Assistant Professor
University of Lisboa, PT
ivmedeiros(at)fc.ul.pt
+351 217500087 (ext: 26380)
Research
Software
   
   

   
Ibéria Medeiros is an Assistant Professor in the Department of Informatics, at the Faculty of Sciences University of Lisboa. She is an integrated researcher of the LASIGE - Large-Scale Informatics Systems Laboratory, and a member of the Navigators research group. She is also a IEEE member.
She holds a PhD degree in Computer Science and a MSc degree in Informatics both at the Faculty of Sciences University of Lisboa, and a Licenciatura (roughly equivalent to BSc+MSc) in Mathematics and Informatics at the University of Azores. She is the author of software security tools for detecting vulnerabilities in source code of applications, and a parser for PHP language. She is coordinator of SEAL national project, has been participating in DiSIEM and SEGRID European projects, REDBOOK national projects, and involved in event organization.
Her research interests are concerned with software security, vulnerability detection, source code static analysis, runtime protection, machine learning, data mining, natural language processing, cyber threat intelligence, and security.
Within the XIVT project, a method and toolchain will be defined for testing highly configurable, variant-rich embedded systems in the automotive, rail, telecommunication and industrial production domains. This will enable a highly effective, cost-efficient quality assurance, allowing the shift to autonomous, flexible and adaptive applications. The method is founded on a knowledge-based analysis of requirements formulated in natural language, and a model-based test generation at product-line level. It is expected that XIVT methods will result in higher test coverage, more flexible processes of higher quality and better products.
The SEAL project aims to make significant advances in security of web applications, developing the SEAL platform containing tools that implement secure programming in applications written in server-side programming languages (e.g., PHP and .NET). The platform will be constituted by three layers, namely, code representation, vulnerability detection, and code correction, where: an intermediate language able to represent server-side languages and secure code features will be defined; on this language, tools to perform code analysis to detect and identify vulnerabilities will be developed, employing code analysis and machine learning techniques; and a secure code layer to remove the vulnerabilities found automatically will be created. The SEAL platform, during its development and evaluation, will resort to use cases defined with the Maxdata enterprise, the market leader in software solutions to health services.
For decades, numerous vulnerabilities have put computer systems and applications at risk. Several cybersecurity issues have been recurrent, being Buffer Overflows (BOs) vulnerabilities a primary attack method, which nowadays still accounts for more than 25% of the reported attacks. Such a high number clearly shows that classical software-based and compiler-assisted techniques for preventing exploitation of buffer overflow vulnerabilities did not succeed. Existing hardware-based methods (e.g., StackGhost) are too restricted and therefore they are not widely used. This project aims the design of an innovative hardware-based system monitoring architecture, introducing novel non-intrusive observation and runtime verification mechanisms for robust defence against cybersecurity hazards emerging either from accidental faults or from malicious attacks. Technical feasibility will be demonstrated for SPARC (aerospace applications) and ARM (telecommunications, including mobile) platforms.
The project aims to provide improvements to Security Information and Event Management (SIEM) systems based on diversity related technology. More specifically, the project wants to (1) enhance the quality of events collected using a diverse set of sensors and novel anomaly detectors, (2) add support for collecting infrastructure-related information from open source intelligence data available on diverse sources from the internet, (3) create new ways for visualising the information collected in the SIEM and provide high-level security metrics and models for improving security-related decision project, and (4) allow the use of multiple storage clouds for secure long-term archival of the raw events feed to the SIEM. Given the high costs of deployment of SIEM infrastructures, all these enhancements will be developed in a SIEM-independent way, as extensions to currently available systems, and will be validated through the deployed in three large-scale production environments.
The project main objective is to enhance the protection of smart electrical grids against cyber-attacks. SEGRID does this by applying a risk management analysis approach to a number of smart grid use cases (the SEGRID use cases), which will define security requirements and determine gaps in current security technologies, standards and regulations. The identified gaps and the analysis itself will give input to the enhancement of risk assessment methodologies and the development of novel security measures for smart grids.
The objective of RC-Clouds is to improve the security and dependability of cloud computing services using Byzantine fault tolerance or intrusion tolerance.
The main objective of MASSIF is to achieve a signicant advance in the area of Security Information and Event
Management (SIEM). On the base of proper multi-level event correlation, MASSIF will provide innovation techniques in order to enable
the detection of upcoming security threats and trigger remediation actions even before the occurrence of possible security incidences.
Thus, MASSIF will develop a new generation SIEM framework for service infrastructures supporting intelligent, scalable, and
multi-level/multi-domain security event processing and predictive security monitoring.
Such service-level SIEM involves the modelling and formal validation of security, including trusted computing concepts, architecture
for dependable and resilient collection of service events, supported by an extremely scalable and performant event collection and
processing framework, in the context of service-level attack models.
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Web Application Protection |
WAP detects and corrects the following vulnerabilities:
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SEPTIC - SElf-Protection daTabases preventIng attaCks |
SEPTIC is a mechanism put inside of the DBMS to protect in runtime any application that use the databases, detecting and blocking injection attacks, such as SQL injection and stored injection (e.g., stored XSS) attacks. It also solves the semantic mismatch between server-side language and DBMS, which is the difference of interpretation between how the queries are believed to be executed by the DBMS and how they are actually executed. This means that SEPTIC protects applications against the semantic mismatch exploitation attacks, i.e., attacks that circumventing with success some forms of protection, such as web application firewalls solutions and sanitization functions present in source code of applications.
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DEKANT - hidDEn marKov model diAgNosing vulnerabiliTies |
DEKANT is a source code static analysis tool inspired in natural language processing that learns to recognize vulnerabilities in web applications using a hidden Markov model (HMM). It uses a sequence model for learning to characterize vulnerabilities, and then uses a HMM to classify code elements of source code, taking into account the order of code elements inside the source code.
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PHP Parser
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PHParser 1.2 generates a pure Java parser for PHP programs. Invoking this parser yields an explicit parse tree (AST) and a tree walker suitable for further analysis.
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Detector of integEr vulnerabilitiEs in softwarE Portability |
DEEEP is a open source static analysis tool to detect, in C programs, integer vulnerabilities caused by the bad adaption of applications from ILP32 to LP64.