By Juan Caballero, Urko Zurutuza, Ricardo J. Rodríguez

This ebook constitutes the refereed lawsuits of the thirteenth foreign convention on Detection of Intrusions and Malware, and Vulnerability overview, DIMVA 2016, held in San Sebastián, Spain, in July 2016.

The 19 revised complete papers and a couple of prolonged abstracts provided have been conscientiously reviewed and chosen from sixty six submissions. They current the state-of-the-art in intrusion detection, malware research, and vulnerability evaluate, facing novel principles, thoughts, and purposes in vital components of laptop safeguard together with vulnerability detection, assault prevention, internet safety, malware detection and class, authentication, info leakage prevention, and countering evasive strategies equivalent to obfuscation.

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Here, we adapted the concolic execution framework Triton [9], which itself uses the Z3 SMT solver [3]. A comprehensive evaluation of our approach on a broad range of targets is subject to future work. Finally, DeepFuzz may help to circumvent current bottlenecks related to automatic exploit generation as described by Avgerinos et al. in [1]. We expect that our proposed algorithm can be deployed for automatic exploitation of vulnerabilities deeply hidden in binaries. References 1. : Automatic exploit generation.

555–565. ACM, New York (2009) 6. : When virtual is better than real. In: Proceedings of the Eighth Workshop on Hot Topics in Operating Systems, HOTOS (2001) 7. : Principles of remote attestation. Int. J. Inf. Secur. 10(2), 63–81 (2011) 8. , and Chan, E. Tracking rootkit footprints with a practical memory analysis system. In: Presented as Part of the 21st USENIX Security Symposium (USENIX Security 2012), pp. 601–615. USENIX, Bellevue (2012) 9. : Virtuoso: narrowing the semantic gap in virtual machine introspection.

Where r−1 derandomizes strings, Kr is a randomized keyword and K is the corresponding keyword. , the characters that make up the keyword in the input were copied to the output). 2 String Randomization AutoRand randomizes (each SQL keyword in) each string constant in the application code. AutoRand randomizes each string constant by tokenizing it and then appending the randomization key to all the SQL keywords in the string. The string is left unmodified if no SQL keywords are found in it. The set of tokens that AutoRand regards as SQL keywords is easily configurable.

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