Related papers: SPIDER: Fuzzing for Stateful Performance Issues in…

Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at…

Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and…

Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO).…

Autonomous vehicles bring the promise of enhancing the consumer experience in terms of comfort and convenience and, in particular, the safety of the autonomous vehicle. Safety functions in autonomous vehicles such as Automatic Emergency…

Spiking Neural Networks (SNN) are more closely related to brain-like computation and inspire hardware implementation. This is enabled by small networks that give high performance on standard classification problems. In literature, typical…

Delay-coupled systems often require low-latency decisions from sparse telemetry, where dense fixed-step neural inference is wasteful and can degrade near stability margins. We introduce Network-Optimised Spiking (NOS), a trainable two-state…

Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling…

Open optical networks have been considered to be important for cost-effectively building and operating the networks. Recently, the optical-circuit-switches (OCSes) have attracted industry and academia because of their cost efficiency and…

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. The communication patterns in such systems are inherently sparse, asynchronous, and localized due to the spiking nature…

Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs,…

Data breaches and cyberattacks represent a severe problem in higher education institutions and universities that can result in illegal access to sensitive information and data loss. To enhance the security of data transmission, Intrusion…

To run a cloud application with the required service quality, operators have to continuously monitor the cloud application's run-time status, detect potential performance anomalies, and diagnose the root causes of anomalies. However,…

Software dependency network metrics extracted from the dependency graph of the software modules by the application of Social Network Analysis (SNA metrics) have been shown to improve the performance of the Software Defect prediction (SDP)…

Resource utilization is one of the emerging problems in many-chip SSDs. In this paper, we propose Sprinkler, a novel device-level SSD controller, which targets maximizing resource utilization and achieving high performance without…

Spiking neural networks (SNNs) have emerged as a class of bio -inspired networks that leverage sparse, event-driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely…

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy…

Time-Sensitive Networking (TSN) is an emerging real-time Ethernet technology that provides deterministic communication for time-critical traffic. At its core, TSN relies on Time-Aware Shaper (TAS) for pre-allocating frames in specific time…

Software-Defined Network (SDN) radically changes the network architecture by decoupling the network logic from the underlying forwarding devices. This architectural change rejuvenates the network-layer granting centralized management and…

This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet…

Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware, as well as models for understanding neuroscience. In this paper, we introduce Spiking Phasor…