Related papers: FloRa: Flow Table Low-Rate Overflow Reconnaissance…

Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and…

A Network Intrusion Detection System (NIDS) is a tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as solutions to detect…

In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require massive amounts of data for training and induce high resource…

This study focuses on a method for detecting and classifying distributed denial of service (DDoS) attacks, such as SYN Flooding, ACK Flooding, HTTP Flooding, and UDP Flooding, using neural networks. Machine learning, particularly neural…

Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training…

Because of the open nature of the Wireless Sensor Networks (WSN), the Denial of the Service (DoS) becomes one of the most serious threats to the stability of the resourceconstrained sensor nodes. In this paper, we develop AccFlow which is…

Federated learning (FL) enables multiple clients to collaboratively train machine learning models under the coordination of a central server, while maintaining privacy. However, the server cannot directly monitor the local training…

The OpenFlow-based SDN is widely studied to better network performance through planning fine-grained paths. However, being designed to configure path hop-by-hop, it faces the scalability issue that both the flow table overhead and path…

Fast and reliable optimal power flow (OPF) approximation is essential for reliable smart-grid operation, yet many learning-based surrogates either flatten the native heterogeneous structure of power networks, target a limited set of grid…

Software-Defined Networking (SDN) provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks, particularly Carpet-Bombing…

This paper proposes an application-aware multipath packet forwarding framework that integrates Machine Learning Techniques (MLT) and Software Defined Networks (SDN). As the Internet provides a variety of services and their performance…

Software-defined networking offers numerous benefits against the legacy networking systems through simplifying the process of network management and reducing the cost of network configuration. Currently, the management of failures in the…

While collaborative systems provide convenience to our lives, they also face many security threats. One of them is the Low-rate Distributed Denial-of-Service (LDDoS) attack, which is a worthy concern. Unlike volumetric DDoS attacks that…

As more devices connect to the internet, it becomes crucial to address their limitations and basic security needs. While much research focuses on utilizing ML and DL to tackle security challenges, there is often a tendency to overlook the…

Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices. In traditional cloud-centric approaches, raw data is…

Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes while preserving data privacy, making it a cornerstone of intelligent service systems in edge-cloud environments. However, in…

Future generation wireless networks are designed with extremely low delay requirements which makes even small contributed delays important. On the other hand, software defined networking (SDN) has been introduced as a key enabler of future…

Continuous-time (CT) Transformers improve irregular and long-range modeling over CT-RNNs by exploiting inputs or outputs embeddings with continuous dynamics. However, the core scaled-dot-product-attention (SDPA) mechanism remains inherently…

The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages, which reveals the need for network intrusion detection approaches to assist in preventing cyber-attacks and reducing their risks.…

We propose a novel framework that reduces the management and integration overheads for real-time network flows by leveraging the capabilities (especially global visibility and management) of software-defined networking (SDN) architectures.…