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As modern networks grow increasingly complex--driven by diverse devices, encrypted protocols, and evolving threats--network traffic analysis has become critically important. Existing machine learning models often rely only on a single…
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance,…
Network traffic data is huge, varying and imbalanced because various classes are not equally distributed. Machine learning (ML) algorithms for traffic analysis uses the samples from this data to recommend the actions to be taken by the…
This paper presents a comprehensive evaluation of lightweight deep learning models for image classification, emphasizing their suitability for deployment in resource-constrained environments such as low-memory devices. Five state-of-the-art…
Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and control of transportation systems. The outstanding challenge…
Classifying network traffic according to their application-layer protocols is an important task in modern networks for traffic management and network security. Existing payload-based or statistical methods of application identification…
Traffic classification, a technique for assigning network flows to predefined categories, has been widely deployed in enterprise and carrier networks. With the massive adoption of mobile devices, encryption is increasingly used in mobile…
In recent years, the growth of Internet of Things (IoT) as an emerging technology has been unbelievable. The number of networkenabled devices in IoT domains is increasing dramatically, leading to the massive production of electronic data.…
Internet of Things (IoT) is one of the technological advancements of the twenty-first century which can improve living standards. However, it also imposes new types of security challenges, including device authentication, traffic types…
Traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). Traffic congestion is one of the most serious problems in a city, which can be predicted in…
Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this…
The need for improved network situational awareness has been highlighted by the growing complexity and severity of cyber-attacks. Mobile phones pose a significant risk to network situational awareness due to their dynamic behaviour and lack…
Accurate prediction of traffic flow parameters and real time identification of congestion states are essential for the efficient operation of intelligent transportation systems. This paper proposes a Periodic Pattern Transformer Network…
Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is…
Network slicing enabled by fifth generation (5G) systems has the potential to satisfy diversified service requirements from different vertical industries. As a typical vertical industry, smart distribution grid poses new challenges to…
We consider traffic-update mobile applications that let users learn traffic conditions based on reports from other users. These applications are becoming increasingly popular (e.g., Waze reported 30 million users in 2013) since they…
Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors…
In data storage and transmission, file compression is a common technique for reducing the volume of data, reducing data storage space and transmission time and bandwidth. However, there are significant differences in the compression…
The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to…
Distributed denial-of-service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising…