arXiv subjects

Domenico Cotroneo

Publications and source records attributed to Domenico Cotroneo.

At least 37 records · Page 2Linked to original sources

arXiv2023

IRIS: a Record and Replay Framework to Enable Hardware-assisted Virtualization Fuzzing

Nowadays, industries are looking into virtualization as an effective means to build safe applications, thanks to the isolation it can provide among virtual machines (VMs) running on the same hardware. In this context, a fundamental issue is understanding to what extent the isolation is guaranteed, despite possible (or induced) problems in the virtualization mechanisms. Uncovering such isolation issues is still an open challenge, especially for hardware-assisted virtualization, since the search space should include all the possible VM states (and the linked hypervisor state), which is prohibitive. In this paper, we propose IRIS, a framework to record (learn) sequences of inputs (i.e., VM seeds) from the real guest execution (e.g., OS boot), replay them as-is to reach valid and complex VM states, and finally use them as valid seed to be mutated for enabling fuzzing solutions for hardware-assisted hypervisors. We demonstrate the accuracy and efficiency of IRIS in automatically reproducing valid VM behaviors, with no need to execute guest workloads. We also provide a proof-of-concept fuzzer, based on the proposed architecture, showing its potential on the Xen hypervisor.

cs.CR↗

arXiv2023

Run-time Failure Detection via Non-intrusive Event Analysis in a Large-Scale Cloud Computing Platform

Cloud computing systems fail in complex and unforeseen ways due to unexpected combinations of events and interactions among hardware and software components. These failures are especially problematic when they are silent, i.e., not accompanied by any explicit failure notification, hindering the timely detection and recovery. In this work, we propose an approach to run-time failure detection tailored for monitoring multi-tenant and concurrent cloud computing systems. The approach uses a non-intrusive form of event tracing, without manual changes to the system's internals to propagate session identifiers (IDs), and builds a set of lightweight monitoring rules from fault-free executions. We evaluated the effectiveness of the approach in detecting failures in the context of the OpenStack cloud computing platform, a complex and "off-the-shelf" distributed system, by executing a campaign of fault injection experiments in a multi-tenant scenario. Our experiments show that the approach detects the failure with an F1 score (0.85) and accuracy (0.77) higher than the ones provided by the OpenStack failure logging mechanisms (0.53 and 0.50) and two non--session-aware run-time verification approaches (both lower than 0.15). Moreover, the approach significantly decreases the average time to detect failures at run-time (~114 seconds) compared to the OpenStack logging mechanisms.

cs.SE↗

arXiv2022

Towards Assessing Isolation Properties in Partitioning Hypervisors

Partitioning hypervisor solutions are becoming increasingly popular, to ensure stringent security and safety requirements related to isolation between co-hosted applications and to make more efficient use of available hardware resources. However, assessment and certification of isolation requirements remain a challenge and it is not trivial to understand what and how to test to validate these properties. Although the high-level requirements to be verified are mentioned in the different security- and safety-related standards, there is a lack of precise guidelines for the evaluator. This guidance should be comprehensive, generalizable to different products that implement partitioning, and tied specifically to lower-level requirements. The goal of this work is to provide a systematic framework that addresses this need.

cs.CR↗

arXiv2022

On Temporal Isolation Assessment in Virtualized Railway Signaling as a Service Systems

Railway signaling systems provide numerous critical functions at different safety level, to correctly implement the entire transport ecosystem. Today, we are witnessing the increasing use of the cloud and virtualization technologies in such mixed-criticality systems, with the main goal of reducing costs, improving reliability, while providing orchestration capabilities. Unfortunately, virtualization includes several issues for assessing temporal isolation, which is critical for safety-related standards like EN50128. In this short paper, we envision leveraging the real-time flavor of a general-purpose hypervisor, like Xen, to build the Railway Signaling as a Service (RSaaS) systems of the future. We provide a preliminary background, highlighting the need for a systematic evaluation of the temporal isolation to demonstrate the feasibility of using general-purpose hypervisors in the safety-critical context for certification purposes.

cs.DC↗

arXiv2022

Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study

Proactive approaches to security, such as adversary emulation, leverage information about threat actors and their techniques (Cyber Threat Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e., natural language), such as incident reports and leaked documents. To support proactive security efforts, we present an experimental study on the automatic classification of unstructured CTI into attack techniques using machine learning (ML). We contribute with two new datasets for CTI analysis, and we evaluate several ML models, including both traditional and deep learning-based ones. We present several lessons learned about how ML can perform at this task, which classifiers perform best and under which conditions, which are the main causes of classification errors, and the challenges ahead for CTI analysis.

cs.CR↗

arXiv2022

Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation

Neural Machine Translation (NMT) has reached a level of maturity to be recognized as the premier method for the translation between different languages and aroused interest in different research areas, including software engineering. A key step to validate the robustness of the NMT models consists in evaluating the performance of the models on adversarial inputs, i.e., inputs obtained from the original ones by adding small amounts of perturbation. However, when dealing with the specific task of the code generation (i.e., the generation of code starting from a description in natural language), it has not yet been defined an approach to validate the robustness of the NMT models. In this work, we address the problem by identifying a set of perturbations and metrics tailored for the robustness assessment of such models. We present a preliminary experimental evaluation, showing what type of perturbations affect the model the most and deriving useful insights for future directions.

cs.CL↗

arXiv2022

Shellcode_IA32: A Dataset for Automatic Shellcode Generation

We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode_IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.

cs.SE↗

arXiv2022

Can We Generate Shellcodes via Natural Language? An Empirical Study

Writing software exploits is an important practice for offensive security analysts to investigate and prevent attacks. In particular, shellcodes are especially time-consuming and a technical challenge, as they are written in assembly language. In this work, we address the task of automatically generating shellcodes, starting purely from descriptions in natural language, by proposing an approach based on Neural Machine Translation (NMT). We then present an empirical study using a novel dataset (Shellcode_IA32), which consists of 3,200 assembly code snippets of real Linux/x86 shellcodes from public databases, annotated using natural language. Moreover, we propose novel metrics to evaluate the accuracy of NMT at generating shellcodes. The empirical analysis shows that NMT can generate assembly code snippets from the natural language with high accuracy and that in many cases can generate entire shellcodes with no errors.

cs.SE↗

arXiv2022

ThorFI: A Novel Approach for Network Fault Injection as a Service

In this work, we present a novel fault injection solution (ThorFI) for virtual networks in cloud computing infrastructures. ThorFI is designed to provide non-intrusive fault injection capabilities for a cloud tenant, and to isolate injections from interfering with other tenants on the infrastructure. We present the solution in the context of the OpenStack cloud management platform, and release this implementation as open-source software. Finally, we present two relevant case studies of ThorFI, respectively in an NFV IMS and of a high-availability cloud application. The case studies show that ThorFI can enhance functional tests with fault injection, as in 4%-34% of the test cases the IMS is unable to handle faults; and that despite redundancy in virtual networks, faults in one virtual network segment can propagate to other segments, and can affect the throughput and response time of the cloud application as a whole, by about 3 times in the worst case.

cs.SE↗

arXiv2021

Virtualizing Mixed-Criticality Systems: A Survey on Industrial Trends and Issues

Virtualization is gaining attraction in the industry as it promises a flexible way to integrate, manage, and re-use heterogeneous software components with mixed-criticality levels, on a shared hardware platform, while obtaining isolation guarantees. This work surveys the state-of-the-practice of real-time virtualization technologies by discussing common issues in the industry. In particular, we analyze how different virtualization approaches and solutions can impact isolation guarantees and testing/certification activities, and how they deal with dependability challenges. The aim is to highlight current industry trends and support industrial practitioners to choose the most suitable solution according to their application domains.

cs.SE↗

arXiv2021

Software Micro-Rejuvenation for Android Mobile Systems

Software aging -- the phenomenon affecting many long-running systems, causing performance degradation or an increasing failure rate over mission time, and eventually leading to failure - is known to affect mobile devices and their operating systems, too. Software rejuvenation -- the technique typically used to counteract aging -- may compromise the user's perception of availability and reliability of the personal device, if applied at a coarse grain, e.g., by restarting applications or, worse, rebooting the entire device. This article proposes a configurable micro-rejuvenation technique to counteract software aging in Android-based mobile devices, acting at a fine-grained level, namely on in-memory system data structures. The technique is engineered in two phases. Before releasing the (customized) Android version, a heap profiling facility is used by the manufacturer's developers to identify potentially bloating data structures in Android services and to instrument their code. After release, an aging detection and rejuvenation service will safely clean up the bloating data structures, with a negligible impact on user perception and device availability, as neither the device nor operating system's processes are restarted. The results of experiments show the ability of the technique to provide significant gains in aging mobile operating system responsiveness and time to failure.

cs.SE↗

arXiv2021

EVIL: Exploiting Software via Natural Language

Writing exploits for security assessment is a challenging task. The writer needs to master programming and obfuscation techniques to develop a successful exploit. To make the task easier, we propose an approach (EVIL) to automatically generate exploits in assembly/Python language from descriptions in natural language. The approach leverages Neural Machine Translation (NMT) techniques and a dataset that we developed for this work. We present an extensive experimental study to evaluate the feasibility of EVIL, using both automatic and manual analysis, and both at generating individual statements and entire exploits. The generated code achieved high accuracy in terms of syntactic and semantic correctness.

cs.SE↗

arXiv2021

Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning

Identifying the failure modes of cloud computing systems is a difficult and time-consuming task, due to the growing complexity of such systems, and the large volume and noisiness of failure data. This paper presents a novel approach for analyzing failure data from cloud systems, in order to relieve human analysts from manually fine-tuning the data for feature engineering. The approach leverages Deep Embedded Clustering (DEC), a family of unsupervised clustering algorithms based on deep learning, which uses an autoencoder to optimize data dimensionality and inter-cluster variance. We applied the approach in the context of the OpenStack cloud computing platform, both on the raw failure data and in combination with an anomaly detection pre-processing algorithm. The results show that the performance of the proposed approach, in terms of purity of clusters, is comparable to, or in some cases even better than manually fine-tuned clustering, thus avoiding the need for deep domain knowledge and reducing the effort to perform the analysis. In all cases, the proposed approach provides better performance than unsupervised clustering when no feature engineering is applied to the data. Moreover, the distribution of failure modes from the proposed approach is closer to the actual frequency of the failure modes.

cs.AI↗

arXiv2021

Timing Covert Channel Analysis of the VxWorks MILS Embedded Hypervisor under the Common Criteria Security Certification

Virtualization technology is nowadays adopted in security-critical embedded systems to achieve higher performance and more design flexibility. However, it also comes with new security threats, where attackers leverage timing covert channels to exfiltrate sensitive information from a partition using a trojan. This paper presents a novel approach for the experimental assessment of timing covert channels in embedded hypervisors, with a case study on security assessment of a commercial hypervisor product (Wind River VxWorks MILS), in cooperation with a licensed laboratory for the Common Criteria security certification. Our experimental analysis shows that it is indeed possible to establish a timing covert channel, and that the approach is useful for system designers for assessing that their configuration is robust against this kind of information leakage.

cs.CR↗

arXiv2020

Towards Runtime Verification via Event Stream Processing in Cloud Computing Infrastructures

Software bugs in cloud management systems often cause erratic behavior, hindering detection, and recovery of failures. As a consequence, the failures are not timely detected and notified, and can silently propagate through the system. To face these issues, we propose a lightweight approach to runtime verification, for monitoring and failure detection of cloud computing systems. We performed a preliminary evaluation of the proposed approach in the OpenStack cloud management platform, an "off-the-shelf" distributed system, showing that the approach can be applied with high failure detection coverage.

cs.SE↗

arXiv2020

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data is inefficient and error-prone, as the analyst can miss severe failure modes that are yet unknown. This paper introduces a new paradigm (fault injection analytics) that applies unsupervised machine learning on execution traces of the injected system, to ease the discovery and interpretation of failure modes. We evaluated the proposed approach in the context of fault injection experiments on the OpenStack cloud computing platform, where we show that the approach can accurately identify failure modes with a low computational cost.

cs.SE↗

arXiv2020

Dependability Evaluation of Middleware Technology for Large-scale Distributed Caching

Distributed caching systems (e.g., Memcached) are widely used by service providers to satisfy accesses by millions of concurrent clients. Given their large-scale, modern distributed systems rely on a middleware layer to manage caching nodes, to make applications easier to develop, and to apply load balancing and replication strategies. In this work, we performed a dependability evaluation of three popular middleware platforms, namely Twemproxy by Twitter, Mcrouter by Facebook, and Dynomite by Netflix, to assess availability and performance under faults, including failures of Memcached nodes and congestion due to unbalanced workloads and network link bandwidth bottlenecks. We point out the different availability and performance trade-offs achieved by the three platforms, and scenarios in which few faulty components cause cascading failures of the whole distributed system.

cs.SE↗

arXiv2020

A Comprehensive Study on Software Aging across Android Versions and Vendors

This paper analyzes the phenomenon of software aging - namely, the gradual performance degradation and resource exhaustion in the long run - in the Android OS. The study intends to highlight if, and to what extent, devices from different vendors, under various usage conditions and configurations, are affected by software aging and which parts of the system are the main contributors. The results demonstrate that software aging systematically determines a gradual loss of responsiveness perceived by the user, and an unjustified depletion of physical memory. The analysis reveals differences in the aging trends due to the workload factors and to the type of running applications, as well as differences due to vendors' customization. Moreover, we analyze several system-level metrics to trace back the software aging effects to their main causes. We show that bloated Java containers are a significant contributor to software aging, and that it is feasible to mitigate aging through a micro-rejuvenation solution at the container level.

cs.SE↗