Automated Bug Hunting With Data-Driven Symbolic Root Cause Analysis

Yagemann, Carter; Chung, Simon P.; Saltaformaggio, Brendan; Lee, Wenke

doi:10.1145/3460120.3485363

Cited by 11 publications

(2 citation statements)

References 85 publications

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“…Hence, ML has been used to prevent the automatic execution of actions due to incorrect predictions. Detection system outputs come in the form of alerts, and thousands of such alerts are generated in modern environments every hour [136,137]. To overcome this challenge, ML could be deployed to filter, prioritize, and aggregate the alerts into a more generalized event [138].…”

Section: Management Of Alertsmentioning

confidence: 99%

Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems

et al. 2023

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With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing need for robust cybersecurity mechanisms to protect these systems from cyberattacks. A cyber-physical system is a combination of physical and cyber components, and a security breach in either component can lead to catastrophic consequences. Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. Overall, effective cyberattack detection and mitigation methods in CPSs require a comprehensive security strategy that considers the unique characteristics of a CPS, such as the interconnectedness of physical and cyber components, the need for real-time response, and the potential consequences of a security breach. By implementing these methods, CPSs can be better protected against cyberattacks, thus ensuring the safety and reliability of critical infrastructure and other vital systems. This paper reviews the various kinds of cyber-attacks that have been launched or implemented in CPSs. It reports on the state-of-the-art detection and mitigation methods that have been used or proposed to secure the safe operation of various CPSs. A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. The roles of machine learning in cyber assault are reviewed. In order to direct future study and motivate additional investigation of this increasingly important subject, some challenges that have been unaddressed, such as the prerequisites for CPSs, an in-depth analysis of CPS characteristics and requirements, and the creation of a holistic review of the different kinds of attacks on different CPSs, together with detection and mitigation algorithms, are discussed in this review.

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Section: Management Of Alertsmentioning

confidence: 99%

Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems

et al. 2023

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“…The problem of identifying the root cause of a vulnerability is orthogonal to the research question we investigate here (namely, if we can tell a language model where to apply a repair, can it generate correct patches?). We also note that root cause identification and localization for security vulnerabilities is an active area of research [36]- [41]. Should LLMs prove useful for vulnerability repair, such work could be used as part of a complete, automated, end-to-end vulnerability repair system.…”

Section: B Extractfix Datasetmentioning

confidence: 99%

Examining Zero-Shot Vulnerability Repair with Large Language Models

Pearce¹,

Tan²,

Ahmad³

et al. 2021

Preprint

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Human developers can produce code with cybersecurity weaknesses. Can emerging 'smart' code completion tools help repair those weaknesses? In this work, we examine the use of large language models (LLMs) for code (such as OpenAI's Codex and AI21's Jurassic J-1) for zero-shot vulnerability repair. We investigate challenges in the design of prompts that coax LLMs into generating repaired versions of insecure code. This is difficult due to the numerous ways to phrase key informationboth semantically and syntactically-with natural languages. By performing a large scale study of four commercially available, black-box, "off-the-shelf" LLMs, as well as a locally-trained model, on a mix of synthetic, hand-crafted, and real-world security bug scenarios, our experiments show that LLMs could collectively repair 100% of our synthetically generated and hand-crafted scenarios, as well as 58% of vulnerabilities in a selection of historical bugs in real-world open-source projects.

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