Browsing by Author "Santos J.C.M."

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Early prediction of severe maternal morbidity using machine learning techniques
(Springer Verlag, 2016) Rodríguez E.A.; Estrada F.E.; Torres W.C.; Santos J.C.M.; Escalante H.J.; Montes-y-Gomez M.; Segura A.; de Dios Murillo J.
Severe Maternal Morbidity is a public health issue. It may occur during pregnancy, delivery, or puerperium due to conditions (hypertensive disorders, hemorrhages, infections and others) that put in risk the women’s or baby’s life. These conditions are really difficult to detect at an early stage. In response to the above, this work proposes using several machine learning techniques, which are considered most relevant in a bio-medical setting, in order to predict the risk level for Severe Maternal Morbidity in patients during pregnancy. The population studied correspond to pregnant women receiving prenatal care and final attention at E.S.E Clínica de Maternidad Rafael Calvo in Cartagena, Colombia. This paper presents the preliminary results of an ongoing project, as well as methods and materials considered for the construction of the learning models. © Springer International Publishing AG 2016.
Leveraging speculative architectures for runtime program validation
(2013) Santos J.C.M.; Fei Y.
Program execution can be tampered with by malicious attackers through exploiting software vulnerabilities. Changing the program behavior by compromising control data and decision data has become the most serious threat in computer system security. Although several hardware approaches have been presented to validate program execution, they either incur great hardware overhead or introduce false alarms. We propose a new hardware-based approach by leveraging the existing speculative architectures for runtime program validation. The on-chip branch target buffer (BTB) is utilized as a cache of the legitimate control flow transfers stored in a secure memory region. In addition, the BTB is extended to store the correct program path information. At each indirect branch site, the BTB is used to validate the decision history of previous conditional branches and monitor the following execution path at runtime. Implementation of this approach is transparent to the upper operating system and programs. Thus, it is applicable to legacy code. Because of good code locality of the executable programs and effectiveness of branch prediction, the frequency of control-flow validations against the secure off-chip memory is low. Our experimental results show a negligible performance penalty and small storage overhead. © 2013 ACM.
Static secure page allocation for light-weight dynamic information flow tracking
(2012) Santos J.C.M.; Fei Y.; Shi Z.J.
Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users. Copyright 2012 ACM.