The newly proposed multimodal transformer architecture offers a new paradigm for UAV detection and aerial object recognition. It introduces an innovative way of feeding multiple data streams, such as audio, infrared video, RGB video, and radar, into the architecture for processing, using independent modalities. The unique features of each modality are attached and processed together in the architecture, where the features are then exposed to the multimodal transformer for classification. Thus, all complementary information can be pooled within the integration framework to allow the model discrimination of any drone target under outdoor conditions from other aerial objects such as birds, helicopters, and airplanes. These methodologies are expected to outperform traditional single-modality systems by improving detection accuracy through class balancing and addressing modality-specific limitations. The proposed model has been further tested through various experiments to evaluate its robustness under conditions such as missing entries, corrupted data, and synthetic inputs. The results suggest that it has strong potential to serve as a benchmark in UAV detection. Thus, this work takes part of an emerging body of sensor fusion and deep learning-related research, demonstrating the potential of multimodal data in real-world detection problems.

All malware are harmful to computer systems ; however, crypto-ransomware specifically leads to irreparable data loss and causes substantial economic prejudice. Ransomware attacks increased significantly during the COVID-19 pandemic, and because of its high profitability, this growth will likely persist. To respond to these attacks, we apply static analysis to detect ransomware by converting Portable Executable (PE) header files into color images in a sequential vector pattern and classifying these via Xception Convolutional Neural Network (CNN) model without transfer learning , which we call Xception ColSeq. This approach simplifies feature extraction, reduces processing load, and is more resilient against evasion techniques and ransomware evolution. The proposed method was evaluated using two datasets. The first contains 1000 ransomware and 1000 benign applications , on which the model achieved an accuracy of 93.73%, precision of 92.95%, recall of 94.64%, and F-measure of 93.75%. The second dataset, which we created and have made available, contains 1023 ransomware, grouped in 25 still active and relevant families, and 1134 benign applications , on which the proposed method achieved an accuracy of 98.20%, precision of 97.50%, recall of 98.76%, and F-measure of 98.12%. Furthermore, we refined a testing methodology for a particular case of zero-day ransomware attacks detection—the detection of new ransomware families—by adding an adequate amount of randomly selected benign applications to the test set, providing representative evaluation performance metrics. These results represent an improvement over the performance of the current methods reported in the literature. Our advantageous approach can be applied as a technique for ransomware detection to protect computer systems from cyber threats.

This paper proposes an automatic method for the identification of twisted pairs sharing the same binder, based on the analysis of phantom circuit measurements. This type of circuit is often used for improving data transmission rates in communication systems, but in this work, phantoming is used to reveal if a 4-wire loop composed by two twisted pairs are close enough and well balanced in order to be considered in the same binder. The identification is done via application of two pattern recognition techniques, K-means and Gaussian Mixture Model, on S11parameter obtained from the phantom-mode measurement of two twisted pairs. This paper also presents an automatic method to labeling the clusters and a method to estimate the length of the two twisted pairs that share the same binder, using Time-Domain Reflectometry analysis. Laboratory results confirm the accuracy of the proposed methods.

Dual-ended line testing (DELT) is a common capability in most current modems and can be used for digital subscriber line (DSL) qualification and monitoring purposes. In spite of that, this feature remains largely unexplored in the literature. This paper proposes a new method based on wavelets and DELT measurements for estimating the total length of the line under test and identification of bridged-taps, two important line parameters that affect the maximum bit rate reached by a DSL line. The proposed method was tested with measurements employing real twisted-pair cables and obtained reasonably accurate results for the analyzed cases.