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• W2173013550 abstract "With our increasing reliance on the correct functioning of computer systems, identifyingand eliminating vulnerabilities in program code is gaining in importance. To date, thevast majority of these flaws are found by tedious manual auditing of code conductedby experienced security analysts. Unfortunately, a single missed flaw can suffice for anattacker to fully compromise a system, and thus, the sheer amount of code plays into theattacker’s cards. On the defender’s side, this creates a persistent demand for methodsthat assist in the discovery of vulnerabilities at scale.This thesis introduces pattern-based vulnerability discovery, a novel approach for identifying vulnerabilities which combines techniques from static analysis, machine learning,and graph mining to augment the analyst’s abilities rather than trying to replace her.The main idea of this approach is to leverage patterns in the code to narrow in on potential vulnerabilities, where these patterns may be formulated manually, derived fromthe security history, or inferred from the code directly. We base our approach on a novelarchitecture for robust analysis of source code that enables large amounts of code to bemined for vulnerabilities via traversals in a code property graph, a joint representationof a program’s syntax, control flow, and data flow. While useful to identify occurrencesof manually defined patterns in its own right, we proceed to show that the platformoffers a rich data source for automatically discovering and exposing patterns in code. Tothis end, we develop different vectorial representations of source code based on symbols,trees, and graphs, allowing it to be processed with machine learning algorithms. Ultimately, this enables us to devise three unique pattern-based techniques for vulnerabilitydiscovery, each of which address a different task encountered in day-to-day auditing byexploiting a different of the three main capabilities of unsupervised learning methods.In particular, we present a method to identify vulnerabilities similar to a known vulnerability, a method to uncover missing checks linked to security critical objects, andfinally, a method that closes the loop by automatically generating traversals for our codeanalysis platform to explicitly express and store vulnerable programming patterns. We empirically evaluate our methods on the source code of popular and widely-used opensource projects, both in controlled settings and in real world code audits. In controlledsettings, we find that all methods considerably reduce the amount of code that needsto be inspected. In real world audits, our methods allow us to expose many previouslyunknown and often critical vulnerabilities, including vulnerabilities in the VLC mediaplayer, the instant messenger Pidgin, and the Linux kernel." @default.
• W2173013550 created "2016-06-24" @default.
• W2173013550 creator A5089184320 @default.
• W2173013550 date "2015-11-10" @default.
• W2173013550 modified "2023-09-26" @default.
• W2173013550 title "Pattern-Based Vulnerability Discovery" @default.
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