Debugging embedded multimedia application traces through periodic pattern mining

Cueva, Patricia Lopez; Bertaux, Aurélie; Termier, Alexandre; Méhaut, Jean François; Santana, Miguel

doi:10.1145/2380356.2380366

Cited by 16 publications

(4 citation statements)

References 17 publications

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“…All of the above approaches are susceptible to producing a huge number of patterns, making the exploitation of their results difficult. The use of a condensed representation for periodic patterns [15] allows to significantly reduce the number of patterns output, without loss of information, but falls short of satisfactorily addressing the problem.…”

Section: Related Workmentioning

confidence: 99%

“…They exhibit some resistance to noise, when it takes the form of slight variations in the inter-occurrence delay [2] or of the recurrence being limited to only a portion of the data [16]. However, such algorithms suffer from the traditional plague of pattern mining algorithms: they output too many patterns (up to several millions), even when relying on condensed representations [15].…”

Section: Introductionmentioning

confidence: 99%

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Mining Periodic Patterns with a MDL Criterion

Galbrun

Cellier

Tatti

et al. 2019

Lecture Notes in Computer Science

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The quantity of event logs available is increasing rapidly, be they produced by industrial processes, computing systems, or life tracking, for instance. It is thus important to design effective ways to uncover the information they contain. Because event logs often record repetitive phenomena, mining periodic patterns is especially relevant when considering such data. Indeed, capturing such regularities is instrumental in providing condensed representations of the event sequences. We present an approach for mining periodic patterns from event logs while relying on a Minimum Description Length (MDL) criterion to evaluate candidate patterns. Our goal is to extract a set of patterns that suitably characterises the periodic structure present in the data. We evaluate the interest of our approach on several real-world event log datasets. ]

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Periodic Patterns with a MDL Criterion

Galbrun

Cellier

Tatti

et al. 2019

Lecture Notes in Computer Science

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“…Trace Analysis Flow : Dierent treatments are often needed to understand traces. Statistics provide general information about application behavior, while pattern recognition [9] or data mining algorithms [10] extract information and synthesize the trace representation. Besides, lters or noise elimination processes help to reduce the amount of information.…”

Section: Storage Of Big Traces : Execution Traces Of Embedded Systemsmentioning

confidence: 99%

Trace Management and Analysis for Embedded Systems

Pagano

Dosimont

Huard³

et al. 2013

2013 IEEE 7th International Symposium on Embedded Multicore Socs

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International audienceThe growing complexity of embedded systems makes their behavior analysis a challenging task. In this context, tracing appears to be a promising solution as it provides relevant information about the system execution. However, trace management and analysis are hindered by the diversity of trace formats, the incompatibility of trace analysis methods, the problem of trace size and its storage as well as by the lack of visualization scalability. In this article we present FrameSoC, a new trace management infrastructure that solves these issues. It proposes generic solutions for trace storage and defines interfaces and plug in mechanisms for integrating diverse analysis tools. We illustrate the benefit of FrameSoC with a case study of a visualization module that provides representation scalability for large traces by using an aggregation algorithm

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“…By combining the analysis of application execution traces with machine learning techniques, we aim to enhance malware detection. Previous studies have emphasized the significance and utility of traces in monitoring computer system behavior [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Software Vulnerabilities’ Detection by Analysing Application Execution Traces

Koala,

Bassol´e,

Tiendrebeogo

et al. 2023

IJACSA

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Over the years, digital traces have proven to be significant for analyzing IT systems, including applications. With the persistent threats arising from the widespread proliferation of malware and the evasive techniques employed by cybercriminals, researchers and application vendors alike are concerned about finding effective solutions. In this article, we assess a hybrid approach to detecting software vulnerabilities based on analyzing traces of application execution. To accomplish this, we initially extract permissions and features from manifest files. Subsequently, we employ a tracer to extract events from each running application, utilizing a set of elements that indicate the behavior of the application. These events are then recorded in a trace. We convert these traces into features that can be utilized by machine learning algorithms. Finally, to identify vulnerable applications, we train these features using six machine learning algorithms (KNN, Random Forest, SVM, Naive Bayes, Decision Tree-CART, and MLP). The selection of these algorithms is based on the outcomes of several preliminary experiments. Our results indicate that the SVM algorithm produces the best performance, followed by Random Forest, achieving an accuracy of 98% for malware detection and 96% for benign applications. These findings demonstrate the relevance and utility of analyzing real application behavior through event analysis.

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Debugging embedded multimedia application traces through periodic pattern mining

Cited by 16 publications

References 17 publications

Mining Periodic Patterns with a MDL Criterion

Mining Periodic Patterns with a MDL Criterion

Trace Management and Analysis for Embedded Systems

Software Vulnerabilities’ Detection by Analysing Application Execution Traces

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