DL2Fence: Deep Learning Framework for DoS Detection in NoCs

Core Concepts

DL2Fence introduces a deep learning framework for effective detection and localization of Denial-of-Service attacks in Network-on-Chips.

Abstract

This study presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for Denial-of-Service (DoS) detection and localization. The framework achieves high accuracies in detection and localization, with reduced hardware overhead compared to existing methods. It addresses the urgent need for effective DoS detection in large-scale NoCs. Abstract: Introduces refined Flooding Injection Rate-adjustable DoS model. Presents DL2Fence framework using DL and Frame Fusion. Achieves high accuracies in detection and localization. Introduction: NoC is prevalent in MPSoC designs. Vulnerable to DoS attacks affecting system performance. Flooding attacks are common in NoCs. Background: Various studies model DoS flooding attacks in NoCs. Traditional approaches monitor packet-related features. Machine learning models increasingly used for detection and localization. Proposed Framework: DL2Fence utilizes CNN models for classification and segmentation. Multi-Frame Fusion technique used for victim localization. Table-Like Method employed for attacker localization. Features and Model Selection: VCO and BOC features selected for detection and localization tasks. CNN models chosen due to effectiveness over BNN models. Experiments and Results: Detection performance using VCO feature shows high accuracy but lower precision on PARSEC benchmarks. BOC feature excels in both detection and localization with high accuracy and precision. Hardware Overhead: Hardware overhead decreases with larger NoCs, showing adaptability of the framework. Conclusion: DL2Fence offers an efficient deep learning-based solution for DoS detection in NoCs, achieving high accuracies with reduced hardware overhead compared to existing methods.

Stats

DL2Fence achieves a detection accuracy of 95.8% on a 16x16 mesh level. It reduces hardware overhead by 76.3% when transitioning from 8x8 to 16x16 NoCs.

Quotes

"DL2Fence achieves outstanding detection performance with minimal hardware overhead." "The proposed framework balances accuracy with efficiency, making it suitable for large-scale NoCs."

Key Insights Distilled From

DL2Fence

by Haoyu Wang,B... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13563.pdf

Deeper Inquiries

How can the DL2Fence framework be adapted to address evolving DoS attack strategies?

The DL2Fence framework can adapt to evolving DoS attack strategies by incorporating continuous learning mechanisms. This involves updating the deep learning models with new data on emerging attack patterns and behaviors. By regularly retraining the models using real-time or near-real-time data, DL2Fence can stay abreast of the latest DoS tactics and adjust its detection and localization algorithms accordingly. Additionally, implementing anomaly detection techniques within the framework can help identify previously unseen attack signatures, enabling proactive responses to novel threats.

What are the potential limitations or vulnerabilities of relying solely on deep learning models for security applications?

Relying solely on deep learning models for security applications may pose several limitations and vulnerabilities. One key concern is adversarial attacks, where malicious actors intentionally manipulate input data to deceive the model into making incorrect predictions. Deep learning models are susceptible to such attacks if not adequately robustified against them through techniques like adversarial training or input sanitization. Another limitation is interpretability and explainability. Deep learning models often operate as black boxes, making it challenging to understand how they arrive at their decisions. In security applications where transparency is crucial for trust and accountability, this lack of interpretability could be a significant drawback. Moreover, deep learning models require large amounts of labeled training data to perform effectively. In dynamic security environments where new threats emerge frequently, acquiring sufficient labeled data for training may become a bottleneck in maintaining model accuracy over time. Lastly, deep learning models are computationally intensive and resource-demanding, which could limit their deployment in resource-constrained environments or real-time systems requiring low latency responses.

How might advancements in machine learning impact the future development of security solutions beyond network-on-chip architectures?

Advancements in machine learning are poised to revolutionize security solutions beyond network-on-chip architectures by enhancing threat detection capabilities across diverse domains. Behavioral Analysis: Machine Learning algorithms can analyze user behavior patterns to detect anomalies indicative of unauthorized access or malicious activities in cybersecurity systems. Predictive Analytics: Advanced ML techniques enable predictive analytics that forecast potential cyber threats based on historical data trends and current indicators. Automated Response Systems: ML-driven automation allows for swift response actions against detected threats without human intervention. 4..IoT Security: As IoT devices proliferate, ML plays a vital role in securing these interconnected systems by identifying abnormal device behavior that could signal a breach. 5..Cloud Security: Machine Learning enhances cloud security through anomaly detection algorithms that monitor cloud infrastructure for suspicious activities or deviations from normal usage patterns. These advancements will lead to more adaptive and resilient security solutions capable of proactively mitigating risks across various technological landscapes beyond traditional network-on-chip architectures