相关论文: PacketCGAN: Exploratory Study of Class Imbalance f…

Recent advances in machine learning (ML) and computer vision tools have enabled applications in a wide variety of arenas such as financial analytics, medical diagnostics, and even within the Department of Defense. However, their widespread…

Conditional Generative Adversarial Networks (cGANs) have been used in many image processing tasks. However, they still have serious problems maintaining the balance between conditioning the output on the input and creating the output with…

Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Despite advances from image processing to deep learning based models, algorithm performance is highly dependent on…

Recent advancements in deep learning have significantly enhanced the performance and efficiency of traffic classification in networking systems. However, the lack of transparency in their predictions and decision-making has made network…

The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of techniques, such as machine learning and deep learning…

We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practical applications in domains including security (e.g.,…

Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that…

Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We present PacketCLIP, a multi-modal framework combining packet data with natural language semantics through contrastive pretraining and…

Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal dependencies in real-world traffic systems. Prior…

Class imbalance is an inherent problem in many machine learning classification tasks. This often leads to trained models that are unusable for any practical purpose. In this study we explore an unsupervised approach to address these…

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem…

The rapid growth of encryption has significantly enhanced privacy and security while posing challenges for network traffic classification. Recent approaches address these challenges by transforming network traffic into text or image formats…

Accurate and efficient network traffic classification is important for many network management tasks, from traffic prioritization to anomaly detection. Although classifiers using pre-computed flow statistics (e.g., packet sizes,…

Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model due to the imbalances in the dataset for discriminating…

This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets. Our primary aim is to enhance the performance and stability of GANs in such datasets. In pursuit of this objective,…

Subject to intricate environmental variables, the precise classification of jamming signals holds paramount significance in the effective implementation of anti-jamming strategies within communication systems. In light of this imperative,…

Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detecting these attacks. Although numerous machine learning-based…

An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for scenario classification but most of them assume that data…

Real-world machine learning systems need to analyze test data that may differ from training data. In K-way classification, this is crisply formulated as open-set recognition, core to which is the ability to discriminate open-set data…

Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and…