The Emergence of Hardware Fuzzing: A Critical Review of its Significance
- URL: http://arxiv.org/abs/2403.12812v1
- Date: Tue, 19 Mar 2024 15:12:11 GMT
- Title: The Emergence of Hardware Fuzzing: A Critical Review of its Significance
- Authors: Raghul Saravanan, Sai Manoj Pudukotai Dinakarrao,
- Abstract summary: Hardware fuzzing, inspired by software testing methodologies, has gained prominence for its efficacy in identifying bugs within complex hardware designs.
Despite the introduction of various hardware fuzzing techniques, obstacles such as inefficient conversion of hardware modules into software models impede their effectiveness.
This work examines the reliability of existing hardware fuzzing techniques in identifying vulnerabilities and identifies research gaps for future advancements in design verification techniques.
- Score: 0.4943822978887544
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, there has been a notable surge in attention towards hardware security, driven by the increasing complexity and integration of processors, SoCs, and third-party IPs aimed at delivering advanced solutions. However, this complexity also introduces vulnerabilities and bugs into hardware systems, necessitating early detection during the IC design cycle to uphold system integrity and mitigate re-engineering costs. While the Design Verification (DV) community employs dynamic and formal verification strategies, they encounter challenges such as scalability for intricate designs and significant human intervention, leading to prolonged verification durations. As an alternative approach, hardware fuzzing, inspired by software testing methodologies, has gained prominence for its efficacy in identifying bugs within complex hardware designs. Despite the introduction of various hardware fuzzing techniques, obstacles such as inefficient conversion of hardware modules into software models impede their effectiveness. This Systematization of Knowledge (SoK) initiative delves into the fundamental principles of existing hardware fuzzing, methodologies, and their applicability across diverse hardware designs. Additionally, it evaluates factors such as the utilization of golden reference models (GRMs), coverage metrics, and toolchains to gauge their potential for broader adoption, akin to traditional formal verification methods. Furthermore, this work examines the reliability of existing hardware fuzzing techniques in identifying vulnerabilities and identifies research gaps for future advancements in design verification techniques.
Related papers
- Hardware-based stack buffer overflow attack detection on RISC-V architectures [42.170149806080204]
This work evaluates how well hardware-based approaches detect stack buffer overflow (SBO) attacks in RISC-V systems.
We conducted simulations on the PULP platform and examined micro-architecture events using semi-supervised anomaly detection techniques.
arXiv Detail & Related papers (2024-06-12T08:10:01Z) - Real-time Threat Detection Strategies for Resource-constrained Devices [1.4815508281465273]
We present an end-to-end process designed to effectively address DNS-tunneling attacks in a router.
We demonstrate that utilizing stateless features for training the ML model, along with features chosen to be independent of the network configuration, leads to highly accurate results.
The deployment of this carefully crafted model, optimized for embedded devices across diverse environments, resulted in high DNS-tunneling attack detection with minimal latency.
arXiv Detail & Related papers (2024-03-22T10:02:54Z) - Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for
Root Cause Analysis with GPT-assisted Mitigation Suggestion [3.847218857469107]
We presentHW-V2W-Map Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and Internet of Things (IoT) security.
The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the Ontology.
Our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions.
arXiv Detail & Related papers (2023-12-21T02:14:41Z) - On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks [1.6951945839990796]
We propose an evaluation methodology of hardware security properties of HLS-produced designs using state-of-the-art Graph Neural Network (GNN) approaches.
We show that GNNs can be efficiently trained to predict important hardware security met-rics concerning fault attacks.
The proposed method predicts the fault vulnerability metrics of the HLS-based designs with high R-squared scores and achieves huge speedup.
arXiv Detail & Related papers (2023-12-11T10:13:53Z) - Leveraging Traceability to Integrate Safety Analysis Artifacts into the
Software Development Process [51.42800587382228]
Safety assurance cases (SACs) can be challenging to maintain during system evolution.
We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models.
We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
arXiv Detail & Related papers (2023-07-14T16:03:27Z) - A survey on hardware-based malware detection approaches [45.24207460381396]
Hardware-based malware detection approaches leverage hardware performance counters and machine learning prowess.
We meticulously analyze the approach, unraveling the most common methods, algorithms, tools, and datasets that shape its contours.
The discussion extends to crafting mixed hardware and software approaches for collaborative efficacy, essential enhancements in hardware monitoring units, and a better understanding of the correlation between hardware events and malware applications.
arXiv Detail & Related papers (2023-03-22T13:00:41Z) - Robust Machine Learning Systems: Challenges, Current Trends,
Perspectives, and the Road Ahead [24.60052335548398]
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT)
They are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy.
This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities.
arXiv Detail & Related papers (2021-01-04T20:06:56Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.