Recent progress in integrated circuit technologies requires precise evaluation between dynamic characteristics and topological architecture design. In this paper, we have investigated the performance evaluation of network-on-chip (NoC) architectures constructed with diverse scale-free network topologies by dynamic packet traffic simulation and theoretical network analysis. Topological differences of scale-free networks are evaluated by the degree-degree correlations that indicate topological tendency between the degree of a node and that of the nearest neighbors. Our simulation results quantitatively show that the NoC architecture constructed with the topology where hubs mostly connect to lower-degree nodes is found to achieve short latency and low packet loss ratio since it can disperse traffic load and avoid the extreme concentration of load on hubs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.74.026115 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
With the increase sizes of training datasets and models, the bottleneck in distributed machine learning (DML) training has shifted from computation to communication. To address this bottleneck, we propose an all-optical switching network architecture for accelerating the communication phase of DML training. Experimental results validate packets with error-free and 385 ns server-to-server low-latency communication at traffic load of 0.
View Article and Find Full Text PDFA Modular AI-Driven Intrusion Detection System for Network Traffic Monitoring in Industry 4.0, Using Nvidia Morpheus and Generative Adversarial Networks.
Sensors (Basel)
December 2024
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Sciences, National University of Science and Technology Polithenica Bucharest, 313 Spl. Independenței, RO060042 Bucharest, Romania.
Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.
View Article and Find Full Text PDFPayload State Prediction Based on Real-Time IoT Network Traffic Using Hierarchical Clustering with Iterative Optimization.
Sensors (Basel)
December 2024
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate.
View Article and Find Full Text PDFA Petri Net and LSTM Hybrid Approach for Intrusion Detection Systems in Enterprise Networks.
Sensors (Basel)
December 2024
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification.
View Article and Find Full Text PDFEnhanced Intrusion Detection for ICS Using MS1DCNN and Transformer to Tackle Data Imbalance.
Sensors (Basel)
December 2024
School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300132, China.
With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network security while addressing challenges related to data imbalance and long-tail distributions. A dataset containing five types of attacks targeting programmable logic controllers (PLCs) in industrial control systems (ICS) was first constructed.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!