@conference {657,
	title = {A biocomputational platform for the automated construction of large-scale mathematical models of miRNA-transcription factor networks for studies on gene dosage compensation},
	booktitle = {2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI)},
	year = {2016},
	month = {Nov},
	keywords = {biocomputational platform, cancer, cancer cell, cancer complexity, cancer resistance, cell-to-cell heterogeneity, chromosomal alteration, Correlation, DNA, gene dosage compensation, gene expression, genetic material, genetics, large-scale mathematical model, Mathematical model, microRNA, miRNA-transcription factor networks, miRNAs, molecular biophysics, proteins, RNA, systems biology, transcription factors},
	doi = {10.1109/CONCAPAN.2016.7942348},
	author = {A. Man-Sai and S. C. Francisco and R. Mora-Rodriguez}
}
@conference {661,
	title = {Biocomputing platform module for cancer genomics and chemotherapy},
	booktitle = {2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI)},
	year = {2016},
	month = {Nov},
	keywords = {Analytical models, batched analysis, biocomputing, biocomputing platform module, Bioinformatics, cancer, cancer cell lines, cancer genomics, chemotherapeutic compounds, chemotherapeutic data, chemotherapy, data analysis, Data models, Data Processing, Data visualization, exploratory data analysis, gene expression, gene profile, genetics, Genomics, Learning Systems, medical computing, patient treatment, pattern clustering, Pattern Recognition, regression, regression analysis, unsupervised clustering models},
	doi = {10.1109/CONCAPAN.2016.7942342},
	author = {J. C. Coto and F. Siles and R. Mora-Rodriguez}
}
@conference {706,
	title = {Known/chosen key attacks against software instruction set randomization},
	booktitle = {Computer Security Applications Conference, 2006. ACSAC{\textquoteright}06. 22nd Annual},
	year = {2006},
	month = {12/2006},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Miami, FL, Estados Unidos},
	abstract = {<p>Instruction set randomization (ISR) has been proposed as a form of defense against binary code injection into an executing program. One proof-of-concept implementation is randomized instruction set emulator (RISE), based on the open-source Valgrind IA-32 to IA-32 binary translator. Although RISE is effective against attacks that are not RISE-aware, it is vulnerable to pure data and hybrid data-code attacks that target its data, as well to some classes of brute-force guessing. In order to enable the design of a production version, we describe implementation-specific and generic vulnerabilities that can be used to overcome RISE in its current form. We present and discuss attacks and solutions in three categories: known-key attacks that rely on the key being leaked and then used to pre-scramble the attacking code; chosen-key attacks that use implementation weaknesses to allow the attacker to define its own key, or otherwise affect key generation; and key-guessing ("brute-force") attacks, about which we explore the design of mini-malistic loaders which can be used to minimize the number of mask bytes required for a successful key-guessing attack. All the described attacks were tested in real-world scenarios</p>},
	keywords = {Binary codes, Computer aided instruction, Emulation, genetics, Hardware, Open source software, Production, Protection, Security, Testing},
	isbn = {0-7695-2716-7},
	doi = {10.1109/ACSAC.2006.33},
	url = {http://ieeexplore.ieee.org/document/4041180/},
	author = {Weiss, Yoav and Barrantes, Elena Gabriela}
}
