arXiv subjects

Fabio Massacci

Publications and source records attributed to Fabio Massacci.

At least 37 records · Page 2Linked to original sources

arXiv2023

A Graph-based Stratified Sampling Methodology for the Analysis of (Underground) Forums

[Context] Researchers analyze underground forums to study abuse and cybercrime activities. Due to the size of the forums and the domain expertise required to identify criminal discussions, most approaches employ supervised machine learning techniques to automatically classify the posts of interest. [Goal] Human annotation is costly. How to select samples to annotate that account for the structure of the forum? [Method] We present a methodology to generate stratified samples based on information about the centrality properties of the population and evaluate classifier performance. [Result] We observe that by employing a sample obtained from a uniform distribution of the post degree centrality metric, we maintain the same level of precision but significantly increase the recall (+30%) compared to a sample whose distribution is respecting the population stratification. We find that classifiers trained with similar samples disagree on the classification of criminal activities up to 33% of the time when deployed on the entire forum.

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arXiv2023

Are Software Updates Useless Against Advanced Persistent Threats?

A dilemma worth Shakespeare's Hamlet is increasingly haunting companies and security researchers: ``to update or not to update, this is the question``. From the perspective of recommended common practices by software vendors the answer is unambiguous: you should keep your software up-to-date. But is common sense always good sense? We argue it is not.

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arXiv2023

Building cross-language corpora for human understanding of privacy policies

Making sure that users understand privacy policies that impact them is a key challenge for a real GDPR deployment. Research studies are mostly carried in English, but in Europe and elsewhere, users speak a language that is not English. Replicating studies in different languages requires the availability of comparable cross-language privacy policies corpora. This work provides a methodology for building comparable cross-language in a national language and a reference study language. We provide an application example of our methodology comparing English and Italian extending the corpus of one of the first studies about users understanding of technical terms in privacy policies. We also investigate other open issues that can make replication harder.

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arXiv2022

The Data Protection Officer, an ubiquitous role nobody really knows

Among all cybersecurity and privacy workers, the Data Protection Officer (DPO) stands between those auditing a company's compliance and those acting as management advisors. A person that must be somehow versed in legal, management, and cybersecurity technical skills. We describe how this role tackles socio-technical risks in everyday scenarios.

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arXiv2022

On the acceptance by code reviewers of candidate security patches suggested by Automated Program Repair tools

Background: Testing and validation of the semantic correctness of patches provided by tools for Automated Program Repairs (APR) has received a lot of attention. Yet, the eventual acceptance or rejection of suggested patches for real world projects by humans patch reviewers has received a limited attention. Objective: To address this issue, we plan to investigate whether (possibly incorrect) security patches suggested by APR tools are recognized by human reviewers. We also want to investigate whether knowing that a patch was produced by an allegedly specialized tool does change the decision of human reviewers. Method: In the first phase, using a balanced design, we propose to human reviewers a combination of patches proposed by APR tools for different vulnerabilities and ask reviewers to adopt or reject the proposed patches. In the second phase, we tell participants that some of the proposed patches were generated by security specialized tools (even if the tool was actually a `normal' APR tool) and measure whether the human reviewers would change their decision to adopt or reject a patch. Limitations: The experiment will be conducted in an academic setting, and to maintain power, it will focus on a limited sample of popular APR tools and popular vulnerability types.

cs.SE↗

arXiv2022

Cryptographic and Financial Fairness

A recent trend in multi-party computation is to achieve cryptographic fairness via monetary penalties, i.e. each honest player either obtains the output or receives a compensation in the form of a cryptocurrency. We pioneer another type of fairness, financial fairness, that is closer to the real-world valuation of financial transactions. Intuitively, a penalty protocol is financially fair if the net present cost of participation (the total value of cash inflows less cash outflows, weighted by the relative discount rate) is the same for all honest participants, even when some parties cheat. We formally define the notion, show several impossibility results based on game theory, and analyze the practical effects of (lack of) financial fairness if one was to run the protocols for real on Bitcoin using Bloomberg's dark pool trading. For example, we show that the ladder protocol (CRYPTO'14), and its variants (CCS'15 and CCS'16), fail to achieve financial fairness both in theory and in practice, while the penalty protocols of Kumaresan and Bentov (CCS'14) and Baum, David and Dowsley (FC'20) are financially fair. This version contains formal definitions, detailed security proofs, demos and experimental data in the appendix.

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arXiv2022

Towards a Security Stress-Test for Cloud Configurations

Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on ``trial and error'' experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a knowledge, AND/OR, graphs approach to model cloud deployment security objects and vulnerabilities. In this way, we can capture relationships between configurations, permissions (e.g., CAP\_SYS\_ADMIN), and security profiles (e.g., AppArmor and SecComp), as first-class citizens. Such an approach allows us to suggest alternative and safer configurations, support administrators in the study of what-if scenarios, and scale the analysis to large scale deployments. We present an initial validation and illustrate the approach with three real vulnerabilities from known sources.

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arXiv2022

Software Updates Strategies: a Quantitative Evaluation against Advanced Persistent Threats

Software updates reduce the opportunity for exploitation. However, since updates can also introduce breaking changes, enterprises face the problem of balancing the need to secure software with updates with the need to support operations. We propose a methodology to quantitatively investigate the effectiveness of software updates strategies against attacks of Advanced Persistent Threats (APTs). We consider strategies where the vendor updates are the only limiting factors to cases in which enterprises delay updates from 1 to 7 months based on SANS data. Our manually curated dataset of APT attacks covers 86 APTs and 350 campaigns from 2008 to 2020. It includes information about attack vectors, exploited vulnerabilities (e.g. 0-days vs public vulnerabilities), and affected software and versions. Contrary to common belief, most APT campaigns employed publicly known vulnerabilities. If an enterprise could theoretically update as soon as an update is released, it would face lower odds of being compromised than those waiting one (4.9x) or three (9.1x) months. However, if attacked, it could still be compromised from 14% to 33% of the times. As in practice enterprises must do regression testing before applying an update, our major finding is that one could perform 12% of all possible updates restricting oneself only to versions fixing publicly known vulnerabilities without significant changes to the odds of being compromised compared to a company that updates for all versions.

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arXiv2021

Technical Leverage in a Software Ecosystem: Development Opportunities and Security Risks

In finance, leverage is the ratio between assets borrowed from others and one's own assets. A matching situation is present in software: by using free open-source software (FOSS) libraries a developer leverages on other people's code to multiply the offered functionalities with a much smaller own codebase. In finance as in software, leverage magnifies profits when returns from borrowing exceed costs of integration, but it may also magnify losses, in particular in the presence of security vulnerabilities. We aim to understand the level of technical leverage in the FOSS ecosystem and whether it can be a potential source of security vulnerabilities. Also, we introduce two metrics change distance and change direction to capture the amount and the evolution of the dependency on third-party libraries. The application of the proposed metrics on 8494 distinct library versions from the FOSS Maven-based Java libraries shows that small and medium libraries (less than 100KLoC) have disproportionately more leverage on FOSS dependencies in comparison to large libraries. We show that leverage pays off as leveraged libraries only add a 4% delay in the time interval between library releases while providing four times more code than their own. However, libraries with such leverage (i.e., 75% of libraries in our sample) also have 1.6 higher odds of being vulnerable in comparison to the libraries with lower leverage. We provide an online demo for computing the proposed metrics for real-world software libraries available under the following URL: https://techleverage.eu/.

cs.SE↗

arXiv2021

Secure Software Development in the Era of Fluid Multi-party Open Software and Services

Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates over months or years, must now cope with small, continuous changes taking place within a week, and happening in sub-components that are controlled by third-party developers one might not even know they existed. In this paper, we aim to provide an overview of the current software security approaches and evaluate their appropriateness in the face of the changed nature in software development. Software security assurance could benefit by switching from a process-based to an artefact-based approach. Further, security evaluation might need to be more incremental, automated and decentralized. We believe this can be achieved by supporting mechanisms for lightweight and scalable screenings that are applicable to the entire population of software components albeit there might be a price to pay.

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arXiv2019

A Convolutional Transformation Network for Malware Classification

Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.

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arXiv2018

Vulnerable Open Source Dependencies: Counting Those That Matter

BACKGROUND: Vulnerable dependencies are a known problem in today's open-source software ecosystems because OSS libraries are highly interconnected and developers do not always update their dependencies. AIMS: In this paper we aim to present a precise methodology, that combines the code-based analysis of patches with information on build, test, update dates, and group extracted from the very code repository, and therefore, caters to the needs of industrial practice for correct allocation of development and audit resources. METHOD: To understand the industrial impact of the proposed methodology, we considered the 200 most popular OSS Java libraries used by SAP in its own software. Our analysis included 10905 distinct GAVs (group, artifact, version) when considering all the library versions. RESULTS: We found that about 20% of the dependencies affected by a known vulnerability are not deployed, and therefore, they do not represent a danger to the analyzed library because they cannot be exploited in practice. Developers of the analyzed libraries are able to fix (and actually responsible for) 82% of the deployed vulnerable dependencies. The vast majority (81%) of vulnerable dependencies may be fixed by simply updating to a new version, while 1% of the vulnerable dependencies in our sample are halted, and therefore, potentially require a costly mitigation strategy. CONCLUSIONS: Our case study shows that the correct counting allows software development companies to receive actionable information about their library dependencies, and therefore, correctly allocate costly development and audit resources, which is spent inefficiently in case of distorted measurements.

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arXiv2018

The Effect of Security Education and Expertise on Security Assessments: the Case of Software Vulnerabilities

In spite of the growing importance of software security and the industry demand for more cyber security expertise in the workforce, the effect of security education and experience on the ability to assess complex software security problems has only been recently investigated. As proxy for the full range of software security skills, we considered the problem of assessing the severity of software vulnerabilities by means of a structured analysis methodology widely used in industry (i.e. the Common Vulnerability Scoring System (\CVSS) v3), and designed a study to compare how accurately individuals with background in information technology but different professional experience and education in cyber security are able to assess the severity of software vulnerabilities. Our results provide some structural insights into the complex relationship between education or experience of assessors and the quality of their assessments. In particular we find that individual characteristics matter more than professional experience or formal education; apparently it is the \emph{combination} of skills that one owns (including the actual knowledge of the system under study), rather than the specialization or the years of experience, to influence more the assessment quality. Similarly, we find that the overall advantage given by professional expertise significantly depends on the composition of the individual security skills as well as on the available information.

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arXiv2018

Attack Potential in Impact and Complexity

Vulnerability exploitation is reportedly one of the main attack vectors against computer systems. Yet, most vulnerabilities remain unexploited by attackers. It is therefore of central importance to identify vulnerabilities that carry a high `potential for attack'. In this paper we rely on Symantec data on real attacks detected in the wild to identify a trade-off in the Impact and Complexity of a vulnerability, in terms of attacks that it generates; exploiting this effect, we devise a readily computable estimator of the vulnerability's Attack Potential that reliably estimates the expected volume of attacks against the vulnerability. We evaluate our estimator performance against standard patching policies by measuring foiled attacks and demanded workload expressed as the number of vulnerabilities entailed to patch. We show that our estimator significantly improves over standard patching policies by ruling out low-risk vulnerabilities, while maintaining invariant levels of coverage against attacks in the wild. Our estimator can be used as a first aid for vulnerability prioritisation to focus assessment efforts on high-potential vulnerabilities.

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arXiv2017

TestREx: a Framework for Repeatable Exploits

Web applications are the target of many well known exploits and also a fertile ground for the discovery of security vulnerabilities. Yet, the success of an exploit depends both on the vulnerability in the application source code and the environment in which the application is deployed and run. As execution environments are complex (application servers, databases and other supporting applications), we need to have a reliable framework to test whether known exploits can be reproduced in different settings, better understand their effects, and facilitate the discovery of new vulnerabilities. In this paper, we present TestREx - a framework that allows for highly automated, easily repeatable exploit testing in a variety of contexts, so that a security tester may quickly and efficiently perform large-scale experiments with vulnerability exploits. It supports packing and running applications with their environments, injecting exploits, monitoring their success, and generating security reports. We also provide a corpus of example applications, taken from related works or implemented by us.

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arXiv2013

My Software has a Vulnerability, should I worry?

(U.S) Rule-based policies to mitigate software risk suggest to use the CVSS score to measure the individual vulnerability risk and act accordingly: an HIGH CVSS score according to the NVD (National (U.S.) Vulnerability Database) is therefore translated into a "Yes". A key issue is whether such rule is economically sensible, in particular if reported vulnerabilities have been actually exploited in the wild, and whether the risk score do actually match the risk of actual exploitation. We compare the NVD dataset with two additional datasets, the EDB for the white market of vulnerabilities (such as those present in Metasploit), and the EKITS for the exploits traded in the black market. We benchmark them against Symantec's threat explorer dataset (SYM) of actual exploit in the wild. We analyze the whole spectrum of CVSS submetrics and use these characteristics to perform a case-controlled analysis of CVSS scores (similar to those used to link lung cancer and smoking) to test its reliability as a risk factor for actual exploitation. We conclude that (a) fixing just because a high CVSS score in NVD only yields negligible risk reduction, (b) the additional existence of proof of concepts exploits (e.g. in EDB) may yield some additional but not large risk reduction, (c) fixing in response to presence in black markets yields the equivalent risk reduction of wearing safety belt in cars (you might also die but still..). On the negative side, our study shows that as industry we miss a metric with high specificity (ruling out vulns for which we shouldn't worry). In order to address the feedback from BlackHat 2013's audience, the final revision (V3) provides additional data in Appendix A detailing how the control variables in the study affect the results.

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arXiv2013

A Systematically Empirical Evaluation of Vulnerability Discovery Models: a Study on Browsers' Vulnerabilities

A precise vulnerability discovery model (VDM) will provide a useful insight to assess software security, and could be a good prediction instrument for both software vendors and users to understand security trends and plan ahead patching schedule accordingly. Thus far, several models have been proposed and validated. Yet, no systematically independent validation by somebody other than the author exists. Furthermore, there are a number of issues that might bias previous studies in the field. In this work, we fill in the gap by introducing an empirical methodology that systematically evaluates the performance of a VDM in two aspects: quality and predictability. We further apply this methodology to assess existing VDMs. The results show that some models should be rejected outright, while some others might be adequate to capture the discovery process of vulnerabilities. We also consider different usage scenarios of VDMs and find that the simplest linear model is the most appropriate choice in terms of both quality and predictability when browsers are young. Otherwise, logistics-based models are better choices.

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arXiv2013

MAP-REDUCE Runtime Enforcement of Information Flow Policies

We propose a flexible framework that can be easily customized to enforce a large variety of information flow properties. Our framework combines the ideas of secure multi-execution and map-reduce computations. The information flow property of choice can be obtained by simply changes to a map (or reduce) program that control parallel executions. We present the architecture of the enforcement mechanism and its customizations for non-interference (NI) (from Devriese and Piessens) and some properties proposed by Mantel, such as removal of inputs (RI) and deletion of inputs (DI), and demonstrate formally soundness and precision of enforcement for these properties.

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