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.

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.

Cyber attackers employ malicious software (Malware) to compromise data integrity and exploit system resources. Tactics include transforming devices into remotely controlled assets or extorting...

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 comparing...

In this paper, we study the frequency of opcode occurrence to detect unknown malware by using machine learning technique. For the purpose, we have used kaggle Microsoft malware...

Through continuous refinement and training on real-world data, ML-driven malware detection systems offer a proactive defense mechanism, capable of identifying and neutralizing emerging threats before they wreak havoc on unsuspecting users and organizations.

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 knowledge on existing security solutions.

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 interpreting issued system calls as aggregations of quantifiable data flows.

To achieve that, we will be classifying applications using Control Flow Graphs and different similarity-based methods including k-nearest neighbors (kNN) as well as Random Forest classifier to see if different methods can detect certain types of malware or any specific features.

Aug 10, 2022 · 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 networks have also recently been …