenotypic resistance testing provides more direct measurement of antiretroviral drug resistance than genotypic testing, it is costly and time-consuming. However, genotypic resistance testing has the advantages of being simpler and more accessible, and it might be possible to use the data obtained for predicting quantitative drug susceptibility to interpret complex mutation combinations. This study applied the Artificial Neural Network (ANN) system to predict the HIV-1 resistance phenotype from the genotype. A total of 7,598 pairs of HIV-1 sequences, with their corresponding phenotypic fold change values for 14 antiretroviral drugs, were trained, validated, and tested in ANN modeling. The results were compared with the HIV-SEQ and Geno2pheno interpretation systems. The prediction performance of the ANN models was measured by 10-fold cross-validation. The results indicated that by using the ANN, with an associated set of amino acid positions known to influence drug resistance for individual antiretroviral drugs, drug resistance was accurately predicted and generalized for individual HIV-1 subtypes. Therefore, high correlation with the experimental phenotype may help physicians choose optimal therapeutic regimens that might be an option, or supporting system, of FDA-approved genotypic resistance testing in heavily treatment-experienced patients.

Combining classifiers for HIV-1 drug resistance prediction.

This paper applies and studies the behavior of three learning algorithms, i.e. the Support Vector machine (SVM), the Radial Basis Function Network (the RBF network), and k-Nearest Neighbor (k-NN) for predicting HIV-1 drug resistance from genotype data. In addition, a new algorithm for classifier combination is proposed. The results of comparing the predictive performance of three learning algorithms show that, SVM yields the highest average accuracy, the RBF network gives the highest sensitivity, and k-NN yields the best in specificity. Finally, the comparison of the predictive performance of the composite classifier with three learning algorithms demonstrates that the proposed composite classifier provides the highest average accuracy.