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Malware Detection with Quantitative Data Flow Graphs
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We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental construc-tion of aggregated …

We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental construc-tion of aggregated quantitative data flow graphs.
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ResearchGate
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A flow chart of malware detection approaches and …
Cyber attackers employ malicious software (Malware) to compromise data integrity and exploit system resources. Tactics include transforming devices into remotely controlled assets or extorting...
ACM Digital Library
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Malware detection with quantitative data flow graphs
Jun 4, 2014 · We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental …
- Author: Tobias Wüchner, Martín Ochoa, Alexander Pretschner
- Publish Year: 2014
International Journal of Engineering Research & Technology
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Cloud based Malware Detection System - International …
For an example of dynamical file analysis for malware detection, via emulation in a virtual environment. Here we give a few references to exemplify such methods.
ResearchGate
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Flows of work for malware detection using machine learning
To overcome the deficiency of the signature-based approach, we proposed a static malware detection system using data mining techniques to identify known and unknown malware by …
ResearchGate
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Flow diagram for malware detection and classification …
In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained...
People also ask
Do graph metrics affect malware detection precision?
Mao et al. also leverage graph metrics on system entity dependency graphs for malware detection. Similarly, in contrast to us they do not incorporate any quantitative flow information for which we could show that it has an considerable impact on detection precision.
Robust and Effective Malware Detection Through Quantitative Data Flow
link.springer.com
Do graph metrics discriminate malware from goodware?
The recently published work of Jang et al. relates to our work in that they also leverage graph metrics to discriminate malware from goodware. But, in contrast to our work, they base the computation of those metrics on system call dependency graphs, while our model is based on quantitative data flow graphs.
Robust and Effective Malware Detection Through Quantitative Data Flow
link.springer.com
Can a plain data flow diagram represent a security solution?
Plain data flow diagrams do not support the representation of security solutions. While it is possible to extend the meta-model to include a list of model element properties (which is what threat modeling tools commonly do), this solution sufers from the issues discussed in the quality analysis in the previous section.
Solution-aware Data Flow Diagrams for Security Threat Modeling
sion.info
What are the different types of malware detection techniques?
... the process of time, researchers have started to use techniques such as behavior-, heuristic-, and model checking-based detection; and new techniques such as deep learning-, cloud-, mobile devices-, and IoT-based detection. Overview of malware detection approaches, features, and used techniques can be seen in Figure 2. ... [...]
A flow chart of malware detection approaches and features
researchgate.net
What is the average detection rate of malware?
Our evaluations on a large and diverse data set consisting of about 7000 malware and 500 goodware samples show an average detection rate of 98.01 % and a false positive rate of 0.48 %.
Robust and Effective Malware Detection Through Quantitative Data Flow
link.springer.com
Can qdfg detect new malware samples that exhibit unknown behavior?
In contrast to previous QDFG-based work and related rule-based approaches , our approach is able to detect novel malware samples that exhibit unknown behavior with better detection effectiveness and efficiency.
Robust and Effective Malware Detection Through Quantitative Data Flow
link.springer.com
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Springer
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Robust and Effective Malware Detection Through Quantitative …
Jan 1, 2015 · We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by …
Springer
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A Survey of Malware Classification Methods Based on Data …
Many malware classification methods based on data flow graphs have been proposed. Some of them are based on user-defined features or graph similarity of data flow graphs. Graph neural …
Springer
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A Survey of Malware Classification Methods Based on Data Flow …
Aug 10, 2022 · After analyzing the advantages and disadvantages of different data flow graph variants and their corresponding malware classification methods, this paper points out the …
sion.info
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Solution-aware Data Flow Diagrams for Security Threat …
Data Flow Diagrams (DFDs) are the main input for threat modeling techniques such as Microsoft STRIDE or LINDDUN. They represent system-level abstractions that lack any architectural …

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