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Relational Framework of Cyberattacks: Empirical Evidence from Multistage Incidents. [PDF]

Sensors (Basel)
Ferrer-Oliva M, Medina-Merodio JA, Martínez-Herraiz JJ, Cilleruelo-Rodríguez C.   +3 more
europepmc   +1 more source

A dataset of windows malware execution traces. [PDF]

Data Brief
Raducu R, Villagrasa-Labrador A, Rodríguez RJ, Álvarez P.   +3 more
europepmc   +1 more source

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Malware Analysis and Classification

2023
Malicious applications can be a security threat to Cyber-physical systems as these systems are composed of heterogeneous distributed systems and mostly depends on the internet, ICT services and products. The usage of ICT products and services gives the opportunity of less expensive data collection, intelligent control and decision systems using ...
Jairaj Singh, Kishore Kumar Kumar Senapati   +1 more
openaire   +1 more source

Improving malware classification

Proceedings of the 5th ACM workshop on Security and artificial intelligence, 2012
Malware classification systems have typically used some machine learning algorithm in conjunction with either static or dynamic features collected from the binary. Recently, more advanced malware has introduced mechanisms to avoid detection in these views by using obfuscation techniques to avoid static detection and execution-stalling techniques to ...
Blake Anderson, Curtis Storlie, Terran Lane   +2 more
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EntropyVis: Malware classification

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017
Malware writers often develop malware with automated measures, so the number of malware has increased dramatically. Automated measures tend to repeatedly use significant modules, which form the basis for identifying malware variants and discriminating malware families.
Zhuojun Ren, Guang Chen
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Android malware classification method

Proceedings of the 2013 Research in Adaptive and Convergent Systems, 2013
The number of Android malware is increasing with the growth of Android, so there needs to have a method to classify malware families. There are many classification methods proposed so far, but most of them are based on permission information such as the number of requested permissions and critical permissions.
Byeongho Kang, BooJoong Kang, Jungtae Kim, Eul Gyu Im   +3 more
openaire   +1 more source

Clustering for malware classification

Journal of Computer Virology and Hacking Techniques, 2016
In this research, we apply clustering techniques to the malware classification problem. We compute clusters using the well-known K-means and Expectation Maximization algorithms, with the underlying scores based on Hidden Markov Models. We compare the results obtained from these two clustering approaches and we carefully consider the interplay between ...
Corrado Aaron Visaggio, Swathi Pai, Fabio Di Troia, Thomas H. Austin, Mark Stamp   +4 more
openaire   +2 more sources

Transfer learning for malware multi-classification

Proceedings of the 23rd International Database Applications & Engineering Symposium on - IDEAS '19, 2019
In this paper, we build on top of the MalConv neural networks learning architecture which was initially designed for malware/benign classification. We evaluate the transfer learning of MalConv for malware multi-class classification by extending its contribution in several directions: (1) We assess MalConv performance on a multi-classification problem ...
Kadri, Mohamad Al, Nassar, Mohamed, Safa, Haidar   +2 more
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Metamorphic Malware Classification

2014
Metamorphic malware tend to change its code structure, every time it infects a new host machine. This makes classification and subsequent detection of the malware very difficult. Unlike other viruses, metamorphic malware uses code obfuscation techniques on the body of the malware and that way the malware structure does not exhibit a common signature ...
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