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Fuzzer Test Log Analysis Using Machine Learning: Framework to analyze logs and provide feedback to guide the fuzzer
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this modern world machine learning and deep learning have become popular choice for analysis and identifying various patterns on data in large volumes.

The focus of the thesis work has been on the design of the alternative strategies using machine learning to guide the fuzzer in selecting the most promising test cases. Thesis work mainly focuses on the analysis of the data by using machine learning techniques. A detailed analysis study and work is carried out in multiple phases. First phase is targeted to convert the data into suitable format(pre-processing) so that necessary features can be extracted and fed as input to the unsupervised machine learning algorithms. Machine learning algorithms accepts the input data in form of matrices which represents the dimensionality of the extracted features. Several experiments and run time benchmarks have been conducted to choose most efficient algorithm based on execution time and results accuracy. Finally, the best choice has been implanted to get the desired result. The second phase of the work deals with applying supervised learning over clustering results. The final phase describes how an incremental learning model is built to score the test case logs and return their score in near real time which can act as feedback to guide the fuzzer.

Abstract [sv]

I denna moderna värld har maskininlärning och djup inlärning blivit populärt val för analys och identifiering av olika mönster på data i stora volymer.

Uppsatsen har fokuserat på utformningen av de alternativa strategierna med maskininlärning för att styra fuzzer i valet av de mest lovande testfallen. Examensarbete fokuserar huvudsakligen på analys av data med hjälp av maskininlärningsteknik. En detaljerad analysstudie och arbete utförs i flera faser. Första fasen är inriktad på att konvertera data till lämpligt format (förbehandling) så att nödvändiga funktioner kan extraheras och matas som inmatning till de oövervakade maskininlärningsalgoritmerna. Maskininlärningsalgoritmer accepterar ingångsdata i form av matriser som representerar dimensionen av de extraherade funktionerna. Flera experiment och körtider har genomförts för att välja den mest effektiva algoritmen baserat på exekveringstid och resultatnoggrannhet. Slutligen har det bästa valet implanterats för att få önskat resultat. Den andra fasen av arbetet handlar om att tillämpa övervakat lärande över klusterresultat. Slutfasen beskriver hur en inkrementell inlärningsmodell är uppbyggd för att få poäng i testfallsloggarna och returnera poängen i nära realtid vilket kan fungera som feedback för att styra fuzzer.

Place, publisher, year, edition, pages
2018. , p. 50
Series
TRITA-EECS-EX ; 2018:744
Keywords [en]
Similarity Measures; Distance function; Machine learning; Clustering; Classification; Benchmarking
Keywords [sv]
Likhetsåtgärder Avståndsfunktion; Maskininlärning; klustring; Klassificering; benchmarking
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254893OAI: oai:DiVA.org:kth-254893DiVA, id: diva2:1335889
Subject / course
Computer Science
Educational program
Master of Science - Software Engineering of Distributed Systems
Supervisors
Examiners
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-07-08Bibliographically approved

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