Leveraging Spectral Representations of Control Flow Graphs for Efficient Analysis of Windows Malware

Author:

Sun Qirui1,Abdukhamidov Eldor1,Abuhmed Tamer1,Abuhamad Mohammed2

Affiliation:

1. Sungkyunkwan University, Suwon, Republic of Korea

2. Loyola University Chicago, Chicago, IL, USA

Funder

National Research Foundation of Korea

Publisher

ACM

Reference7 articles.

1. Ahmed Abusnaina , Mohammed Abuhamad , Hisham Alasmary , Afsah Anwar , Rhongho Jang , Saeed Salem , Daehun Nyang , and David Mohaisen . 2021. DL-FHMC: Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification . IEEE Transactions on Dependable and Secure Computing ( 2021 ). Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary, Afsah Anwar, Rhongho Jang, Saeed Salem, Daehun Nyang, and David Mohaisen. 2021. DL-FHMC: Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification. IEEE Transactions on Dependable and Secure Computing (2021).

2. Hojjat Aghakhani , Fabio Gritti , Francesco Mecca , Martina Lindorfer , Stefano Ortolani , Davide Balzarotti , Giovanni Vigna , and Christopher Kruegel . 2020 . When Malware is Packin'Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features . Network and Distributed Systems Security Symposium 2020. Hojjat Aghakhani, Fabio Gritti, Francesco Mecca, Martina Lindorfer, Stefano Ortolani, Davide Balzarotti, Giovanni Vigna, and Christopher Kruegel. 2020. When Malware is Packin'Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features. Network and Distributed Systems Security Symposium 2020.

3. Soteria: Detecting Adversarial Examples in Control Flow Graph-based Malware Classifiers

4. Abdulbasit Darem , Jemal Abawajy , Aaisha Makkar , Asma Alhashmi , and Sultan Alanazi . 2021. Visualization and deep-learning-based malware variant detection using OpCode-level features. Future Generation Computer Systems 125 ( 2021 ). Abdulbasit Darem, Jemal Abawajy, Aaisha Makkar, Asma Alhashmi, and Sultan Alanazi. 2021. Visualization and deep-learning-based malware variant detection using OpCode-level features. Future Generation Computer Systems 125 (2021).

5. Radare2. 2018. Radare2. http://www.radare.org/r/ Radare2. 2018. Radare2. http://www.radare.org/r/

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1. A Survey of Control Flow Graph Recovery for Binary Code;Computer Applications;2023-12-16

2. BiBE: A Self-supervised Contrastive Learning Architecture for Malware Detection;2023 IEEE 11th International Conference on Computer Science and Network Technology (ICCSNT);2023-10-21

3. BejaGNN: behavior-based Java malware detection via graph neural network;The Journal of Supercomputing;2023-04-17

4. Optimized Attention-based Long-short-term memory and Gated Recurrent Unit for Malware Detection in Windows;2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON);2022-12-22

5. BejaGNN: Behavior-based Java Malware Detection via Graph Neural Network;2022-11-10

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