Binary-level Function Profiling for Intrusion Detection and Smart Error Virtualization

Most current approaches to self-healing software (SHS) suffer from semantic incorrectness of the response mechanism. To support SHS, we propose Smart Error Virtualization (SEV), which treats functions as transactions but provides a way to guide the program state and remediation to be a more correct value than previous work. We perform runtime binary-level profiling on unmodified applications to learn both good return values and error return values (produced when the program encounters ``bad'' input). The goal is to ``learn from mistakes'' by converting malicious input to the program's notion of ``bad'' input. We introduce two implementations of this system that support three major uses: function profiling for regression testing, function profiling for host-based anomaly detection (environment-specialized fault detection), and function profiling for automatic attack remediation via SEV. Our systems do not require access to the source code of the application to enact a fix. Finally, this paper is, in part, a critical examination of error virtualization in order to shed light on how to approach semantic correctness

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

Fetch unit design for scalable simultaneous multithreading (ScSMT)

Continuous IC process enhancements make possible to integrate on a single chip the re-sources required for simultaneously executing multiple control flows or threads, exploiting different levels of thread-level parallelism: application-, function-, and loop-level. Scalable simultaneous multi-threading combines static and dynamic mechanisms to assemble a complexity-effective design that provides high instruction per cycle rates without sacrificing cycle time nor single-thread performance. This paper addresses the design of the fetch unit for a high-performance, scalable, simultaneous multithreaded processor. We present the detailed microarchitecture of a clustered and reconfigurable fetch unit based on an existing single-thread fetch unit. In order to minimize the occurrence of fetch hazards, the fetch unit dynamically adapts to the available thread-level parallelism and to the fetch characteristics of the active threads, working as a single shared unit or as two separate clusters. It combines static and dynamic methods in a complexity-efficient way. The design is supported by a simulation- based analysis of different instruction cache and branch target buffer configurations on the context of a multithreaded execution workload. Average reductions on the miss rates between 30% and 60% and peak reductions greater than 200% are obtained.Facultad de Informátic

Automatic Removal of Flaws in Embedded System Software

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2022Currently, embedded systems are present in a myriad of devices, such as Internet of Things, drones, and Cyber-physical Systems. The security of these devices can be critical, depending on the context they are integrated and the role they play (e.g., water plant, car). C is the core language used to develop the software for these devices and is known for missing the bounds of its data types, which leads to vulnerabilities such as buffer overflows. These vulnerabilities, when exploited, cause severe damage and can put human life in danger. Therefore, the software of these devices must be secure. One of the concerns with vulnerable C programs is to correct the code automatically, employing secure code that can remove the existing vulnerabilities and avoid attacks. However, such task faces some challenges after finding the vulnerabilities, namely determining what code is needed to remove them and where to insert that code, maintaining the correct behavior of the application after applying the code correction, and verifying that the generated code correction is secure and effectively removes the vulnerabilities. Another challenge is to accomplish all these elements automatically. This work aims to study diverse types of buffer overflow vulnerabilities in the C programming lan guage, forms to build secure code for invalidating such vulnerabilities, including functions from the C language that can be used to remove flaws. Based on this knowledge, we propose an approach that automatically, after discovering and confirming potential vulnerabilities of an application, applies code correction to fix the vulnerable code of those vulnerabilities verified and validate the new code with fuzzing/attack injection. We implemented our approach and evaluated it with a set of test cases and with real applications. The experimental results showed that the tool detected the intended vulnerabilities and generated corrections capable of removing the vulnerabilities found