1,347 research outputs found
Building an Emulation Environment for Cyber Security Analyses of Complex Networked Systems
Computer networks are undergoing a phenomenal growth, driven by the rapidly
increasing number of nodes constituting the networks. At the same time, the
number of security threats on Internet and intranet networks is constantly
growing, and the testing and experimentation of cyber defense solutions
requires the availability of separate, test environments that best emulate the
complexity of a real system. Such environments support the deployment and
monitoring of complex mission-driven network scenarios, thus enabling the study
of cyber defense strategies under real and controllable traffic and attack
scenarios. In this paper, we propose a methodology that makes use of a
combination of techniques of network and security assessment, and the use of
cloud technologies to build an emulation environment with adjustable degree of
affinity with respect to actual reference networks or planned systems. As a
byproduct, starting from a specific study case, we collected a dataset
consisting of complete network traces comprising benign and malicious traffic,
which is feature-rich and publicly available
Mitigating Webshell Attacks through Machine Learning Techniques.
[EN] A webshell is a command execution environment in the form of web pages. It is often used by attackers as a backdoor tool for web server operations. Accurately detecting webshells is of great significance to web server protection. Most security products detect webshells based on feature-matching methods-matching input scripts against pre-built malicious code collections. The feature-matching method has a low detection rate for obfuscated webshells. However, with the help of machine learning algorithms, webshells can be detected more efficiently and accurately. In this paper, we propose a new PHP webshell detection model, the NB-Opcode (naive Bayes and opcode sequence) model, which is a combination of naive Bayes classifiers and opcode sequences. Through experiments and analysis on a large number of samples, the experimental results show that the proposed method could effectively detect a range of webshells. Compared with the traditional webshell detection methods, this method improves the efficiency and accuracy of webshell detectionGuo, Y.; Marco-Gisbert, H.; Keir, P. (2020). Mitigating Webshell Attacks through Machine Learning Techniques. Future Internet. 12(1):1-16. https://doi.org/10.3390/fi1201001211612
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