Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions

BAS Al-Rimy, MA Maarof, SZM Shaid - Computers & Security, 2018 - Elsevier
Ransomware is a malware category that exploits security mechanisms such as cryptography
in order to hijack user files and related resources and demands money in exchange for the …

Measuring and modeling the label dynamics of online {Anti-Malware} engines

S Zhu, J Shi, L Yang, B Qin, Z Zhang, L Song… - 29th USENIX Security …, 2020 - usenix.org
VirusTotal provides malware labels from a large set of anti-malware engines, and is heavily
used by researchers for malware annotation and system evaluation. Since different engines …

Semantics-based online malware detection: Towards efficient real-time protection against malware

S Das, Y Liu, W Zhang… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Recently, malware has increasingly become a critical threat to embedded systems, while the
conventional software solutions, such as antivirus and patches, have not been so successful …

Bugram: bug detection with n-gram language models

S Wang, D Chollak, D Movshovitz-Attias… - Proceedings of the 31st …, 2016 - dl.acm.org
To improve software reliability, many rule-based techniques have been proposed to infer
programming rules and detect violations of these rules as bugs. These rule-based …

xfuzz: Machine learning guided cross-contract fuzzing

Y Xue, J Ye, W Zhang, J Sun, L Ma… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Smart contract transactions are increasingly interleaved by cross-contract calls. While many
tools have been developed to identify a common set of vulnerabilities, the cross-contract …

[HTML][HTML] A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

S Ndichu, S Kim, S Ozawa, T Misu, K Makishima - Applied Soft Computing, 2019 - Elsevier
Websites attract millions of visitors due to the convenience of services they offer, which
provide for interesting targets for cyber attackers. Most of these websites use JavaScript (JS) …

A systematic literature review and quality analysis of Javascript malware detection

MF Sohan, A Basalamah - IEEE Access, 2020 - ieeexplore.ieee.org
Context: JavaScript (JS) is an often-used programming language by millions of web pages
and is also affected by thousands of malicious attacks. Objective: In this investigation, we …

Wobfuscator: Obfuscating javascript malware via opportunistic translation to webassembly

A Romano, D Lehmann, M Pradel… - 2022 IEEE Symposium …, 2022 - ieeexplore.ieee.org
To protect web users from malicious JavaScript code, various malware detectors have been
proposed, which analyze and classify code as malicious or benign. State-of-the-art detectors …

An empirical study on the effects of obfuscation on static machine learning-based malicious javascript detectors

K Ren, W Qiang, Y Wu, Y Zhou, D Zou… - Proceedings of the 32nd …, 2023 - dl.acm.org
Machine learning is increasingly being applied to malicious JavaScript detection in
response to the growing number of Web attacks and the attendant costly manual …

Active automata learning in practice: an annotated bibliography of the years 2011 to 2016

F Howar, B Steffen - Machine Learning for Dynamic Software Analysis …, 2018 - Springer
Active automata learning is slowly becoming a standard tool in the toolbox of the software
engineer. As systems become ever more complex and development becomes more …