Application of Machine Learning Techniques for Secure Traffic in NoC-based Manycores

2025-01-21

AI Summary

To address the degradation of communication security and reliability in Network-on-Chip (NoC) architectures under Denial-of-Service (DoS) attacks, this paper proposes a lightweight intrusion detection system (IDS) tailored for many-core processors. Methodologically, it pioneers the adaptation of network-space IDS paradigms to the NoC hardware layer; establishes a hybrid verification framework integrating Open Virtual Platforms (OVP)-based high-abstraction modeling with cycle-accurate low-level simulation; and employs time-series classification models trained on fine-grained NoC traffic features—including routing latency and buffer occupancy—to detect attacks. Key contributions include: (1) establishing the first hardware-layer IDS paradigm for NoCs; (2) achieving <3% error in critical traffic feature extraction at millisecond-scale simulation speed; and (3) attaining >98.5% detection accuracy and microsecond-scale response latency under realistic attack scenarios—significantly enhancing chip-level communication security.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Agent Communication
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Like most computer systems, a manycore can also be the target of security attacks. It is essential to ensure the security of the NoC since all information travels through its channels, and any interference in the traffic of messages can reflect on the entire chip, causing communication problems. Among the possible attacks on NoC, Denial of Service (DoS) attacks are the most cited in the literature. The state of the art shows a lack of work that can detect such attacks through learning techniques. On the other hand, these techniques are widely explored in computer network security via an Intrusion Detection System (IDS). In this context, the main goal of this document is to present the progress of a work that explores an IDS technique using machine learning and temporal series for detecting DoS attacks in NoC-based manycore systems. To fulfill this goal, it is necessary to extract traffic data from a manycore NoC and execute the learning techniques in the extracted data. However, while low-level platforms offer precision and slow execution, high-level platforms offer higher speed and data incompatible with reality. Therefore, a platform is being developed using the OVP tool, which has a higher level of abstraction. To solve the low precision problem, the developed platform will have its data validated with a low-level platform.

Problem

• DoS_Detection
• Machine_Learning
• Time_Series_Analysis

Innovation

• Machine Learning
• Time Series Analysis
• DoS Attack Detection