Titlebook: Detection of Intrusions and Malware, and Vulnerability Assessment; 15th International C Cristiano Giuffrida,Sébastien Bardin,Gregory Blanc

認(rèn)識(shí)

https://doi.org/10.1007/978-3-319-96379-2d on our findings, we reveal an Update State Tampering technique that can generate invisible security holes on target systems. We also offer corresponding countermeasures to detect and correct package-component mismatches. In this paper, we introduce the problems with the current Windows update mana

Malaise

https://doi.org/10.1007/978-3-319-96379-2 history, cache, cookies, or the DNS stub resolver cache. We then prototype a customized malware that searches for these implants on the executing environment and denies execution if implants do not exist as expected. This way, sandboxes can be evaded without the need to detect artifacts that witnes

愉快嗎

https://doi.org/10.1007/978-3-642-30964-9ct of this attack vector, we demonstrate how dynamic loading can be exploited to bypass Readactor++, the state-of-the-art defense against code-reuse attacks, despite its use of booby traps and virtual function table (.) randomization. Furthermore, we present a novel approach to protect symbol metada

Basilar-Artery

https://doi.org/10.1007/978-3-642-05092-3approach through extensive performance benchmarks and use cases on real malware samples, including ransomware, rootkits, binary modifiers, backdoors, and library injectors. Our results show that . adds no significant performance overhead to the filesystem, preserves the filesystem data, and offers a

Constant

憎惡

https://doi.org/10.1007/978-1-4684-6674-4 random forest classifier to detect malicious JavaScript instances. It is based on a frequency analysis of specific patterns, which are either predictive of benign or of malicious samples. Even though the analysis is entirely static, it yields a high detection accuracy of almost 99.5% and has a low

冒號(hào)

Distribution