ML-FEED: Machine Learning Framework for Efficient Exploit Detection

Saha, Tanujay; Rahat, Tamjid Al; Aaraj, Najwa; Tian, Yuan; Jha, Niraj K.

doi:10.1109/tps-isa56441.2022.00027

Cited by 2 publications

(1 citation statement)

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“…Saha et al [18] developed a method to automatically capture patterns including sequences of instructions from CWEs published in databases like NIST SARD, each CWE being enriched with the information collected from CVE repositories. They propose the ML-FEED (Machine Learning Framework for Efficient Exploit Detection), which extracts individual instructions from the newly developed exploit database and label them with the CWEs that these instructions may trigger.…”

Section: Related Workmentioning

confidence: 99%

Detection of Software Security Weaknesses Using Cross-Language Source Code Representation (CLaSCoRe)

2023

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The research presented in the paper aims at increasing the capacity to identify security weaknesses in programming languages that are less supported by specialized security analysis tools, based on the knowledge gathered from securing the popular ones, for which security experts, scanners, and labeled datasets are, in general, available. This goal is vital in reducing the overall exposure of software applications. We propose a solution to expand the capabilities of security gaps detection to downstream languages, influenced by their more popular “ancestors” from the programming languages’ evolutionary tree, using language keyword tokenization and clustering based on word embedding techniques. We show that after training a machine learning algorithm on C, C++, and Java applications developed by a community of programmers with similar behavior of writing code, we can detect, with acceptable accuracy, similar vulnerabilities in C# source code written by the same community. To achieve this, we propose a core cross-language representation of source code, optimized for security weaknesses classifiers, named CLaSCoRe. Using this method, we can achieve zero-shot vulnerability detection—in our case, without using any training data with C# source code.

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Section: Related Workmentioning

confidence: 99%