We investigate the problem of finding a spanning tree of a set of n moving points in Rdim that minimizes the maximum total weight (under any convex distance function) or the maximum bottleneck throughout the motion. T...
详细信息
Human activity detection plays a vital role in applications such as healthcare monitoring, smart environments, and security surveillance. However, traditional methods often rely on computationally intensive models, wh...
详细信息
The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercrimi...
详细信息
The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers
One of the primary reasons of illness and mortality across the world is cardiovascular disease (CVD), for the timely detection of which the need for accurate detection methods persists. The last years have seen the de...
详细信息
In recent years, YouTube has become the leading platform for Bangla movies and dramas, where viewers express their opinions in comments that convey their sentiments about the content. However, not all comments are rel...
详细信息
This article defines embeddings between state-based and action-based probabilistic logics which can be used to support probabilistic model checking. First, we slightly modify the model embeddings proposed in the liter...
详细信息
AI Driven Crop Prediction is fundamental for upgrading agrarian arranging and efficiency within the confront of worldwide challenges, populace development, and nourishment security. Conventional strategies of trim sur...
详细信息
With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained ...
详细信息
With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.
Network intrusion detection system (NIDS) has become a vital tool to protect information and detect attacks in computer networks. The performance of NIDSs can be evaluated by the number of detected attacks and false a...
详细信息
The research community has traditionally concentrated on emotion detection in emotion modeling, while emotion generation has garnered less focus. With the rise of artificial intelligence, numerous chatbots have been d...
详细信息
暂无评论