RTL-ConTest: Concolic Testing on RTL for Detecting Security Vulnerabilities

Meng, Xingyu; Kundu, Shamik; Kanuparthi, Arun K.; Basu, Kanad

doi:10.1109/tcad.2021.3066560

Cited by 14 publications

(2 citation statements)

References 29 publications

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“…Optimization calculations adjust the weight attributed to distinct vulnerability characteristics, giving rise to an attack diagram. Over time, attackers will exploit the network via diverse vulnerabilities, and the visible weight disparities between these vulnerabilities will become apparent [12]. The composition of an attack graph involves incorporating a vulnerability group technique.…”

Section: Extraction Of Network Vulnerability Feature Attributesmentioning

confidence: 99%

Vulnerability Detection in Computer Networks using Virtual Reality Technology

Liu

2023

SCPE

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This paper challenges the time-related challenges inherent in conventional network security detection methodologies. It is achieved by incorporating virtual reality technology into the domain of computer network security detection. The research methodology employs optimization calculations to extract attributes that characterize network security vulnerabilities. Concurrently, the weighting of diverse vulnerability attributes is adjusted using a web crawler, a comprehensive list of injection points, and meticulous analyses of the attacks’ genetic characteristics. This collective approach facilitates the exploration of automated network security vulnerability detection within a virtual reality framework. The study’s empirical results demonstrate that the detection method proposed within this investigation exhibits a notably reduced delay of 75.33 milliseconds. The respective delays observed in the two conventional methods stand at 290.11 milliseconds and 337.30 milliseconds. The substantial decrease in detection delay validates the effectiveness and efficiency of the devised automated network vulnerability detection approach grounded in virtual reality technology.

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Section: Extraction Of Network Vulnerability Feature Attributesmentioning

confidence: 99%

Vulnerability Detection in Computer Networks using Virtual Reality Technology

Liu

2023

SCPE

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“…Prior work has investigated different possible paths trying to automate some of these processes. More specifically, we have seen interest in the areas of hardware static analysis [84,85], fuzzing [126,169,207,234,283], and dynamic IFT (Information Flow Tracking) [15,266,280,283]. However, recently published frameworks have mostly confirmed the existence of manually identified vulnerabilities and are usually not scalable to complex CPUs.…”

Section: Attacksmentioning

confidence: 99%

Exploiting Hardware from Software

Frigo

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In recent years we observed a deepening integration between hardware and software; we now have dedicated hardware for all sorts of applications and software heavily optimized for the underlying hardware. And while this allowed modern systems to keep up with the increasing demand for performance, this new paradigm came at the cost of a more complex hardware-software stack. Unfortunately, between the cracks of these two domains, we notice a new class of attacks gaining momentum: software-based hardware attacks. As the name suggests, these attacks target underlying hardware vulnerabilities while being leveraged from software—notorious examples being the Rowhammer bug, Spectre, and Meltdown. In this thesis, we perform an in-depth attack surface analysis of different software-based hardware vulnerabilities while revisiting some of the assumptions upon which the current attacks and defenses are built. More specifically, we deepen the understanding of the DRAM Rowhammer bug from various perspectives: we show how it represents a serious threat to various targets such as mobile devices, web browsers, and Deep Neural Networks; and, we demonstrate that the silver-bullet defense introduced on DDR4 devices against the issue—in-DRAM Target Row Refresh—does not prevent an attacker from triggering bit-flips on millions of devices previously deemed safe. On top of that, we investigate the effectiveness of hardware defenses introduced against the recent Spectre bug and show how those deployed to prevent cross-privilege Spectre attacks are incomplete, allowing attackers to build new exploits.

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